historical-vegetation-height-model-nfi

title_for_slug: historical-vegetation-height-model-nfi

en: dsm nfi historical forest photogrammetry vhm

en: 1990-1998 Vegetation Height Model

en: 1998-2006 Vegetation Height Model

en: Description of available data

en: 1979-1985 Vegetation Height Model

en: 1985-1991 Vegetation Height Model

en:
The main datasets available are 4 nationwide **Digital Surface Models (DSMs)** and corresponding **Vegetation Height Models (VHMs)** with a spatial resolution of 1 m.
The Vegetation Height Models are calculated by subtraction of a Digital Terrain Model (swissAlti3d version 2017, swisstopo) from the generated DSMs. DSMs were derived by image matching of **scanned historical images** (panchromatic 8-bit) acquired with a RC10 frame camera, in the context of the swisswide image acquisition campaigns of swisstopo.
Nationwide products could be generated for the **four epochs**
 1979-1985 / 1985-1991 / 1990-1998 / 1998-2006.
Additional information about successful image matching vs. interpolation of pixels and about the year of image acquisition are available in the corresponding files (**matched_area_xxxx_xxxx_binay.tif** and **meta_acquisition_year_xxxx_xxxx.json**, respectively).
To separate vegetation from non-vegetation areas a mask derived from a combination of information of the Topographic landscape model - TLM (swisstopo) and a NDVI layer was used and is supplied as **"TLM_mask.tif"**
Due to the occurrence of unrealistic vegetation height values caused by the failure of the image matching process (mainly overexposed images and clouds or very steep rocky terrain), pixel values > 60 m were set to NoData and < 0 m to 0.
All datasets are available in the Reference System **LV95 (EPSG: 2056) LN02 (EPSG: 5728)** and share the common **extent definition** (xmin ymin xmax ymax): 2480000 1070000 2840000 1300000

dsm

display_name: dsm

forest

display_name: forest

historical

display_name: historical

nfi

display_name: nfi

photogrammetry

display_name: photogrammetry

vhm

display_name: vhm

educ

en: Education, culture and sport

en: Historical Vegetation Height Model NFI


maintainer: Felix Gugerli

en: melanargia-galathea genetic-structure monitoring conservation-genetics genetic-diversity

state: active

type: dataset

media_type: text/csv

format: CSV

en:
Individual genotypes assessed at six nuclear microsatellite loci (two alleles per locus) for individuals of the butterfly Melanargia galathea (Marbled white) that were collected throughout Switzerland, along the regular grid of the Biodiversity Monitoring (BDM) of Switzerland. For each individual, the sampling site (number) and the genotype are given. Note that due to privacy restrictions, the original geographic coordinates remain disclosed. For original coordinates, data holders should be contacted (Swiss Federal Office for the Environment; Biodiversity Monitoring Switzerland).
The data refer to the following publication: Terzer et al., Distinct spatial patterns of genetic structure and diversity in the butterfly Marbled White (Melanargia galathea) inhabiting fragmented grasslands. Conservation Genetics, https://doi.org/0.1007/s10592-023-01593-4.

conservation-genetics

display_name: conservation-genetics

genetic-diversity

display_name: genetic-diversity

genetic-structure

display_name: genetic-structure

melanargia-galathea

display_name: melanargia-galathea

monitoring

display_name: monitoring

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Nuclear microsatellite genotypes of the butterfly Melanargia galathea


maintainer: Merin Reji Chacko

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

en: trophiCH

media_type: application/zip

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

format: ZIP

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

**Please note that this dataset is under embargo until publication of the paper.**

food-webs

display_name: food-webs

invertebarates

display_name: invertebarates

metaweb

display_name: metaweb

switzerland

display_name: switzerland

trophic-networks

display_name: trophic-networks

vascular-plants

display_name: vascular-plants

vertebrates

display_name: vertebrates

en: trophiCH v1 - a food web for Switzerland


photogrammetric-drone-data-gruenbodeli

maintainer: EnviDat Support

title_for_slug: photogrammetric-drone-data-gruenbodeli

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

en: Data Access

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

All products are also available as a cloud optimized geotiff.

format: SERVICE

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

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

snow-depth-mapping

display_name: snow-depth-mapping

uav

display_name: uav

wingtra

display_name: wingtra

en: Photogrammetric Drone Data Grüenbödeli


maintainer: Anita Risch

en: ecosystem-coupling subalpine exclusion grazing swiss-national-park ecosystem-functions grassland ecosystem-multifunctionality invertebrates vertebrates

en: Datafile

en:
ID: Plot ID composited of Fence & Treatment number (90 plots total)

Grassland: Name of the six subalpine grasslands where the exclosure setups were erected. In large grasslands (GRIM, MING, STAB), we established four hierarchical exclosure setups (two in short- and two in tall-grass vegetation), whereas in the small grasslands (BOTS, DADA, MARG) only one setup was erected per vegetation type. BOTS = Val dal Botsch, GRIM = Grimmels, MING = Val Mingér, STAB = Alp Stabelchod, DADA = Stabelchod da daint, MARG = Margunet

Fence: Number of exclosure setup (1 - 18). Note that uneven numbers (1,3,5,7,9,11,13,15,17) represent setups established on short-grass vegetation, even numbers (2,4,6,8,10,12,14,16,18) setups established in tall-grass vegetation
Vegetation	Vegetation type, 1 = short-grass vegetation, 2 = tall-grass vegetation; (9 replicate fences on each vegetation type)
Treatment	Progressive animal exclusion, numbers indicate which animals had access to the plot: 1 = Large/medium/small mammals, and invertebrates have access ("L/M/S/I"), 2 = Medium/small mammals, and invertebrates have access (“M/S/I”), 3 = Small mammals and invertebrates have access (“S/I”), 4 = Invertebrates have access (“I”), 5 = No animals have access (“None”)

Bulkden:	Fine fraction soil bulk density (g cm-3)

Soil.moist	: Soil moisture (%)

Soil.org.C: Rhizosphere soil C content (%)

pH: Soil pH

Nema.PC1: First principal component (PC1) of principal component analysis (PCA) of the soil nematode community composition

Nema.PC2: Second principal component (PC2) of principal component analysis (PCA) of the soil nematode community composition

Micro.PC1: 	First principal component (PC1) of principal component analysis (PCA) of the soil microbial community composition

Micro.PC2: Second principal component (PC2) of principal component analysis (PCA) of the soil microbial community composition

Abv.Inv.PC1: First principal component (PC1) of principal component analysis (PCA) of the aboveground-dwelling invertebrate community composition

Abv.Inv.PC2: Second principal component (PC2) of principal component analysis (PCA) of the aboveground-dwelling invertebrate community composition

Arthr.PC1: First principal component (PC1) of principal component analysis (PCA) of the soil arthropod community composition

Arthr.PC2: Second principal component (PC2) of principal component analysis (PCA) of the soil arthropod community composition

Plant.PC1: First principal component (PC1) of principal component analysis (PCA) of the plant community composition

Plant.PC2: Second principal component (PC2) of principal component analysis (PCA) of the plant community composition

Root.biomass: Root biomass (g m-2)

Micro.biomass	: Microbial biomass carbon (mg kg-1)

Plant.richness: Plant species richness

Plant.N: Plant tissue nitrogen content (%)

Soil.N.min: Soil net nitrogen mineralisation (kg ha-1 season)

Soil.resp: Soil carbon dioxide emissions (soil respiration; g m-2 h-1)

format: XLS

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

Paper Citation:
 > Risch AC, Ochoa-Hueso R, van der Putten WH, Bump JK, Busse MD, Frey B, Gwiazdowicz DJ, Page-Dumroese DS, Vandegehuchte ML, Zimmermann S, Schütz M. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. 2018. Nature Communications 9: 3684. [doi: 10.1038/s41467-018-06105-4](https://doi.org/10.1038/s41467-018-06105-4).

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

#Methods
Study sites
The experimental exclosure setups were installed within the SNP (IUCN category Ia preserve; Dudley 2008), in south-eastern Switzerland. The park covers 172 km2 of forests and subalpine and alpine grasslands along with scattered rock outcrops and scree slopes. The entire area has been protected from human impact (no hunting, fishing, camping or off-trail hiking) since 1914. Large, fairly homogenous patches of short- and tall-grass vegetation, which originate from different historical management and grazing regimes, cover the park’s subalpine grasslands entirely. Short-grass vegetation developed in areas where cattle used to rest (nutrient input) prior to the park’s foundation (14th century to 1914) (Schütz and others 2003, 2006) and is dominated by lawn grass species such as Festuca rubra L., Briza media L. and Agrostis capillaris L. (Schütz and others 2003, 2006). Today, this vegetation type is intensively grazed by diverse vertebrate and invertebrate communities that inhabit the park and consume up to 60% of the available biomass (Risch and others 2013). Tall-grass vegetation developed where cattle formerly grazed, but did not rest, and is dominated by rather nutrient-poor tussocks of Carex sempervirens Vill. and Nardus stricta L. (Schütz and others 2003, 2006). This vegetation type receives considerably less grazing, with only roughly 20% of the biomass consumed (Risch and others 2013). Consequently, the two vegetation types together represent a long-term trajectory of changes in grazing regimes. Underlying bedrock of all grasslands is dolomite, which renders these grasslands rather poor in nutrients regardless of former and current land-use regimes.
Experimental design
To progressively exclude aboveground vertebrate and invertebrate animals, we established 18 size-selective exclosure setups (nine in short-grass, nine in tall-grass vegetation) distributed over six subalpine grasslands across the SNP (Risch and others 2013, 2015). Elevation differences of exclosure locations did not exceed 350 m (between 1975 and 2300 m a.s.l.). The exclosures were established immediately after snowmelt in spring 2009 and were left in place for five consecutive growing seasons (until end of 2013). They were, however, temporarily dismantled every fall (late October after first snowfall) to protect them from avalanches. They were re-established in the same location every spring immediately after snowmelt. Each size-selective exclosure setup consisted of five plots (2 x 3 m) that progressively excluded aboveground vertebrates and invertebrates from large to small. The plots are labelled according to the guilds that had access to them “L/M/S/I”, “M/S/I”, “S/I”, “I”, “None”; L = large mammals, M = medium mammals, S = small mammals, I = invertebrates, None = no animals had access. As we only had permission to have the experimental setup in place for five consecutive growing seasons, the experiment had to be completely dismantled in the late fall of 2013 and all material removed from the SNP.
Our exclosure design was aimed at excluding mammalian herbivores, but naturally also excluded the few medium and small mammalian predators, as well as the entire aboveground invertebrate food web. A total of 26 large to small mammal species can be found in the SNP, but large apex predators are missing (wolf, bear, lynx) . Reptiles, amphibians and birds are scarce to absent in the subalpine grasslands under study. Only two reptile species occur in the park and they are confined to rocky areas that warm up enough for them to survive. One frog species spawns in an isolated pond far from our grasslands. Only three bird species occasionally feed on the subalpine grasslands. Using game cameras (Moultrie 6MP Game Spy I-60 Infrared Digital Game Camera, Moultrie Feeders, Alabaster, AL, USA), we did observe that the medium- and small-sized mammals (marmot/hares and mice) were not afraid to enter the fences and feed on their designated plots. We never spotted reptiles, amphibians or birds on camera. We distinguished between 59 higher aboveground-dwelling invertebrate taxa that our size-selective exclosures excluded (see also methods for aboveground-dwelling invertebrates below).
The “L/M/S/I” plot (not fenced) was located at least 5 m from the 2.1 m tall and 7 x 9 m large main electrical fence that enclosed the other four plots. The bottom wire of this fence was mounted at 0.5 m height and was not electrified to enable safe access for medium and small mammals, while fencing out the large ones. Within each main fence, we randomly established four 2 x 3 m plots separated by 1-m wide walkways from one another and from the main fence line: 1) the “M/S/I” plots were unfenced, allowing access to all but the large mammals; 2) the “S/I” plots (10 x 10 cm electrical mesh fence) excluded all medium-sized mammals. Note that the bottom 10 cm of this fence remained non-electrified to enable safe access for small mammals; 3) the “I” plots (2 x 2 cm metal mesh fence) excluded all mammals. We double-folded the mesh at the bottom 50 cm to reduce the mesh size to smaller than 1 x 1 cm openings; and 4) the “None” plots were surrounded by a 1 m tall mosquito net (1.5 x 2 mm) to exclude all animals. The top of the plot was covered with a mosquito-meshed wooden frame mounted to the corner posts (roof). We treated these plots a few times with biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) to remove insects that might have entered during data collection or that hatched from the soil, but amounts were negligible and did not impact soil moisture conditions within these plots.
To assess whether the design of the “None” exclosure (mesh and roof) affected the response variables within the plots and, therefore, influenced the results, we established an additional six “micro-climate control” exclosures (one in each of the six grasslands) (Risch and others 2013, 2015). These exclosures were built as the “None” exclosures but were open at the bottom (20 cm) of the 3-m side of the fence facing away from the prevailing wind direction to allow invertebrates to enter. A 20-cm high and 3-m long strip of metal mesh was used to block access to small mammals. Thus, this construction allowed a comparable micro-climate to the “None” plots, but also a comparable feeding pressure by invertebrates to the “I” plots. We compared various properties within these exclosures against one another to assess if our construction altered the conditions in the “None” plots. We showed that differences in plant (e.g., vegetation height, aboveground biomass) and soil properties (e.g., soil temperature, moisture) found between the “I” and the “None” treatments were not due to the construction of the “None” exclosure, but a function of animal exclusions, although the amount of UV light reaching the plant canopy was significantly reduced (Risch and others 2013).
Aboveground invertebrate sampling
Aboveground invertebrates were sampled with two different methods to capture both ground- and plant-dwelling organisms: 1) we randomly placed two pitfall traps (67 mm in diameter, covered with a roof) filled with 20% propylene glycol in one 1 x 1 m subplot of the 2 x 3 m treatment plots in spring 2013 (May) and emptied them every two weeks until late September 2013 (Vandegehuchte and others 2017b, 2018). A pitfall trap consisted of a plastic cylinder (13 cm depth, 6.75 cm diameter). Within each cylinder we placed a 100 ml plastic vial with outer diameter 6.70 cm and on top of the cylinder we placed a plastic funnel to guide the invertebrates into the vials. Each trap was cover with a cone-shaped and transparent plastic roof to protect the trap from rain (Vandegehuchte and others 2017b, 2018). Note that in the “None” plots only one trap was placed as control to check for effectiveness of the exclosure. 2) We vacuumed all invertebrates from a 60 x 60 cm area on another 1 x 1 m subplot with a suction sampler (Vortis, Burkhard manufacturing CO, Ltd., Rickmansworth, Hertfordshire, UK) every month from June to September 2013 (Vandegehuchte and others 2017b, 2018). For this purpose, we quickly placed a square plastic frame (60 x 60 x 40 cm) with a closable mosquito mesh sleeve attached to the top edge into the plot from the outside. The suction sample was then inserted into through the sleeve and operated for 45 s to collect the invertebrates (Vandegehuchte and others 2017b, 2018).
We sorted the ≈100 000 individuals collected with both methods by hand and identified each individual morphologically to the lowest taxonomic level feasible (59 taxa, including orders, suborders, subfamilies, families; phylum for Mollusca). These taxa belonged to the following feeding types: 19 herbivores, 16 detritivores, 9 predators, 8 mixed feeders, 5 omnivores and 2 non-classified feeders (or not feeding as adults) (Vandegehuchte and others 2017b). We summed the numbers from the two pitfall traps and the suction sampling over the course of the 2013 season to represent the aboveground invertebrate abundance and community composition of a plot. Note: we did not specifically attempt to catch flying invertebrates with e.g., sticky traps, thus a few flying insects may have been missed with our vacuum sampling approach.
Sampling of plant properties
The vascular plant species composition was assessed at peak biomass every summer (July) by estimating the frequency of occurrence of each species with the pin count method in each plot (Frank and McNaughton 1990). A total of 172 taxa occurred within our 90 plots and we calculated plant species richness for each plot separately. We used the 2013 data in this study. Plant quality was assessed every year in July and September; here we use plant quality at the end of the experiment (September 2013). Two 10 x 100 cm wide strips of vegetation per plot were clipped, combined, dried at 65°C, and ground (Pulverisette 16, Fritsch, Idar-Oberstein, Germany) to pass through a 0.5 mm sieve. Twenty randomly selected samples across all treatments were analysed for N (Leco TruSpec Analyser, Leco, St. Joseph, Michigan, USA) (Vandegehuchte and others 2015). Nitrogen concentrations of the other samples were then estimated from models established for the experiment and the entire SNP relating Fourier transform-near infrared reflectance (FT-NIR) spectra to the measured values of N using a multi-purpose FT-NIR spectrometer (Bruker Optics, Fällanden, Switzerland) (Vandegehuchte and others 2015). Root biomass was sampled every fall by collecting five 2.2 cm diameter x 10 cm deep soil samples (Giddings Machine Company, Windsor, CO, USA) per plot (450 samples year-1). The samples were dried at 30 °C and roots were sorted from the sample by hand. We sorted each sample for 1 h which allowed to retrieve over 90% of all roots present in the samples (Risch and others 2013). The roots were then dried at 65 °C for 48 and weighed to the nearest mg. We averaged the values per plot and used the 2013 data only in this study.
Sampling of edaphic communities
In 2009, 2010, and 2011 we collected three composited soil samples (5 cm diameter x 10 cm depth; AMS Samplers, American Falls, ID, USA) and assessed bacterial community structure using T-RFLP profiling (Liu and others 1997; Blackwood and others 2003; Hodel and others 2014). We detected a total of 89 operational taxonomic units (OTUs). These values are in accordance with other studies reporting OTU richness (Wirthner and others 2011; Zumsteg and others 2012; Meola and others 2014) using T-RFLP profiling, a method that detects the most abundant, and thus likely, the most relevant, taxa. We averaged the data over the three years of collections for our calculations. Microbial biomass carbon (MBC) was determined with the substrate-induced method (Anderson and Domsch 1978) every fall (September) between 2009 and 2013 by collecting three mineral soil samples (5 cm diameter × 10 cm mineral soil core, AMS Samplers, American Falls, ID, USA). The three samples were combined (90 samples for each sampling year), immediately put on ice, taken to the laboratory, passed through a 2-mm sieve and stored at 4°C. Again, we only used the 2013 data in this study.
Soil samples (5 cm diameter x 10 cm depth) to extract soil arthropods were collected in June, July, and August 2011 with a soil corer lined with a plastic sleeve to ensure an undisturbed sample (total of 270 samples). The plastic line core was immediately sealed on both ends using cling film and put into a cooler. All plots were sampled within three days and the extraction of arthropods started the evening of the sampling day using a high-gradient Tullgren funnel apparatus (Crossley and Blair 1991; Vandegehuchte and others 2015). Samples were kept in the extractor for four days and the soil arthropods were collected in 95% ethanol. All individuals were counted and each individual was identified morphologically to the lowest level feasible [76 taxa, including orders, suborders, subfamilies, families (Protura, Thysanoptera, Aphidina, Psylina, Coleoptera, Brachycera, Nematocera, Auchenorryncha, Heteroptera, Formicidae); sub-phylum for Myriapoda, for Acari and Collembola also including morpho-species). Note that we also included larval stages (nine of the 76 taxa) (Vandegehuchte and others 2015). All data were summed over the season. A detailed species list for mites and collembolans is published (Vandegehuchte and others 2017a) [https://doi.org/10.1371/journal.pone.0118679.s001]. Earthworms are rare in the SNP and therefore were not included. We collected eight random 2.2 cm diameter x 10 cm deep soil cores from each plot in September 2013 to determine the soil nematode community composition. The samples were mixed and the nematodes were extracted from 100 ml of fresh soil using Oostenbrink elutriators (Oostenbrink 1960). All nematodes in a 1 ml of the 10 ml extract were counted, a minimum of 150 individuals sample-1 were identified to genus or family level using (Bongers 1988), the numbers of all nematodes were extrapolated to the entire sample and expressed for a 100 g dry sample. In total we identified 63 genus or family levels (Vandegehuchte and others 2015). The list of all the nematodes found is published (Vandegehuchte and others 2015) [http://www.oikosjournal.org/appendix/oik-03341] or DOI: [doi: 10.1111/oik.03341].
We are aware that sampling soil microbes from 2009 to 2011 and soil arthropods in 2011 was not ideal, but we are positive that this does not bias the results. Most of the parameters measured in our experiment either already showed a treatment response after the first growing season (e.g., plant biomass) or did not respond over the entire time experiment (e.g., microbial biomass C). The microbial community composition (2009 – 2011) was highly influenced by inter-annual differences in temperature and precipitation, but did not differ between treatments or vegetation types (Hodel and others 2014). We therefore felt comfortable using the 2009 through 2011 data for describing the soil microbial community in our experimental treatments. Similarly, we are positive that our soil arthropod data are representative. We did assess soil arthropods in August 2012 and found no differences to the August 2011 data. However, we did not feel comfortable combining the 2011 June, July, August data with only August data for 2012 for our analyses.
Sampling of soil properties
We collected three soil samples (5 cm diameter x 10 cm depth) in each plot in September 2013 after removing the vegetation. First, we collected the top layer of mineral soil rich in organic matter, the surface organic layer or rhizosphere, typically 1 to 3 cm in depth with a soil corer (AMS Samples, American Falls, Idaho, USA). Second, we collected a 10 cm mineral soil core beneath this surface layer. The cores for each layer were composited, dried at 65 °C for 48 h and fine-ground to pass a 0.5 mm screen. We then analysed all samples for total C using a Leco TruSpec Analyser (Leco, St. Joseph, Michigan, USA). Mineral soil pH was measured potentiometrically in 1:2 soil:CaCl2 solution with an equilibration time of 30 min.
Soil net N mineralisation was assessed during the 2013 growing season (Risch and others 2015). For this purpose, we randomly collected a 5 cm diameter x10 cm deep soil sample with a soil corer (AMS Samples, American Falls, Idaho, USA) after clipping the vegetation in June 2013. After weighing and sieving (4 mm mesh) the soil, we extracted a 20 g subsample in 1 mol l-1 KCl for 1.5 h on an end-over-end shaker and thereafter filtered it through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnenmühle FineArt GmbH, Dassel, Germany). From these filtrates NO3- concentrations were measured colorimetrically (Norman and Stucki 1981) and NH4+with flow injection analysis (FIAS 300, Perkin Elmer, Waltham Massachusetts, USA) (Risch and others 2015). We dried the rest of the sample 105 °C to constant mass to determine fine,fraction bulk density. A second soil sample was collected within each plot in June 2013 with a corer lined with a 5 x 13 cm aluminium cylinder. The corer was driven 11.5 cm deep into the soil so that the top 1.5 cm of the cylinder remained empty. Into this space we placed a polyester bag (250 µm) filled an ion-exchanger resin to capture the incoming N. The bag was filled with a 1:1 mixture of acidic and alkaline exchanger resin (ion-exchanger I KA/ion exchanger IIIAA, Merck AG, Darmstadt, Germany). We then removed 1.5 cm soil at the bottom of the cylinder and placed a second resin exchanger bag into this space to capture the N leached from the soil column. To assure that the exchange resin was saturated with H+ and Cl- prior to filling the bags, the mixture was stirred with 1.2 ml l-1 HCl for 1 h and then rinsed with demineralized water until the electrical conductivity of the water reached 5 µm cm-1. The cylinder with the resin bags in place was reinserted into the soil with the top flush to the soil surface and incubated for three months. We recollected the cylinders in September 2013. Each resin bag and 20 g of sieved soil (4 mm mesh) from each cylinder were then separately extracted with KCl and NO3- and NH4+ concentrations were measured. Nitrate and NH4+ concentrations of all samples were then converted to a content basis by multiplying their values with fine fraction bulk density. Net N mineralisation was thereafter calculated as the difference between the N content of the samples collected at the end of the three-month incubation (including the N extracted from the bottom resin bag) and the N content at the beginning of the incubation (Risch and others 2015).
Soil CO2 emissions were measured every two weeks between 0900 and 1700 hrs from early May through late September 2013 with a PP-Systems SRC-1 soil respiration chamber (15 cm high, 10 cm diameter; closed circuit) attached to a PP-Systems EGM-4 infrared gas analyser (PP-Systems, Amesbury, MA, USA) on two locations per plot (Risch and others 2013). The chamber was placed on randomly placed, permanently installed PVC collars (10 cm diameter) driven 5 cm into the soil at the beginning of the study (Risch and others 2013). Freshly germinated plants growing within the collars were removed prior to each measurement to avoid measuring plant respiration or photosynthesis. The two measurements collected per plot and sampling date were averaged.
Soil moisture (with time domain reflectometry; Field-Scout TDR-100, Spectrum Technologies, Plainfield, Illionois, USA) and temperature (with a waterproof digital pocket thermometer; Barnstead International, Dubuque, Iowa, USA) were measured at five random locations per plot every two weeks during the growing seasons during the experiment for the 0 to 10 cm depth (Risch and others 2013, 2015). As soil moisture and soil temperature were highly negatively correlated (Risch and others 2013), we only used soil moisture for this study. We used plot-level averages of all values available to capture soil moisture variability during the five years of the experiment. The results remained unchanged when we only used soil moisture from the 2013 growing season.
Numeral calculations and statistical analyses
Ecosystem coupling. We conducted principal component analyses (PCAs; unscaled) at the complete dataset level using the abundances of each taxonomical entity to describe each of the five different communities used in this study: aboveground-dwelling invertebrates, vascular plants, soil microorganisms, soil arthropods and soil nematodes. We retained the first two components (PCA axis 1 and PCA axis 2) of each analysis as we found them to adequately represent the temporal and spatial variability of our 90 treatment plots in previous studies55,67. Together they explained a total of 71.70% of the variation for aboveground invertebrates, 44.36% for plants, 44.85% for soil microorganisms, 61.85% for soil arthropods and 77.19% for soil nematodes. In addition, we used soil pH and soil organic C content as a proxy for soil chemical properties, soil bulk density as a proxy for soil physical properties and soil moisture (negatively correlated with soil temperature) as a proxy for soil micro-climatic conditions for an overall total of fourteen constituents.
We calculated ecosystem coupling9 for each exclosure treatment within each vegetation type (i.e., 2  5 treatment combinations in total) as an integrated measure of pairwise ecological interactions between ecosystem constituents representing ecological communities and the soil abiotic environment. These ecological interactions are defined by non-parametric Spearman rank correlation analyses between two constituents, excluding interactions involving two abiotic constituents (e.g., soil pH vs. soil moisture) and interactions between the first (PC1) and second (PC2) component of each community type, as these are orthogonal by definition. Interactions between abiotic constituents were excluded from the analyses because the focus of our study was on communities and how they interact with one another and their surrounding environment; therefore, including abiotic-abiotic interactions was not of interest here. Given that the effectiveness of our experimental design resulted in that no community composition data of aboveground-dwelling invertebrates was available for the “None” plots (all animals excluded), only thirteen instead of fourteen constituents were included in the ecosystem coupling calculations for this treatment. The complete absence of aboveground invertebrates represents the most extreme case of disturbance between aboveground animal communities and the rest of the ecosystem constituents. This may have resulted in a slight overestimation of ecosystem coupling for these plots.
	Average ecosystem coupling was calculated as follows:
Ecosystem coupling=
where Xi is the absolute Coupling was calculated value of the Spearman’s rho coefficient of the ith correlation for each treatment within each vegetation type (i.e., based on nine replicates each), considering and n is the number of pairwise comparisons considered (n = a total of 80; interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).	Coupling was calculated for each treatment within each vegetation type (i.e., based on nine replicates each), considering a total of 80 interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).
To establish whether constituents were significantly and positively coupled within treatments (i.e., the average of their correlation coefficients were greater than in a null model where correlation only happens by chance), we calculated one-tailed p-values based on permutation tests with 999 permutations.

We considered six ecosystem functions and process rates commonly used to assess ecosystem functioning (Meyer and others 2015; Manning and others 2018). Plant N content represents a measure of forage quality, while plant richness has been shown to stabilise biomass production, thus allowing the system to respond to changes in herbivory. Soil net N mineralisation, soil respiration, root biomass, and microbial biomass represent fluxes or stocks of energy. For all functions and processes higher values represent higher functioning (Manning and others 2018). All these variables were measured in the last year of the experiment (2013). We then quantified ecosystem multifunctionality using the multiple threshold approach (Byrnes and others 2014; Manning and others 2018), which considers the number of functions that are above a certain threshold, over a series of threshold values (typically 10-99%) that are defined based on the maximum value of each function. We weighted all our functions equally for these calculations (Manning and others 2018). The number of functions in a plot with values higher than a given threshold value for the respective function is summed up. The sum represents ecosystem multifunctionality for that plot. Given that choosing any particular threshold as a measure of ecosystem multifunctionality is arbitrary, we calculated the average of thresholds from 10-90% (in 10% intervals) as a more integrated representation of ecosystem multifunctionality.
We used Pearson correlations to explore the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and ecosystem multifunctionality by calculating the slopes of all relationships between ecosystem coupling and multifunctionality for all thresholds between 10 and 99%. We also related ecosystem coupling with the average of multifunctionality at thresholds between 30-80% as explained before and considered this correlation as a robust indication of the type of association between these two variables. In addition, we explored the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and individual ecosystem functions. The effects of exclosures and vegetation type on individual functions and multifunctionality were evaluated using linear mixed effects models ('lme' function of the nlme package), with exclosure and vegetation type as fixed effects and fence as a random factor. All statistical analyses and numerical calculations were done in R version 3.4.0 (R Core Team 2016).

#References

- Anderson J, Domsch K. 1978. A physiological method for the quantitative measurement of microbial biomass in soil. Soil Biol Biochem 10:215–21.
- Blackwood CB, Marsh T, Kim S-H, Paul EA. 2003. Terminal Restriction Fragment Length Polymorphism Data Analysis for Quantitative Comparison of Microbial Communities. Appl Environ Microbiol 69:926–32. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC143601/
- Bongers T. 1988. De nematoden von Nederland. Schoorl, The Netherlands: Pirola
- Byrnes JEK, Gamfeldt L, Isbell F, Lefcheck JS, Griffin JN, Hector A, Cardinale BJ, Hooper DU, Dee LE, Duffy JE. 2014. Investigating the relationship between biodiversity and ecosystem multifunctionality: Challenges and solutions. Methods Ecol Evol 5:111–24.
- Crossley DAJ, Blair JM. 1991. A high-efficiency low-technology Tulgren-type extractor for soil microarthopods. Agric Ecosyst Environ 34:187–92.
- Dudley N. 2008. Guidelines for applying protected area managment categories. Gland: IUCN
- Frank DA, McNaughton SJ. 1990. Aboveground biomass estimation with the canopy intercept method: A plant growth form caveat. Oikos 57:57–60.
- Haynes AG, Schütz M, Buchmann N, Page-Dumroese DS, Busse MD, Risch AC. 2014. Linkages between grazing history and herbivore exclusion on decomposition rates in mineral soils of subalpine grasslands. Plant Soil 374.
- Hodel M, Schütz M, Vandegehuchte ML, Frey B, Albrecht M, Busse MD, Risch AC. 2014. Does the aboveground herbivore assemblage influence soil bacterial community composition and richness in subalpine grasslands? Microb Ecol 68:584–95.
- Liu WT, Marsh TL, Cheng H, Forney LJ. 1997. Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA. Appl Environ Microbiol 63:4516–22. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC168770/
- Manning P, van der Plas F, Soliveres S, Allan E, Maestre FT, Mace G, Whittingham MJ, Fischer M. 2018. Redefining ecosystem multifunctionality. Nat Ecol Evol 2:427–36. https://doi.org/10.1038/s41559-017-0461-7
- Meola M, Lazzaro A, Zeyer J. 2014. Diversity, resistance and resilience of the bacterial communities at two alpine glacier forefields after a reciprocal soil transplantation. Environ Microbiol 16:1918–34. https://onlinelibrary.wiley.com/doi/abs/10.1111/1462-2920.12435
- Meyer ST, Koch C, Weisser WW. 2015. Towards a standardized Rapid Ecosystem Function Assessment (REFA). Trends Ecol Evol 30:390–7. http://www.sciencedirect.com/science/article/pii/S0169534715000968
- Norman R., Stucki JW. 1981. The determination of nitrate and nitrite in soil extracts by ultraviolet spectrophotometry. Soil Sci Soc Am J 45:347–53.
- Ochoa-Hueso R. 2016. Non-linear disruption of ecological interactions in response to nitrogen deposition. Ecology 87:2802–2814.
- Oostenbrink M. 1960. Estimating nematode populations by some selected methods. In: Sasser NJ, Jenkins WR, editors. Nematology. Chapel Hill, NC, USA: University of North Carolina Press. pp 85–101.
- R Core Team. 2016. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing
- Risch AC, Haynes AG, Busse MD, Filli F, Schütz M. 2013. The response of soil CO2 fluxes to progressively excluding vertebrate and invertebrate herbivores depends on ecosystem type. Ecosystems 16:1192–202.
- Risch AC, Schütz M, Vandegehuchte ML, Van Der Putten WH, Duyts H, Raschein U, Gwiazdowicz DJ, Busse MD, Page-Dumroese DS, Zimmermann S. 2015. Aboveground vertebrate and invertebrate herbivore impact on net N mineralization in subalpine grasslands. Ecology 96:3312–22.
- Schütz M, Risch AC, Achermann G, Thiel-Egenter C, Page-Dumroese DS, Jurgensen MF, Edwards PJ. 2006. Phosphorus translocation by red deer on a subalpine grassland in the Central European Alps. Ecosystems 9:624–633.
- Schütz M, Risch AC, Leuzinger E, Krüsi BO, Achermann G. 2003. Impact of herbivory by red deer (Cervus elaphus L.) on patterns and processes in subalpine grasslands in the Swiss National Park. For Ecol Manage 181:177–88.
- Vandegehuchte ML, van der Putten WH, Duyts H, Schütz M, Risch AC. 2017a. Aboveground mammal and invertebrate exclusions cause consistent changes in soil food webs of two subalpine grassland types, but mechanisms are system-speciﬁc. Oikos 126:212–23.
- Vandegehuchte ML, Raschein U, Schütz M, Gwiazdowicz DJ, Risch AC. 2015. Indirect short- and long-term effects of aboveground invertebrate and vertebrate herbivores on soil microarthropod communities. PLoS One 10:e0118679.
- Vandegehuchte ML, Schütz M, de Schaetzen F, Risch AC. 2017b. Mammal-induced trophic cascades in invertebrate food webs are modulated by grazing intensity in subalpine grassland. J Anim Ecol 86:1434–46.
- Vandegehuchte ML, Trivellone V, Schütz M, Firn J, de Schaetzen F, Risch AC. 2018. Mammalian herbivores affect leafhoppers associated with specific plant functional types at different timescales. Funct Ecol 32:545–55.
- Wirthner S, Frey B, Busse MD, Schütz M, Risch AC. 2011. Effects of wild boar (Sus scrofa L.) rooting on the bacterial community structure in mixed-hardwood forest soils in Switzerland. Eur J Soil Biol 47:296–302. http://dx.doi.org/10.1016/j.ejsobi.2011.07.003
- Zumsteg A, Luster J, Göransson H, Smittenberg RH, Brunner I, Bernasconi SM, Zeyer J, Frey B. 2012. Bacterial, Archaeal and Fungal Succession in the Forefield of a Receding Glacier. Microb Ecol 63:552–64. https://doi.org/10.1007/s00248-011-9991-8

ecosystem-coupling

display_name: ecosystem-coupling

ecosystem-functions

display_name: ecosystem-functions

ecosystem-multifunctionality

display_name: ecosystem-multifunctionality

exclusion

display_name: exclusion

grassland

display_name: grassland

grazing

display_name: grazing

invertebrates

display_name: invertebrates

subalpine

display_name: subalpine

swiss-national-park

display_name: swiss-national-park

educ

en: Education, culture and sport

en: Ecosystem coupling and multifunctionality - exclosure experiment


en: avalanche-forecasting snow-instability avalanche danger-level avalanche-danger snow

en: Field observations of snow instabilities

en: Read-me file

media_type: application/pdf

en: This read-me file contains the descriptions of the variables.

format: PDF

en:
This data set includes 589 snow profile observations including a rutschblock test, observations of signs of instability and an assessment of the local avalanche danger level, mainly recorded in the region of Davos (eastern Swiss Alps) during the winter seasons 2001-2002 to 2018-2019.

These data were analyzed and results published by Schweizer et al. (2021). They characterized the avalanche danger levels based on signs of instability (whumpfs, shooting cracks, recent avalanches), snow stability class and new snow height.

The data are provided in a csv file (589 records); the variables are described in the corresponding read-me file.

These data are the basis of the following publication: Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, Cryosphere, 15, 3293-3315, https://doi.org/10.5194/tc-15-3293-2021, 2021.

 Acknowlegements
Many of the data were recorded by SLF observers and staff members, among those Roland Meister, Stephan Harvey, Lukas Dürr, Marcia Phillips, Christine Pielmeier and Thomas Stucki. Their contribution is gratefully acknowledged.

avalanche

display_name: avalanche

avalanche-danger

display_name: avalanche-danger

avalanche-forecasting

display_name: avalanche-forecasting

danger-level

display_name: danger-level

snow

display_name: snow

snow-instability

display_name: snow-instability

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: roads jupyter gis buffer density geospatial

en: Road Density Calculation (Python Notebook) online

en: Plots

en: Random plot locations

mimetype: application/zip

en: Road data from OSM for the surroundings of Menzberg, Switzerland

en:
This dataset contains the road and plot data used for the geospatial analysis example showcased in "Fostering Open Science at WSL with the EnviDat Environmental Data Portal", a contribution to the 5th Open Source Geospatial Research and Education Symposium (OGRS), 2018.
The example uses Jupyter Notebook to calculate road densities in the neighbourhood of sample plot locations with Python. Road data were extracted from OpenStreetMap, while the point data (sample plots) were generated manually.

buffer

display_name: buffer

density

display_name: density

geospatial

display_name: geospatial

gis

display_name: gis

jupyter

display_name: jupyter

roads

display_name: roads

en: Dataset for OGRS 2018 publication


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

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

en: RESTORE WP4_Questionnaire_Italian.pdf

en: Questionnaire in Italian

en: Study protocol of the survey

en: RESTORE WP4_Questionnaire_French.pdf

en: Questionnaire in French

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

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

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

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

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

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

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

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

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

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

en: RESTORE WP4_Questionnaire_German.pdf

en: Questionnaire in German

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

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

landscape-perception

display_name: landscape-perception

outdoor-restoration

display_name: outdoor-restoration

ppgis

display_name: ppgis

survey

display_name: survey

traffic-noise

display_name: traffic-noise

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


maintainer: Stefan Klesse

en: temperature-reconstruction mxd wood-anatomy paleoclimate tree-ring

media_type: text/plain

en: This dataset contains the mean chronologies of MXD and QWA of the relict and living wood material presented in Figure 3. It also contains the JJA temperature record of the Salekhard Meteo station.

format: TXT

en:
This dataset contains data presented in Figure 2 in the paper "New Prospects for Temperature Reconstructions from Relict Wood Using Tree-Ring Anatomy".

Sample ID = Sample ID
group = living or relict wood material
Bulk wood density = volumetric measured density (kg / m^3)
Mean Tree Ring Density	= Density derived via X-ray densitometry (kg / m^3)
Cellulose_to_wood ratio = Holo-cellulose-to-wood ratio
Anatomical density = Area of cell wall / (area of cell wall + lumen area)

en: Data (Quantitative Wood Anatomy, Maximum Latewood Density, Volumetric Density, holo-cellulose-to-wood-ratio) used in the manuscript "New Prospects for Temperature Reconstructions from Relict Wood Using Tree-Ring Anatomy". Wood samples stem from Larix sibirica collected in the Yamal region, northeastern Siberia, Russia. We investigated wood properties of living trees and subfossil wood buried in riverbank sediments, dated to approximately -2760 to -2600 BCE. Our goal was to evaluate the integrity of climate proxies in sub-fossil wood and develop practical methods to detect wood degradation when reconstructing Late Holocene and Common Era climate conditions using tree-ring data.

mxd

display_name: mxd

paleoclimate

display_name: paleoclimate

temperature-reconstruction

display_name: temperature-reconstruction

tree-ring

display_name: tree-ring

wood-anatomy

display_name: wood-anatomy

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: Quantitative Wood Anatomy and Maximum Latewood Density from Yamal peninsula


en: taxonomic-diversity plants migclim zonation phylogenetic-diversity land-use-change european-alps functional-diversity species-distribution-models global-change conservation beta-diversity

en: Data Access

format: GeoTIFF

en: Updated SDM outputs

en: Refined scenarios of plants distribution in the European Alps

en:
This repository contains extensive data for the European Alps:
  - Observations of ~3,500 plant species
  - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal)
  - Flora migration rates (categorical) and ecological preferences (continuous indicator values)
  - Regional maps of barriers to migration and water bodies at 100-m resolution
  - Sampling effort, distance to roads and cities predictors at 100-m resolution
  - Present and future abundances over the study region at 1-km resolution (~2,000 species)
  - Present and future multifaceted and uniqueness of the European Alps' Flora at 1-km resolution
  - Present and future conservation recommendations at 1-km resolution (26 current and future strategies)
  - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees)
  - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them

beta-diversity

display_name: beta-diversity

conservation

display_name: conservation

european-alps

display_name: european-alps

functional-diversity

display_name: functional-diversity

global-change

display_name: global-change

land-use-change

display_name: land-use-change

migclim

display_name: migclim

phylogenetic-diversity

display_name: phylogenetic-diversity

plants

display_name: plants

species-distribution-models

display_name: species-distribution-models

taxonomic-diversity

display_name: taxonomic-diversity

zonation

display_name: zonation

en: Present and future multifaceted plant diversity, uniqueness and conservation in the European Alps


latent-heat-flux-over-snow

maintainer: Sergi González-Herrero

title_for_slug: latent-heat-flux-over-snow

en: swiss-alps meteo-station snow meteo meta central-asia

en:
This file includes the Jupyter notebooks to reproduce the method and the figures of the paper. This consists in:
- 3 files for Alpine Valley
- 3 files for Antarctic Plateau
- 1 file for application at Pamir mountains

en:
This file includes the datasets used to compare the methods. It includes:
- EC Postprocessed heat flux data from sonic anemometer and gas analyzer. Files named: [SITE]_(NO)WatVap_eddypro_OUT_[type].csv
- MOST Postprocessed data from SNOWPACK model. Files named [SITE]_[year]_[METHOD].smet
- Configuration files for SNOWPACK (.ini files) and Eddypro (.eddypro files)

[SITE] correspond to DISHMA/DISH or Alpine Valley, PLATEAU/PLAT for Antarctic Plateau, TAJ for Pamir mountains (no EC)
[type] correspond to fluxnet, full_output or qc_details
[METHOD] indicates the stability model used and/or the roughness length

en:
This dataset contains three case studies where are compared the latent heat flux calculated using the Eddy Covariance (EC) method, the Monin-Obukhov Similarity Theory (MOST) and the modified Bowen-ratio method presented in the research paper "Using the sensible heat flux eddy covariance based exchange coefficient to calculate latent heat from moisture mean gradients overn snow". We include the datasets, the code and the ini files for reproduce the research.
The dataset contain two files in zip format:
 1. Sowftware-Heat_fluxes_on_snow: Jupyter notebooks to reproduce the method and the figures of the paper.
 2. data.zip: Datasets used to compare the methods

central-asia

display_name: central-asia

meta

display_name: meta

meteo

display_name: meteo

meteo-station

display_name: meteo-station

snow

display_name: snow

swiss-alps

display_name: swiss-alps

en: Latent Heat Flux over Snow


radar-wind-profiler-davos-wolfgang

maintainer: Maxim Hervo

title_for_slug: radar-wind-profiler-davos-wolfgang

en: wind-direction wind-speed radar wolfgangpass raclets

en: Radar Wind Profiler Data

en: A Vaisala Lap3000 was installed at Davos Wolfgang to record wind profiles. The time format is number of days fom January 0, 0000 in UTC with a 10 minute resolution.

en: The RADAR wind profiler from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 2171 m above sea level to 11079 m, with a temporal resolution of 10 minutes.

raclets

display_name: raclets

radar

display_name: radar

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wolfgangpass

display_name: wolfgangpass

en: RADAR Wind profiler Davos Wolfgang


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

media_type: image/png

en: Overview of most soil solution water isotopic signatures.

format: PNG

en: Water isotopic signature of deposition samples.

en: Water isotope signatures of soil solution samples.

en: Overview of most bulk soil water isotopic signatures.

en: Overview of most xylem water isotopic signatures.

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

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

en: Overview of most deposition water isotopic signatures.

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

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

forest-ecosystems

display_name: forest-ecosystems

soil-solution

display_name: soil-solution

water-budget

display_name: water-budget

water-isotopes

display_name: water-isotopes

xylem-water

display_name: xylem-water

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


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

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

mimetype: application/pdf

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

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

en: Factsheet summarising the key results of the study (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: Conclusions and recommendations of the project (in German).

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

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:
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


maintainer: Markus Didion

en: allometry stemwood tree-volume section-wise-diameter growing-stock branchwood

en: Updated dataset of outdated_Stem&Branchchwood_2024-02-29.txt, including corrections to the assignment of tree species to NFI main species and location information for two additional sites.

en: dataset with section-wise stem diameter and branchwood volume measurements. For column definitions see Column_definitions.pdf.

en: Revised version of the initial submission StemBranchwood_Didionetal_initialsubmission.pdf after review.

en: manuscript submitted for publication.

en: additional background information on this work

en: definitions of data in columns contained in the dataset Stem&Branchchwood_2024-02-29.txt

en:
The data presented here were prepared for publication in form of a data paper. The dataset presents an update of the information in Didion M, Herold A, Vulovic Z, Nitzsche J, Stillhard J (2022) Datasets for deriving functions for the stem- and branchwood volume in the Swiss National Forest Inventory. EnviDat. doi:https://www.doi.org/10.16904/envidat.358. The new dataset is based on an alternative, more accurate and comprehensive dataset, that was identified as the source of the dataset in Didion et al. 2022. It includes additional data, including newly digitized information.
The dataset includes section-wise stem measurements on 40’349 felled individual trees and empirically derived coarse (diameter >= 7 cm) and fine branch volume of 27’297 and 18’980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range. The dataset has undergone quality controls and due to its origin from 768 plots of the Experimental Forest Management project long-term consistency is assured.

allometry

display_name: allometry

branchwood

display_name: branchwood

growing-stock

display_name: growing-stock

section-wise-diameter

display_name: section-wise-diameter

stemwood

display_name: stemwood

tree-volume

display_name: tree-volume

en: A dataset of 40000 trees with section-wise measured stem diameter and length and volume of branches from across Switzerland


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

en: eemma vignette script

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

format: OCTET

mimetype: application/octet-stream

en: vignette data

en: A simple data set for the vignette script

mimetype: text/plain

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

en: eemma user's guide

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

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

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

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

eemma

display_name: eemma

emma

display_name: emma

end-member-mixing-analysis

display_name: end-member-mixing-analysis

mixing-models

display_name: mixing-models

stable-isotopes

display_name: stable-isotopes

tracers

display_name: tracers

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Yohann Chauvier

en: functional-diversity grain-size climate ecological-indicator-values taxonomic-diversity soil phylogenetic-diversity

en: Data access

en:
This dataset comprises a large array of ecological data for the European Alps:

  (1) Current soil and climate predictors at various resolutions.
  (2) GBIF observations of the European Alps Flora (~4,000 species).
  (3) Species habitat suitability maps (1,109 species; based on species observations filtered at 40x40-km)
  at various resolutions used in the study to generate (4); except 'expert'...
  (4) Expert, Taxonomic, phylogenetic and functional diversity of the study region at various resolutions
  (from 100-m to 40-km --> 100-m aggregated & mean to km + non-aggregated/predicted) for CLIM,
  SOIL and CLIM-SOIL models.
  (5) Ecological and altitudinal preferences of the European Alps Flora.
  (6) Data outputs of the related published article.
  (7) All scripts used for analyses.
  (8) Additional files used for analyses.
  (9) Improved set of species habitat suitability maps (~2,600 species; based on species observations
  filtered at 1x1-km) and related taxonomic diversity at 100-m resolution (aggregated to km +
  non-aggregated/predicted) for CLIM, SOIL and CLIM-SOIL models ---> not incorporated in the study.

climate

display_name: climate

ecological-indicator-values

display_name: ecological-indicator-values

functional-diversity

display_name: functional-diversity

grain-size

display_name: grain-size

phylogenetic-diversity

display_name: phylogenetic-diversity

soil

display_name: soil

taxonomic-diversity

display_name: taxonomic-diversity

en: Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity


en: digital-zoning-plans land-use-transformations land-use-simulations spatial-planning land-use-change

en: Legend simulated maps

en:
Table of Content: 1. General context of the data set "LUSzoning”; 2. Background and aims of the study using the data set LUSzoning; 3. The data set LUSzoning.
1. General context of the data set "LUSzoning".
The data set "LUSzoning" stands for Land-use simulations integrating zoning regulations in Spanish functional urban areas. The data set has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2021. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, digital zoning plans) into quantitative land-change modelling approaches at the urban regional level.
2.	Background and aims of the study using the data set “LUSzoning”.
As part of the CONCUR project, a specific task was to integrate planning spatial policies in land-change modelling. Planning can be implemented in modelling using either hard or gradual restrictions. Different studies have addressed the inclusion of spatial planning policies in land-use change modelling. However, the integration of zoning constraints is generally established as hard or Boolean-based restrictions (e.g., whether urban development is allowed or not), while not accounting for the spatial heterogeneity or gradual characteristics within planning zones (e.g., whether planning regulations allow low, medium or high urban density), though these could improve real patterns simulations in urban areas. We assume Spanish General Zoning plans were suitable to explore the integration of planning into land-change modelling as soft constrains because they define land-use intensities in the buildable zoning areas. In light of the above considerations, the overall aim of the study was to model urban land-use changes using a multi-scenario approach that integrates digitized zoning plans for the Functional Urban Areas (FUAs) of Madrid, Barcelona, Valencia, and Zaragoza. The following specific objectives were addressed: i) to analyse the role of planning by defining three future scenarios that integrate digitized zoning plans and one scenario that assumes almost no planning intervention; ii) to introduce zoning constraints that reflect different degrees of urban densities; iii) to generate a transferable spatially-explicit modelling framework to integrate planning into land-use change simulations.
Four future land-use demands scenarios were defined for the FUAs. Storylines were created considering probable development scenarios related to zoning plans, current Spanish legislation and sustainability goals defined along two axes: a high market-oriented vs. high planning-intervention axis, and an axis of short-term economic growth vs. long-term sustainable growth. The sustainable development scenario (S1) is characterized by low gross floor area (GFA) growth that is limited to areas that are currently under development according to zoning plans. The business-as-usual scenario (S2) is characterized by medium GFA growth in the range of on-going trends. The strong development scenario (S3) is characterized by high GFA growth rates. Growth is restricted to buildable areas without urbanization project designated in zoning plans. The unrestricted development scenario (S4) prioritizes a high degree of market liberalization characterized by high GFA growth that surpasses population demands. S4 follows a rapid economic growth pattern with almost no planning intervention.
3.	 The data set “LUSzoning”.
The dataset includes 16 .asc raster layers providing the simulated land-uses under four defined scenarios for Barcelona, Madrid, Valencia and Zaragoza Functional Urban Areas (FUAs) for 2030. The simulated raster layers were created using CLUMondo simulation framework and have a spatial resolution of 30m. The .asc layers name include the name of the FUA and scenario number. For example, the output from simulating the urban growth for the city of Zaragoza under Scenario 2 is named “Zaragoza_S2.tif”. Furthermore, a .txt file named “Legend.txt” includes the numeric value of the land-use and the category of land-use that represents to interpret the .asc raster layers. The name of the land-use classes is a reclassification of the Urban Atlas 2012 land-use classes within the four Spanish FUAs analyzed.

digital-zoning-plans

display_name: digital-zoning-plans

land-use-change

display_name: land-use-change

land-use-simulations

display_name: land-use-simulations

land-use-transformations

display_name: land-use-transformations

spatial-planning

display_name: spatial-planning

en: LUSzoning: Land-use simulations integrating zoning regulations in Spanish functional urban areas


causal-effect-of-lup

title_for_slug: causal-effect-of-lup

en: annual-effect plan-evaluation land-use-change land-use-planning psm-did

en: causal-effect-of-lup

en:
Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities
PSM_DID.R can re-produce the results

en:
Title: Does zoning contain built-up land expansion? Causal evidence from Zhangzhou City, China.

Research objective: Built-up land zoning is an imporatant policy measure of land use planning (LUP) to contain built-up land expansion in China. We used a difference-indifference model with propensity score matching to estimate the average and annual effect of built-up land zoning on built-up land expansion in Zhangzhou City, China between 2010 and 2020.

Data: Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020.
XZQDM2 is villages' unique administrative ID;
Area is the land area of village i;
Dis2water is the Euclidean distance from village i to the nearest waterbody;
Dis2coastl is the Euclidean distance from village i to the nearest coastline;
Dis2city is the the Euclidean distance from village i to the city center;
Dis2county is the the Euclidean distance from village i to the nearest county center;
Elevation is the the average elevation within village i;
Dis2road is the the Euclidean distance from village i to the nearest road;
Nei_Built_ is the the area of built-up land (Nei Built.upit) in the neighboring villages of village i in year t;
Treated is a binary variable, Treated = 1 to the villages that were partially or entirely located inside the development-permitted zones, and Treated = 0 to the villages that were entirely located outside the development-permitted zones;
Intensity is the percentage of land that was assigned to the development-permitted zones in village i;
Year represent the year in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020;
BuLE is the dependent variable, representing built-up land expansion in village i in year t;
Town is town' unique administrative ID.

Method: First, we employed propensity score matching to overcome the selection bias and satisfy the parallel trend
assumption. Second, we built four Difference-in-Difference models to estimate the average and annual effect.

annual-effect

display_name: annual-effect

land-use-planning

display_name: land-use-planning

plan-evaluation

display_name: plan-evaluation

psm-did

display_name: psm-did

en: Causal effect of LUP


maintainer: Joan Casanelles Abella

en: urban poznan tartu antwerp paris angiospermae zurich floral-resources plants floral-inventories

en: List of plant species sampled in the five cities (Antwerp, Paris, Poznan, Tartu and Zurich) during the different sampling perionds (four for Zurich, three for the other cities) with their frequency (oc1-oc16) within a 100 m radius

en: Floral units, mean number of flowers per floral unit and floral abundance of the different plants counted in quadrats in the study sites in Zurich during the four sampling periods

en: Trait values for the sampled plants

en: Data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland) sampled between April and July 2018.

angiospermae

display_name: angiospermae

antwerp

display_name: antwerp

floral-inventories

display_name: floral-inventories

floral-resources

display_name: floral-resources

paris

display_name: paris

plants

display_name: plants

poznan

display_name: poznan

tartu

display_name: tartu

urban

display_name: urban

zurich

display_name: zurich

educ

en: Education, culture and sport

en: Flowering Plants (Angiospermae) in Urban Green Areas in five European Cities


maintainer: Elisabeth Hafner

en: avalanche reliability avalanche-mapping 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

envidat

en: EnviDat: the Environmental Data Portal

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

en: Data reliability study: avalanche size estimates and outlines


maintainer: Franziska Gerber

en: cryowrf antarctica reproducibility blowing-snow surface-mass-balance

en: Processed WRF output (run with Noah-MP land surface model) used for the analysis and the figures in the paper.

en: Namelist and input files and information to run the WRF and CRYOWRF simulations used in the paper.

en: Static input data from the WRF simulations (used in the python scripts in Scripts_Reproducibility).

en: Processed CRYOWRF output used for the analysis and the figures in the paper.

en: Python scripts to reproduces figures in Gerber et al., 2022, submitted.

en: Python scripts to reproduces figures in Gerber et al., 2022, revised

en:
This dataset contains data and scripts for "CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB" (Gerber et al., submitted).

* Simulation_setup: Namelists and input information to run the simulation. Some input files need to be downloaded from Sharma et a., 2021.
* Static_input: Static topography input file of WRF (geo_em.d01).
* WRF_27km_NoahMP: Preprocessed WRF output of the simulation run with the WRF using the surface parameterization Noah-MP to reproduce the figures and results in the paper.
* WRF_27km_CRYOWRF: Preprocessed WRF output of the simulation run with CRYOWRF to reproduce the figures and results in the paper.
* Scripts_Reproducibility: Python scripts to reproduce the figures and results in the paper.

Note:

* To run some of the scripts Atmospheric Weather station data needs to be prepared using Gerber and Lehning, 2022.
* AWS data is not provided and needs to be downloaded from the corresponding databases. Please make sure to comply with the respective terms and conditions.

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

cryowrf

display_name: cryowrf

reproducibility

display_name: reproducibility

surface-mass-balance

display_name: surface-mass-balance

en: Reproducibility dataset for CRYOWRF validation


maintainer: Elisabeth D. Hafner

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

en: 1999 avalanche data

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

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

 Datenbeschrieb

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

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

 Bildverfügbarkeit:

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

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

- **FAN Publikation** (Info folgt)

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

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

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

 Data description

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

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

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

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

- **FAN Publication** (info to follow)

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

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

aerial-imagery

display_name: aerial-imagery

avalanche-activity

display_name: avalanche-activity

educ

en: Education, culture and sport

en: Avalanche outlines February and March 1999 from aerial imagery


maintainer: Christoph Marty

en: Dataset

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

en: Graubunden input

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

mimetype: application/x-tar

en: Aare input

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

en: CH2014 REPORT

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

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

en:
 Overview

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

 Experimental Setup

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

 Data

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

climate-change

display_name: climate-change

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

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: Alpine3D simulations of future climate scenarios CH2014


en: runoff stable-water-isotopes alptal groundwater streamflow

en: Precipitation and discharge event A

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
With any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
Q: Discharge in mm per hour at catchment outlet (697234,210472)
P: Rainfall in mm per hour averaged for two rain gauges (697494,210675) and (698004, 210680)

mimetype: text/csv

en: Deuterium streamwater extra event

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date and time of sample collection (UTC; year-month-day, hours:minutes)
Q d.D: Deuterium composition (per mil) of streamwater sampled at the catchment outlet (697234,210472); the first sample is used as pre-event streamflow sample

en: Deuterium rainfall event C

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
P d.D: Deuterium composition of the rainfall samples (per mil)
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)

en: Precipitation and discharge event B

en: Deuterium streamwater event A

en: Precipitation and discharge event C

en: Deuterium rainfall event B

en: Deuterium streamwater event C

en: Deuterium rainfall extra event

en: Deuterium rainfall event A

en: Deuterium groundwater and baseflow October 2016

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

Date: Date (year-month-day)
Location: Well number or outlet
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)
riparian: riparian wells (i.e. from the riparian-like cluster as presented in Kiewiet et al. (2019)) are marked a one, all other wells are marked a zero
d.D (per mil): Deuterium composition (per mil)

en: Deuterium groundwater and baseflow October 2017

en: Deuterium groundwater and baseflow August 2017

en: Precipitation and discharge extra event

en: Deuterium streamwater event B

en:
This dataset includes discharge and rainfall measurements and deuterium compositions of streamflow, rainfall and groundwater, for four rainfall events and three baseflow snapshot campaigns in the Studibach (Alptal, Switzerland). More specifically, we present the following data:

- Specific discharge at the catchment outlet at 5-minute resolution (mm per hour);
- Rainfall at 5-minute resolution (mm per hour);
- Rainfall deuterium composition (‰);
- Stormflow deuterium composition (‰);
- Groundwater and baseflow deuterium compositions (‰).

For the files containing rainfall and discharge timeseries (QP), and rainfall and streamwater deuterium compositions ("Deuterium_Rainfall" and "Deuterium_Streamwater"), we added the corresponding event identifier (A, B, C or extra) to the file names. For the files containing the groundwater and baseflow deuterium values ("Deuterium_Snapshot") we added the sample collection date to the file name. We included the X and Y coordinates for each data point (coordinate system: CH1903 LV3) as well as the date and time (UTC).

More information on the data collection and preparation can be found in Kiewiet et al. (in review). A detailed description of the baseflow snapshot campaigns can also be found in Kiewiet et al., 2019.

en: Discharge, rainfall, and deuterium compositions of streamwater, rainwater and groundwater, for four rainfall events in the Studibach, Alptal, Switzerland