en:
Increase in the volume of stemwood with bark of the surviving trees and shrubs starting at 12 cm dbh between two inventories and the losses (modelled for the half period), plus the volume of gains. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

avalanches-silvaprotect

display_name: avalanches-silvaprotect

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

increment-star

display_name: increment-star

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

rockfall-silvaprotect

display_name: rockfall-silvaprotect

site-quality

display_name: site-quality

tree-species-woody-species

display_name: tree-species-woody-species

en: Increment*


en: forest-snow snow-energy-balance snow-models meteorological-data forest-structure

state: active

type: dataset

en: AWS data

en: Model driving data from operational AWS

format: SERVICE

en: Site overview

en: Overview of field sites

en: Point data

media_type: application/zip

en: Wind speed data acquired with the portable meteorological stations

format: ZIP

en: Grid data

en: Canopy structure and snow depth data at the grids

en: Transect data

en: All meteorological and canopy structure data acquired at cable car transects

en:
This dataset contains datasets of sub-canopy meteorological variables acquired in coniferous forest stands in Switzerland (Davos, Engadine) and Finland (Sodankylä) during the winters 2018 and 2019. The data are presented and used in the publication:
Mazzotti, G., Essery, R., Webster, C., Malle, J., & Jonas T. (2020)
Process-level evaluation of a high-resolution forest snow model using observations from mobile multi-sensor platforms
Water Resources Research, under review

The above publication must be cited when using this dataset, and the user is referred to the publication for additional detail.

Data are grouped into 4 folders:
1) Point data includes wind speed data measured with stationary meteorological stations
2) Transect data includes data of incoming short- and longwave radiation, air and snow surface temperature acquired with an automated calblecar system along within-stand transects
3) Grid data includes data of incoming short- and longwave radiation, air and snow surface temperature acquired on 40x40m gridded plots using a handheld instrument, as well as snow depth data measured at the same grids.

Canopy structure information derived from hemispherical images is included for each all surveyed locations as well, and an overview of the field sites is provided.

forest-snow

display_name: forest-snow

forest-structure

display_name: forest-structure

meteorological-data

display_name: meteorological-data

snow-energy-balance

display_name: snow-energy-balance

snow-models

display_name: snow-models

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Distributed sub-canopy datasets from mobile multi-sensor platforms (CH / FIN, 2018-2019) for hyper-resolution forest snow model evaluation


maintainer: EnviDat Support

en: climate-change-impact cryosphere natural-climate-variability rain-on-snow uncertainty snow-water-equivalent

en: climate realizations

en: The dataset contains for three variables (snow water equivalent, surface water input and liquid precipitation) 50 realizations of current and future climate periods for two time horizons (mid end end of century), two emission senarions (RCP 4.5 and 8.5) and 10 climate model chains (all EUR11 chains within CH2018). To quantify natural climate variability for projections of snow conditions and resulting rain-on-snow (ROS) flood events, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The data was extracted in 2021 from original model output.

climate-change-impact

display_name: climate-change-impact

cryosphere

display_name: cryosphere

natural-climate-variability

display_name: natural-climate-variability

rain-on-snow

display_name: rain-on-snow

snow-water-equivalent

display_name: snow-water-equivalent

uncertainty

display_name: uncertainty

en: Multiple realizations of daily snow water equivalent, surface water input and liquid precipitation projections for mid- and late-century


pollination-experiment-insect-traits

title_for_slug: pollination-experiment-insect-traits

en: seed-production urban-ecology gardens hoverflies plant-pollinator-interactions biodiversity insects urbanization bees zurich phytometer

en: Trait values of bees

en:
The data contains the following columns:
- SiteID: numeric, the identity of the garden where the bee was sampled.
- Taxonname: character, species name
- Lecty: numeric discreet, a mesure of specialisation degree. Levels: 1 = monolectic; 2=oliolectic; 3=polilectic
- Pheno.start= numeric discreet, the starting week of the flying activity.
- Pheno.end= numeric discreet, the ending week of the flying activity.
- Pheno.duration=numeric discreet, Pheno.end - Pheno.start
- Daytime.activity = numeric continuous, individual-level measure of the day hour when the bee was sampled
- ITD = numeric continuous, individual-level measure of the InterTegular Distance, a metric of mobility in mm.
- Rel.tongue.length =numeric continuous, individual-level measure of the relative tongue length (Tongue length / ITD), a metric of specilaisation degree, in mm.

en: Understanding the interplay of local and landscape-scale drivers of plant-pollinator interactions is crucial to maintaining pollination services in urban environments. The data contains plant-pollinator interactions changed across two independent gradients of local flowering plant species richness and landscape-scale urbanisation level (proportional area of impervious surface within a 500-m radius) in 24 home gardens in the city of Zurich, Switzerland. The data also contains the trait values (tongue length, body size and activity time) of all visiting wild- and honeybees.

bees

display_name: bees

biodiversity

display_name: biodiversity

gardens

display_name: gardens

hoverflies

display_name: hoverflies

insects

display_name: insects

phytometer

display_name: phytometer

plant-pollinator-interactions

display_name: plant-pollinator-interactions

seed-production

display_name: seed-production

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

zurich

display_name: zurich

en: Pollination experiment: insect traits


en: decomposition fungi biogeochemical-cycling bacteria carrion yellowstone soil-biodiversity carcass wolf detritus

en:
Site	ID for different carcasses across the Yellowstone Landscape
Location	Sampling location, either under carcass or control plot
Species	Indicating whether carcass was from bison or elk
Gender	Gener of carcass; F = female, M = male, ? = unknown
Lat	Latitude
Long	Longitude
DSK	Days since kill; in days
Soil.N	Soil N concentration (%)
Soil.C	Soil C concentration (%)
Veg.C	Vegetation C concentration (%); dominant grasses sampled
Veg.N	Vegetation N concentration (%); dominant grasses sampled
Veg.CtoN	Vegetation C to N ratio; dominant grasses sampled
Soil.resp	Soil respiration (g m-2 h-1)
soil.temp	Soil temperature (°C)
Soil.moist	Soil moisture (volumentric; %)
CRR	Cotton rotting rate
bacteria.richness	Bacteria richness
bacteria.diversity	Bacteria diverstiy
fungi.richness	Fungi richness
fungi.diversity	Fungi diversity
bacteria.abundance	Bacteria abundance
fungi.abundance	Fungi abundance

format: XLS

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

Paper Citation:

Risch, A.C., Frossard, A., Schütz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology
doi: ...

Methods
Study area and study sites
This study was conducted in YNP’s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163° N, 110.4169° W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995.
For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951).
The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter “days since kill”, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP’s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter “control”) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter “carcass”). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009).

Ecosystem functions and soil properties
We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 ºC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5 g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3–V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA).
Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length >300 bp for 16S and >200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively.
Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, Lüscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).
The other half of the soil sample was bagged in paper, dried to constant weight at 60°C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60°C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 – 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s.
In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18’’, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20°C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (CTScontrol - CTSfinal/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties.

Statistical analyses
Univariate analyses for ecosystem functions, soil biotic and abiotic properties
We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (“Location”), bison and elk (“Species”) and days since kill (“DSK”). For this purpose, we used linear mixed effect models (LMM, “nlme” package v 3.1 – 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the “lsmeans” package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk.
We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR < 0 indicates higher value at control compared to carcass, LRR > 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran’s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the “moran.test” function in the “spdep” package version 1.1-3 (Bivand et al., 2019).
Multivariate analyses
Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray–Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (“vegan” package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (“nlme” package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the “lsmeans” package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the “envfit” function in the “vegan” package (Oksanen et al., 2019).
Indicator species analyses were performed using the multipatt function implemented in the “indicspecies” package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De Cáceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p < 0.05, correlation coefficient > 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017).

References
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R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Zurich, Switzerland.
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., & Frey, B. (2015). Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Molecular Ecology, 24(5), 1091–1108. doi:10.1111/mec.13051
Risch, A. C., Jurgensen, M. F., & Frank, D. A. (2007). Effects of grazing and soil micro-climate on decomposition rates in a spatio-temporally heterogeneous grassland. Plant and Soil, 298(1–2), 191–201. doi:10.1007/s11104-007-9354-x
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., … Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–7541. doi:10.1128/AEM.01541-09
Smith, D., Stahler, D., Cassidy, K., Stahler, E., Metz, M., Cassidy, B., … Cato, E. (2018). Yellowstone National Park wolf project annual report 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center of Resources.
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. doi:10.1128/AEM.00062-07
Withington, C., & Sanford Jr., R. (2007). Decomposition rates of buried substances increase with altitude in a forest-alpine tundra ecotone. Soil Biology and Biochemistry, (39), 68–75.

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

bacteria

display_name: bacteria

biogeochemical-cycling

display_name: biogeochemical-cycling

carcass

display_name: carcass

carrion

display_name: carrion

decomposition

display_name: decomposition

detritus

display_name: detritus

fungi

display_name: fungi

soil-biodiversity

display_name: soil-biodiversity

wolf

display_name: wolf

yellowstone

display_name: yellowstone

en: Elk and bison carcasses in Yellowstone, USA


radiosondes

maintainer: Jörg Wieder

title_for_slug: radiosondes

en: temperature relative-humidity wind pressure holoballoon wind-direction raclets radiosonde

en: Radiosond Template Files

en:
The "template" files contain standard parameters, i.e. ambient pressure (mbar and Pa), temperature (°C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), as well as additional information, e.g. time (UTC and local time), dew point (°C), mixing ration (g/kg), density (kg m³), altitude (m MSL), height above ground (ft and m AGL), and geographic coordinates (longitude, latitude). If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

mimetype: application/zip

en: Radiosond Sounding Files

en:
The "soundings" radiosond data contains information on ambient pressure (mbar), temperature (C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), and are ordered with respect to the height above ground (m AGL) with a step size of 20 m. The first line of each file idicates the geographic location and the start time (in UTC) of the ascent. If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

wind

display_name: wind

wind-direction

display_name: wind-direction

educ

en: Education, culture and sport

en: Radiosondes


en: plot lwf meteorological-data infrastructure swiss-forest-lab forest meteorological-measurements

en: Othmarsingen Meteorological Data LWF

en: LWF plot Othmarsingen. Photo: Christian Hug, 2017

en:
![alt text](https://www.envidat.ch/dataset/ebb37e73-1280-4a06-81d8-e18bc5d9c1cf/resource/64291bfd-37d4-4f88-b505-81fc85a109c2/download/lwf_othmarsingen.jpg "LWF Othmarsingen")

LWF Plot Othmarsingen

- Community: Othmarsingen / canton AG
- Date of installation: 9 September 1994
- Size of the plot: 1 ha
- Altitude (m): 467-500
- Mean slope: 27%
- Soil types (WSL): Stagnic Luvisols, Haplic Luvisols
- Woodland association after EK72: 7: Galio odorati-Fagetum typicum
- Main tree species: Fagus sylvatica
- Management system: former coppices w. standards
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 62.8 cm
- Number of trees BHD >= 12 cm (2011): 167
- Maximum tree age: Fagus sylvatica 120-140 yr - Tilia sp. 120-140 yr

More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/othmarsingen.html

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

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: LWF Othmarsingen long-term research site


disdrometer-data-gotschnagrat

title_for_slug: disdrometer-data-gotschnagrat

en: disdrometer raclets hydrometeors visibility precipitation

en: Disdrometer Data Gotschnagrat

en: The zip folder contains one netCDF file per day for Gotschnagrat from February 11 to March 27 2019 with a temporal resolution of 30 seconds (time in UTC). The naming convention for the files is Gotschna_YYYYMMDD.nc and the files include the following: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

en: A laser optical disdrometer (Parsivel² , OTT Hydromet) was deployed at Gotschnagrat (LON: 9.849, LAT: 46.859) to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms. The dataset contains information on precipitation amount and type for the period of February 11 to March 27 2019 at Gotschnagrat.

disdrometer

display_name: disdrometer

hydrometeors

display_name: hydrometeors

precipitation

display_name: precipitation

visibility

display_name: visibility


en: microstructure compression-experiments x-ray-tomography snow-mechanics snow

en: Snowmicroquakes Data

en: Compression experiments, X-ray images and simulation data with supplemented README file

en: README

media_type: text/plain

en: README file explaining the organization of the data

format: TXT

en:
When snow is compressed with a certain speed, micro-snowquakes are triggered in the porous structure of bonded crystals. The present dataset covers uniaxial compression experiments of snow at different strain rates and concurrent X-ray tomography imaging documenting this feature.

The experiments were conducted in a micro-compression stage operated in the X-ray tomography scanner in the SLF cold laboratory. The dataset comprises the compression force data of 17 compression experiments, the 3D image data from 4 X-ray tomography scans and the results of numerical simulations.

compression-experiments

display_name: compression-experiments

microstructure

display_name: microstructure

snow

display_name: snow

snow-mechanics

display_name: snow-mechanics

x-ray-tomography

display_name: x-ray-tomography

en: Compressive stick slip and snow-micro-quakes


disdrometer-data-davos-wolfgang

title_for_slug: disdrometer-data-davos-wolfgang

en: precipitation disdrometer davos fall-velocity raclets

en: Disdrometer Data Davos Wolfgang

en:
The zip folder contains one netCDF file per day for Davos Wolfgang from February 8 to March 19 2019 with a temporal resolution of 30 seconds (time in UTC) and includes:

- characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...)
- precipitation rate
- radar reflectivity
- visibility range
- weather codes
- instrument performance

en: The dataset contains information on precipitation amount and type for Davos Wolfgang (LON: 9.853594, LAT: 46.835577) from February 8 to March 19 2019. It includes: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

davos

display_name: davos

fall-velocity

display_name: fall-velocity

raclets

display_name: raclets

educ

en: Education, culture and sport


en: alptal groundwater streamflow runoff stable-water-isotopes

en: Deuterium groundwater and baseflow October 2017

media_type: text/csv

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)

format: CSV

mimetype: text/csv

en: Precipitation and discharge event C

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)

en: Deuterium streamwater 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 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 streamwater event A

en: Deuterium rainfall 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 (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: Deuterium rainfall event B

en: Deuterium streamwater extra event

en: Deuterium rainfall event A

en: Precipitation and discharge event B

en: Precipitation and discharge event A

en: Deuterium rainfall event C

en: Deuterium groundwater and baseflow October 2016

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.

envidat

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

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


en: pollen soil-matric-potential heat forest drought beech mast litterfall fruit-abortion

en: LWF beech litterfall and pollen, and environmental parameters

en: Parameters for the LWF beech plots Bettlachstock, Lausanne and Schänis. Fruit biomass, leaf biomass and pollen are present as percentage of highest annual value measured per plot. Environmental parameters precipitation sums, mean temperatures and soil matric potentials in four soil depths are present as deviations from longterm mean for the seasons spring (April and May) and summer (June and July).

en:
This dataset contains the parameters used in the statistical analyses for the manuscript SREP-19-40170-T, submitted in Scientific Reports. This study is part of the WSL Drought Initiative 2018 (C3 - Analysis of the beech litterfall of the drought year 2018).

Data originate from the Long-term Forest Ecosystem Research Programme LWF (litterfall, soil matric potential, deposition (precipitation) and meteo (temperature)), and from the Swiss Federal Office of Meteorology and Climatology MeteoSwiss (pollen).

Datafile:

 _LWF_beech_plots_litterfall_pollen.xlsx_

1. Sheet _extreme_weather_: values used for analysis of weather conditions in strongest mast years compared to years with fruit abortion.

2. Sheet _weather_and_resource_allocation_: values used for analysis of weather impacts on mast occurrence and resource allocation models.

beech

display_name: beech

drought

display_name: drought

forest

display_name: forest

fruit-abortion

display_name: fruit-abortion

heat

display_name: heat

litterfall

display_name: litterfall

mast

display_name: mast

pollen

display_name: pollen

soil-matric-potential

display_name: soil-matric-potential

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Litterfall and pollen data of three LWF beech plots


soil-matrix-chemistry-lwf

title_for_slug: soil-matrix-chemistry-lwf

en: long-term-forest-ecosystem-research soil-matrix soil-chemistry icp-forests swiss-forest-lab

en: Data Request

en:
Assessment of several chemical soil parameters at the beginning of the monitoring campaign. Most parameters were determined accordung to the manual of ICP-Integrated-Monitoring. The parameters were analysed for all genetic horizons in soil pits and additionally for fixed layers in the Intensive-Monitoring-Plots. In heterogeneous LWF-plots, several soil pits were analysed in order to assess the soil variability of the plot. The manual (in German) for soil sampling and soil analyses is available in www: http://e-collection.ethbib.ethz.ch/view/eth:25622?q=walthert. The results and data of the first soil survey are available (in German) in www: http://e-collection.ethbib.ethz.ch/view/eth:26275?q=walthert.

 Purpose:

The chemical characterisation of soil matrix down to the paraent material is realised with data from soil pits. The monitoring of the soil matrix in a frequency of roundly 15 years is effected with soil samples from Intensiv-Monitoring-Plots. For soil monitoring, pooled samples with 16 replicats are used down to a depth of 80 cm. The date of the second soil survey is not yet fixed.

 Manual Citation:

 * Walthert L, Lüscher P, Luster J, Peter B (2002) Langfristige Waldökosystem-Forschung LWF. Kernprojekt Bodenmatrix. Aufnahmeanleitung zur ersten Erhebung 1994-1999. ETHZ Zürich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 269: 56 p. [10.3929/ethz-a-004375470](https://doi.org/10.3929/ethz-a-004375470)

 Paper Citation:

 * Walthert L, Blaser P, Lüscher P, Luster J, Zimmermann S (2003) Langfristige Waldökosystem-Forschung LWF. Kernprojekt Bodenmatrix. Ergebnisse der ersten Erhebung 1994-1999. ETHZ Zürich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 276: 340 p.

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

soil-chemistry

display_name: soil-chemistry

soil-matrix

display_name: soil-matrix

swiss-forest-lab

display_name: swiss-forest-lab

en: Soil matrix chemistry LWF


maintainer: Franziska Gerber

en: wrf static-data cosmo-wrf topography cryowrf landuse

en: Example namelist.wps to use with the 1s static data

en: Static data for WRF - Landuse

en: Static data for WRF - Soiltype bottom

en: Static data for WRF - Soiltype top

en: Documentation

en: Documentation of the dataset

en:
Example GEOGRID.TBL to use with the 1s static data;
Note about naming:
* 121 -> 1-2-1 topography smoothing applied to dataset
* 1 -> one smoothing cycle applied to the data (needs to be changed depending on the chosen resolution to avoid too steep slope angles)

en: Static data for WRF - Topography

en: Static input data (topography, landuse and soiltype) for the WRF preprocessing system WPS is provided for Switzerland and its neighboring countries between 45-49 N and 4-12 E. The data is provided at a resolution of 1 s. Topography is based on the Aster dataset, while landuse is extracted from the Corine landuse dataset. Soil type is set to silty clay loam for the entire domain. This static input data is valid for WRF and CRYOWRF.

cosmo-wrf

display_name: cosmo-wrf

cryowrf

display_name: cryowrf

landuse

display_name: landuse

static-data

display_name: static-data

topography

display_name: topography

wrf

display_name: wrf

en: High resolution static data for WRF over Switzerland


net-increment

title_for_slug: net-increment

en:
Increment including ingrowth minus the mortality.  The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

avalanches-silvaprotect

display_name: avalanches-silvaprotect

biogeographical-region

display_name: biogeographical-region

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

net-increment

display_name: net-increment

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

rockfall-silvaprotect

display_name: rockfall-silvaprotect

site-quality

display_name: site-quality

stage-of-stand-development

display_name: stage-of-stand-development

tree-species-woody-species

display_name: tree-species-woody-species

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: Net increment


en: acoustic-emissions sintering viscous-deformation snow-failure snow fiber-bundle-model snow-mechanics snow-avalanche

en: fiber-bundle-model-for-snow-failure

en: Data underlying Figures in publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted to "Geophysical Research Letters"

en:
This dataset contains modeled and experimental results for laboratory snow failure experiments and the concurrent acoustic emissions signatures for different loading rates. For modelling the snow failure we used a fiber bundle model that includes sintering and viscous deformation.

The data underlay the figures in the publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted for publication to "Geophysical Research Letters".

acoustic-emissions

display_name: acoustic-emissions

fiber-bundle-model

display_name: fiber-bundle-model

sintering

display_name: sintering

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-mechanics

display_name: snow-mechanics

viscous-deformation

display_name: viscous-deformation

en: Fiber Bundle Model for snow failure and concurrent Acoustic Emissions


en: species-richness-models insects urbanization birds arthropods sdms snails urban-ecology spiders species-abundance-distributions diversity

en: contains the raw data to perform species distribution models, only for common species

en: abundance and occurrence data of the 1446 species studied, used to investigate species-abundance distributions and species-occurrence distributions

en: contains the raw data to perform the species richness models of the hypercommon species

en: contains the raw data to perform the species richness models of the rare species

en: contains the raw data to perform the species richness models of all species

en:
Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.
Data are provided on request to contact person against bilateral agreement.

arthropods

display_name: arthropods

birds

display_name: birds

diversity

display_name: diversity

insects

display_name: insects

sdms

display_name: sdms

snails

display_name: snails

species-abundance-distributions

display_name: species-abundance-distributions

species-richness-models

display_name: species-richness-models

spiders

display_name: spiders

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

en: Ecological properties of urban ecosystems. Biodiversity dataset of Zurich


en: spongy-moth gypsy-moth nitrogen flight-traps larch-budmoth phenol nun-moth

en: Moth monitoring

en: See ReadMe

en: The population dynamics of eruptive moths were monitored with pheromone traps and the composition of the larvae's foodplants were analyzed for water content, nitrogen and total phenolics. Moth catches cover a period of 20 years, leaf analyses 10 years. For Zeiraphera griseana (= Z. diniana) only needle analyses are available. The corresponding data on the moth population dynamics are property of A. Fischlin, ETH Zürich, and will be made available on EnviDat as well.

flight-traps

display_name: flight-traps

gypsy-moth

display_name: gypsy-moth

larch-budmoth

display_name: larch-budmoth

nitrogen

display_name: nitrogen

nun-moth

display_name: nun-moth

phenol

display_name: phenol

spongy-moth

display_name: spongy-moth

educ

en: Education, culture and sport

en: Monitoring of Lymantria dispar, Lymantria monacha and Zeiraphera griseana and host food quality in the Swiss Alps


en: forest-inventory natural-forest-dynamics fagus-sylvatica sihlwald forest-reserves

en: Standing dead trees 2003 - 2017

en: Contains data on standing dead trees with a DBH >= 8 cm.

en: Regeneration 2017

en: Data on regeneration inventory 2017 (living trees between 10 cm height and DBH < 7 cm)

en: Data Description

media_type: application/pdf

en: Describes the available tables

format: PDF

en: Standing living trees 1981 - 2017

en: Contains data on standing, living trees with a DBH >= 8 cm.

en: Standing dead trees 2017

en: Contains data on standing dead trees with a DBH >= 7 cm.

en: Lying deadwood 2017

en: Contains data on lying deadwood assessed on 3 transects.

en: Standing living trees 2017

en: Contains data on standing, living trees with a DBH >= 7 cm.

en:
 Supplementary Data Sample Plot Inventory Sihlwald

The Sihlwald is one of the largest contiguous beech forests in the Swiss Plateau region. In the year 2000, timber harvesting was abandoned. Since 2007 the forest has been under strict protection as a natural forest reserve on an area of 1098 ha and since 2008 as a cantonal nature and landscape conservation area (SVO Sihlwald). Since 2010, it carries the national label ‘Nature discovery park’ (‘Naturerlebnispark’). As part of the national monitoring in nature forest reserves, a sampling inventory (calipering threshold of 7 cm) with 226 plots on an area of 917 ha was carried out in the Sihlwald in autumn and early winter 2017. The aim was to describe the state and development of the forest structure and make comparisons with earlier sampling inventories in the same area from 1981, 1989 and 2003.
This dataset contains supplementary tables for the publication by Brändli et al. (2020). The metadata file describes the structure of the tables.

fagus-sylvatica

display_name: fagus-sylvatica

forest-inventory

display_name: forest-inventory

forest-reserves

display_name: forest-reserves

natural-forest-dynamics

display_name: natural-forest-dynamics

sihlwald

display_name: sihlwald

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.


maintainer: Frank Techel

en: rutschblock-test extended-column-test switzerland norway avalanche-danger avalanche-observations

en: avalanche occurrence data (Norway, Sitzerland) - largest avalanche size per day and warning region

en: ECT data (Norway, Switzerland)

en: Rutschblock data (Switzerland)

en: avalanche occurrence data (Norway, Sitzerland) - avalanche sizes per day and warning region

en:
Observational data used to quantitatively describe the key elements describing avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size.
The data set consists of
- Rutschblock test results (Switzerland)
- Extended Column Test results (Switzerland, Norway)
- Avalanche occurrence data (Switzerland, Norway).
The data were extracted from the respective operational databases of the national avalanche warning services in Switzerland (WSL Institute for Snow and Avalanche Research SLF Davos, Switzerland) and Norway (The Norwegian Water Resources and Energy Directorate NVE).
For further information regarding the data, please refer to the publication or contact the author.

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland

educ

en: Education, culture and sport

en: Stability tests, avalanche observations, Switzerland, Norway


maintainer: Adam Winstral

en: snow jules-investigation-model snow-simulation snow-depth jim

en: Station forcing data

en: All forcing data need to run JIM at HS and SWE stations. These are data downscaled directly from COSMO NWP data (WY14, WY15) and further enhanced by additional station observations of snow depth (HS) and air temperature (Ta) in WY17. Further information can be found in README file. Note that in WY14 and WY15, shortwave radiation was not split into direct and diffuse (not necessary for JIM) and that the default method in JIM for splitting precipitation phase was used. In WY 17, direct and diffuse were combined as input to JIM and JIM was forced with pre-processed liquid and solid precipitation phases.

en: HS and SWE observations

en: Automated (HS) and manual (SWE) data used to evaluate model simulations

en: DEM

en: 1-km resolution DEM used for downscaling gridded NWP data to stations and for distributing BDE evaluations at HS-stations to SWE-stations

mimetype: text/plain

en: File/data descriptor

en: These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the "deterministic" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations.

jim

display_name: jim

jules-investigation-model

display_name: jules-investigation-model

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-simulation

display_name: snow-simulation

en: Meteorological data used to develop and validate the bias-detecting ensemble (BDE)


en: weissfluhjoch water-front snowmicropen snow-stratigraphy snow-temperature ice-layers

en: WFJ_ICE_LAYERS dataset

en: README

en: The WFJ_ICE_LAYERS dataset contains multi-instrument snowpack measurements at high temporal resolution, which enable to monitor the formation of deep ice layers due to preferential water flow, at the Weissfluhjoch research site, Davos, Switzerland. It covers the winter 2016/2017, with a focus on the early melting season. This dataset includes traditional snowpack profiles (weekly resolution, 15/11/2016-29/05/2017), SnowMicroPen penetration resistance profiles (daily resolution, 01/02/2017-19/04/2017), snow temperatures measured at different heights in the snowpack (half-hourly resolution, 01/03/2017-15/04/2017) and the water front height derived from an upward-looking ground penetrating radar (3-hour resolution, 04/03/2017-08/04/2017). The measurements are complemented by initialization files for SNOWPACK model simulations with the ice reservoir parameterization at Weissfluhjoch for the winter 2016/2017.

ice-layers

display_name: ice-layers

snow-stratigraphy

display_name: snow-stratigraphy

snow-temperature

display_name: snow-temperature

snowmicropen

display_name: snowmicropen

water-front

display_name: water-front

weissfluhjoch

display_name: weissfluhjoch

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: WFJ_ICE_LAYERS: Multi-instrument data for monitoring deep ice layer formation in an alpine snowpack


en: deadwood icp-forests coarse-woody-debris long-term-forest-ecosystem-research swiss-forest-lab

en: Data Request

en:
Assessment of coarse woody debris on LWF plots using the line intersect method

 Purpose:

Assessing the amount of coarse woody debris

 Manual Citation:

 * Matthias Dobbertin, Nathalie Bretz Guby, 1997: Totholz. In: Peter Brang (ed.) LWF Aufnahmeanleitung. Langfristige Waldökosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 5 S.

 Paper Citation:

 * Bretz Guby, N.A., Dobbertin, M., 1996. Quantitative estimates of coarse woody debris and standing dead trees in selected Swiss forests. Glob. Ecol. Biogeogr. Lett. 5, 327-341.

coarse-woody-debris

display_name: coarse-woody-debris

deadwood

display_name: deadwood

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

swiss-forest-lab

display_name: swiss-forest-lab

educ

en: Education, culture and sport

en: Deadwood survey LWF 1995 - line intersect


biomass-of-lying-dead-wood-lis

title_for_slug: biomass-of-lying-dead-wood-lis

en:
Dry weight (mass) of lying deadwood starting at 7 cm in diameter that does not fulfil the criteria for a tally tree (measurement location of dbh not identifiable or the dbh is less than 12cm).

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

biogeographical-region

display_name: biogeographical-region

biomass-of-lying-dead-wood-lis

display_name: biomass-of-lying-dead-wood-lis

canton

display_name: canton

economic-region

display_name: economic-region

ownership

display_name: ownership

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

en: Biomass of lying dead wood (LIS)


maintainer: Mathias Bavay

en: air-pressure relative-humidity wind-direction stillberg wind-speed soil-temperature snow-depth air-temperature treeline dew-point-temperature shortwave-radiation precipitation-sum

en: The Davos Stillberg meteorological data (Stillberg_meteo_v1.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological research site. Metadata are provided in the folder 'Stillberg_meteo_metadata' and the data in the folder 'Stillberg_meteo_data' with subfolders for the three quality levels 'raw_standardized', ‘raw_edited’, and ‘filtered’.

en: The Davos Stillberg meteorological data (Stillberg_meteo_v1.1.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological research site. Metadata are provided in the folder 'Stillberg_meteo_metadata' and the data in the folder 'Stillberg_meteo_data' with subfolders for the three quality levels 'raw_standardized', ‘raw_edited’, and ‘filtered’.

en:
 Background information
The Stillberg ecological treeline research site is located in the transition zone between the relatively humid climate of the Northern Alps and the continental climate of the Central Alps. In 1975, 92,000 seedlings of the high-elevation conifer species *Larix decidua* Mill. (European larch), *Pinus cembra* L. (Cembran pine), and *Pinus mugo* ssp. *uncinata* (DC.) Domin (mountain pine) were systematically planted across an area of 5 hectares along an elevation gradient of about 150 metres, with the aim to develop ecologically, technically, and economically sustainable afforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Alongside the ecological long-term monitoring of the afforestation, several meteorological stations have recorded local meteorological conditions at the Stillberg research site. Here, we provide the Davos Stillberg meteorological timeseries of five stations from 1975 (01-01-1975), the year of the afforestation establishment, until the end of the year 2022 (31-12-2022).

 Station description
The five meteorological stations were all installed at the same location (46°46′25.015″N 9°52′01.792″E) at 2090 m a.s.l., in the lower part of the afforestation area. In general, the five stations were operated sequentially (Stillberg_meteo_metadata_stations_v1.csv). However, there are some overlapping time periods when more than one station was operated in parallel. The stations have recorded environmental parameters, such as air and soil temperature, dew point temperature, air pressure, relative humidity, wind direction and velocity, radiation, precipitation, and snow depth (Stillberg_meteo_metadata_parameters_v1.csv). The meteorological measurements were recorded hourly from 1975 until 1996 and have been recorded in 10-minute intervals since 1997.

 Data description
We processed the Davos Stillberg meteorological timeseries with the MeteoIO meteorological data pre-processing library (Bavay & Egger, 2014). Data files are provided for each station and quality level separately and named according to the station (see ‘Stillberg_meteo_metadata_stations_v1.csv’). From the raw data in their original formats, we generated three data quality levels: raw standardized (folder ‘raw_standardized’), edited (folder ‘raw_edited’) and filtered (folder ‘filtered’). The processing level is indicated in the headers of the data files. The whole processing protocol is described in a set of human-readable configuration files that are used by MeteoIO to generate the required data quality levels. This improves long-term reproducibility (Bavay et al., 2022), as the data could be regenerated in the future, even using a completely different software, to account for additional data points or to introduce new data corrections. The first quality level (raw standardized) is generated by parsing the original data files and interpreting them in order to convert all data points to a common format and meteorological parameter naming scheme, while excluding unreadable or duplicated data lines. The generated data files are derivatives of CSV files, with a standardised header that contains the metadata that are necessary to interpret and use the data (use metadata) and to populate a data index (search metadata). The latter is a textual implementation of the Attribute Convention for Data Discovery (ACDD) metadata standard (Attribute Convention for Data Discovery 1-3, 2022). The second quality level (edited) builds on the raw data by performing low-level data editing, such as removing some data periods that are known to be unusable (often based on maintenance records or anecdotal evidence) or applying undocumented calibration factors (for example, when there seems to be an obvious offset on a measured parameter for a period between two documented maintenance operations). The third quality level is generated by applying statistical filters on the data (per station and per meteorological parameter) to exclude presumably wrong values. We did not perform gap filling, as no single strategy could be relied upon that would work best for all possible data usage scenarios.

air-pressure

display_name: air-pressure

air-temperature

display_name: air-temperature

dew-point-temperature

display_name: dew-point-temperature

precipitation-sum

display_name: precipitation-sum

relative-humidity

display_name: relative-humidity

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

soil-temperature

display_name: soil-temperature

stillberg

display_name: stillberg

treeline

display_name: treeline

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

en: Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.


en: tree-ring-width tree-selection-effects swiss-forest-lab dendrochronology long-term-forest-ecosystem-research icp-forests

en:
Two stem core samples of all trees of a subplot of approximately 1 a. Basal Area, Wood volume increment per area estimation

 Purpose:

Investigation of the relation between dendroparameters of dominance/surpression and stem growth. Abgleich Jahrringdaten mit Inventurdaten.

 Paper Citation:

 * Nehrbass-Ahles C, Babst F, Klesse S, Nötzli M, Bouriaud O, Neukom R, Dobbertin M, Frank D (2014) The influence of sampling design on tree-ring-based quantification of forest growth. Global Change Biology, 20 (9): 2867–2885. [doi: 10.1111/gcb.12599](http://doi.org/10.1111/gcb.12599)

 * Klesse S, Etzold S, Frank D (2016) Integrating tree-ring and inventory-based measurements of aboveground biomass growth: research opportunities and carbon cycle consequences from a large snow breakage event in the Swiss Alps. European Journal of Forest Research, 135 (2): 297-311.

dendrochronology

display_name: dendrochronology

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

swiss-forest-lab

display_name: swiss-forest-lab

tree-ring-width

display_name: tree-ring-width

tree-selection-effects

display_name: tree-selection-effects

en: Dendrochronological analyses of tree core samples (subplot) LWF


en: mogli lfi sdm switzerland woody-species

en: Map Access

en: Maps are available as georeferenced rasters on request via login or directly via [Andri Baltensweiler](https://www.wsl.ch/en/employees/baltensw.html).

en: Tabelle S3

en:
Tabelle S3 zum Artikel: Wüest Karpati et al. (2021) Modellierte Verbreitungskarten der häufigsten Gehölzarten der Schweiz, _Schweizerische Zeitschrift für Forstwesen_ 172:4

Liste der verwendeten Umweltprädiktoren für jede der Arten mit erfülltem Qualitätskriterium (TSS > 0.5).

en: Tabelle S2

en:
Tabelle S2 zum Artikel: Wüest Karpati et al. (2021) Modellierte Verbreitungskarten der häufigsten Gehölzarten der Schweiz, _Schweizerische Zeitschrift für Forstwesen_ 172:4

Übersicht zu den verwendeten Umweltprädiktoren für diejenigen Arten, welche das Qualitätskriterium zur Publikation nicht erreichten (TSS < 0.5)

en: Tabelle S1

en:
Tabelle S1 zum Artikel: Wüest Karpati et al. (2021) Modellierte Verbreitungskarten der häufigsten Gehölzarten der Schweiz, _Schweizerische Zeitschrift für Forstwesen_ 172:4

Übersicht zu den verwendeten Umweltprädiktoren für alle Arten mit erfülltem Qualitätskriterium (TSS > 0.5).

en: MoGLI Summary

en: A summary of methods and results on the potential distribution maps of woody species in Swiss forests.

en:
**We used Swiss National Forest Inventory ([NFI](https://www.lfi.ch/index-en.php)) data to model the potential distribution of the most common woody species for the forested area of Switzerland and provide potential distribution maps that fulfill specific quality criteria with regard to predicting performance.**

More details on the methods and results are described in the project summary available [here](https://www.envidat.ch/dataset/07a9c22c-9ec2-4f49-87e2-3b2d73ad81f2/resource/9bfd5308-d9be-4d01-ab78-48d33889e04e/download/mogli_summary.pdf).

**The resulting maps can be viewed in a simple web-GIS application available at:**
[https://www.lfi.ch/produkte/mogli/mogli-en.php](https://www.lfi.ch/produkte/mogli/mogli-en.php)

**Data can be used without restrictions, but the data must be explicitly asked from the contact person of the dataset in order to obtain access.**
This is a requirement to fulfill the needs of reporting towards the funding agencies.

lfi

display_name: lfi

mogli

display_name: mogli

sdm

display_name: sdm

switzerland

display_name: switzerland

woody-species

display_name: woody-species

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: Distribution maps of common woody species for Swiss forests


planning-intentions-lyon

title_for_slug: planning-intentions-lyon

en: lyon plan-evaluation land-use-planning strategic-planning land-change

en: The present dataset is the summary of each planning intention (name, type, development pattern, land use, and link with governance and supra regionel conditions), the explanation of interest, and the mapping from plan.

land-change

display_name: land-change

land-use-planning

display_name: land-use-planning

lyon

display_name: lyon

plan-evaluation

display_name: plan-evaluation

strategic-planning

display_name: strategic-planning

educ

en: Education, culture and sport

en: Planning intentions Lyon


maintainer: Leo Gallus Bont

en: forest seilaplan openstreetmap qgis geoprocessing accessible-forest

en: Digitised sample perimeter

en: Power lines vector data (close to the area of the sample forest range)

en: Juypter Notebook for Data Preparation (online)

en: Forest vector data (for sample forest range)

en: Railways vector data (close to the sample forest range)

en: Preview

media_type: image/png

format: PNG

mimetype: image/png

en: DEM (for the area of the sample forest range)

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: Juypter Notebook for Data Preparation

en:
This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps).
The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting.
The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data:
Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

- Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
- Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan

en: Sample Geodata and Software for Demonstrating Geospatial Preprocessing for Forest Accessibility and Wood Harvesting at FOSS4G2019


en: debris-flow illgraben magnitude monitoring-station force-plate volume

en: Illgraben debris-flow bulk volumes from 2000 to 2017.

en:
Debris-flow bulk volumes from the WSL monitoring station.
More information can be found in McArdell et al. (2007) and Schlunegger et al. (2009).

debris-flow

display_name: debris-flow

force-plate

display_name: force-plate

illgraben

display_name: illgraben

magnitude

display_name: magnitude

monitoring-station

display_name: monitoring-station

volume

display_name: volume

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: Debris-flow volumes at the Illgraben 2000-2017


en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were used, died or disappeared between two inventories. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

increment-star

display_name: increment-star

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

ownership

display_name: ownership

yield-and-mortality-star

display_name: yield-and-mortality-star

educ

en: Education, culture and sport

en: Yield and mortality*


en: random-forest landslide rainfall-thresholds python debris-flow illgraben

en: Threshold code and example data

en: .zip file with code and example data. See the README.md for how to use it.

en:
This is the source code to compute rainfall thresholds for debris flows or landslides following Hirschberg et al. (2021).

 How to install and run the example

Pyhton has to be installed to run the codes. To make sure it works correctly, it is easiest to install Anaconda and create an environment with the right packages from the yml-file. To this end, in a command-line interpreter, change the working directory to where you saved this project and run the following:

`$ conda env create -f environment.yml`

`$ conda activate thresholds` or `$ source activate thresholds`

To run an example:

`$ python run_example()`

It will save a dat-file and a figure as Fig. 7 in Hirschberg et al. (2021), where more information can be found.

 License
GNU General Public License v3.0

landslide

display_name: landslide

python

display_name: python

rainfall-thresholds

display_name: rainfall-thresholds

random-forest

display_name: random-forest

en: Source code for: Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment


carabid-beetles-in-forests

title_for_slug: carabid-beetles-in-forests

en: running-beetles carabidae insects forest trapping arthropoda coleoptera

en: Tab-delimited data of the according sql query.

en: Data query used on Fauna Database to retrieve the submitted data.

en: Data extract in TAB-delimted format. First row is header, first column is counter.

en: SQL query used to extract the data from fauna database.

en:
Carabidae data from all historic up to the recent projects (21.10.2019) of WSL, collected with various methods in forests of different types.

Version 2 ('FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae') contains additional data field PROJ_FALLENBEZEICHNUNG.

Data are provided on request to contact person against bilateral agreement.

arthropoda

display_name: arthropoda

carabidae

display_name: carabidae

coleoptera

display_name: coleoptera

forest

display_name: forest

insects

display_name: insects

running-beetles

display_name: running-beetles

trapping

display_name: trapping

en: Carabid beetles in forests


diversity-of-woody-species

title_for_slug: diversity-of-woody-species

en:
Index based on the number of tree and shrub species starting at 12 cm dbh in the upper layer and the occurrence of especially ecologically valuable tree and shrub species starting at 12 cm dbh in the upper layer.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

diversity-of-woody-species

display_name: diversity-of-woody-species

economic-region

display_name: economic-region

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

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: Diversity of woody species


tdr-pfynwald

title_for_slug: tdr-pfynwald

en: long-term-forest-ecosystem-research irrigation swiss-forest-lab icp-forests soil-water-content

en: Data Request

en:
Continuous measurement of soil water content at one control and

in one irrigated plot in 10, 40 and 60 cm depth (4 replications) with TDR (Tektronix

1502B cable tester, Beaverton, OR, US).

 Purpose:

Monitoring of the soil water content

 Paper Citation:

 * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123)

icp-forests

display_name: icp-forests

irrigation

display_name: irrigation

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

soil-water-content

display_name: soil-water-content

swiss-forest-lab

display_name: swiss-forest-lab

educ

en: Education, culture and sport

en: TDR Pfynwald


en: geomorphology awe-gen modelling sediment-transport illgraben debris-flow sedcas

en: SedCas Input/Output Data

en:
The dataset contains the input and output files from the publication by Hirschberg et al. (2022).

The input files are the climate forcing time series generated with the AWE-GEN model. The output files include the hydrological outputs, which is the same for scenarios 1-6 considered in Hirschberg et al. (2022), and the sediment-related outputs, whereas the transport-limited scenario 6 is included in the output of scenario 1.

The input file includes:

- time _D_ (h)
- precipitation _Pr_ (mm/h)
- atmospheric temperature _Ta_ (°C)
- incoming shortwave radiation _Rsw_ (W/m^2)
- cloudiness _N_ (-)

The output files include:

- hydrological outputs (accroding to time in input and normalized by basin area)
  - total discharge _Q_ (mm/h)
  - surface discharge _Qs_ (mm/h)
  - subsurface discharge _Qss_ (mm/h)
  - soil water storage _Vw_ (mm)
  - snow depth _snow_ (mm SWE)
  - snow depth change _snowacc_ (mm/h SWE)
  - potential evapotranspiration _PET_ (mm/h)
  - actual evapotranspiration _AET_ (mm/h)

- sediment outputs (accroding to time in input and normalized by basin area)
  - hillslope landslide magnitude _ls_ (mm/h)
  - channel sediment storage _sc_ (mm)
  - hillslope sediment storage _sh_ (mm)
  - total sediment discharge _so_ (mm/h)
  - transport-limited total sediment discharge _sopot_ (mm/h)
  - sediment discharge by debris flows _dfs_ (mm/h)
  - transport-limited sediment discharge by debris flows _dfspot_

awe-gen

display_name: awe-gen

debris-flow

display_name: debris-flow

geomorphology

display_name: geomorphology

illgraben

display_name: illgraben

modelling

display_name: modelling

sedcas

display_name: sedcas

sediment-transport

display_name: sediment-transport

en: Data for Numerical Investigation of Sediment Yield Underestimation in Supply-Limited Mountain Basins with Short Records


en: landscape urban-ecology place-attachment electrodermal-activity sense-of-place

en: 01. EDA Processed Data

en: 00. EDA Raw Data

en: 02. Questionnaire Data

en: 03. Publication Ready Data

en: 04. Scripts

en: 05. Visuals

en: This repository contains data on EDA measurements of visitors with different cultural backgrounds in virtual urban park settings. The parks are a Persian garden (Shiraz, Iran) and a historical park in Zurich, Switzerland. The cultural background of the visitors is Persian and Central European. The repository contains raw data from EDA, processed time series and statistical procedures.

electrodermal-activity

display_name: electrodermal-activity

landscape

display_name: landscape

place-attachment

display_name: place-attachment

sense-of-place

display_name: sense-of-place

urban-ecology

display_name: urban-ecology

en: Electrodermal Activity (EDA) of Bi-cultural Visitors In Virtual Park Settings


stem-number-of-dead-wood

title_for_slug: stem-number-of-dead-wood

en:
Number of stems of dead trees and shrubs (standing and lying) starting at 12 cm dbh.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

cavity-by-woodpecker

display_name: cavity-by-woodpecker

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

main-tree-species

display_name: main-tree-species

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

stem-number-of-dead-wood

display_name: stem-number-of-dead-wood

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: Stem number of dead wood


en: elevation insects pests bark-beetles predators pinus gradient parasitoids

en: Gradients

en:
The column headers refer to:

- SamplingCode: WSL fauna database code referring to year and trap type (not relevant)
- SamplingDate: Date when the emergence traps were emptied, corresponds roughly to the date of emergence
- TreeNumber: Tree ID
- Defoliation: percentage of needle loss or crown transparency, according to international standards, of the trees prior to felling
- BarkSurface: bark surface area of stem parts exposed in emergence traps
- YearOfAttack: Year of colonization. Technically, the colonization window opened on 1st of January and closed in June, when sections of the tree stems were transported to the institute
- YearOfEmergence: Emergence year of a given cohort of insects in the greenhouse
- YearAfter Attack: Temporal difference between year of colonization and year of emergence of a given cohort of insects, corresponding to total development time
- Region: AO Aosta (ITA), GR Grisons (CH), VS Valais (CH)
- Location: locality nearest to the gradient
- Elevation: Site where trees were felled and exposed to colonization
- Aspect: north (N) or south (S) facing slope
- Species: taxonomic species according to Fauna Europaea
- Family: taxonomic family
- Order: taxonomic order: Col. Coleoptera, Hym. Hymenoptera, Dipt Diptera, Raph Raphidioptera
- Guild: Phyto phytophagous, Pred predatory, para parasitoid
- Number: number of sampled individuals of a given species at a given date.
- Density: number of emerged insects per m2 bark surface
- Identifyer: specialist who identified the insects

en:
The colonization of cut pine stems by wood-inhabiting insects was investigated at various elevations. The study sites were located in the regions of Aosta Valley (Italy), Valais (Switzerland), and Grisons (Switzerland). In each region, there were two gradients in pine (Pinus sylvestris) forests, with three study sites at 900 m, 1200 m, and 1600 m a.s.l. each. Vital trees were felled in late autumn and the stems were colonized by pioneering xylophagous insects and their natural enemies next spring. Pieces of these stems were cut and exposed in emergence traps in a greenhouse. In each region the survey was done in two consecutive years.

Please contact author for terms of use.

bark-beetles

display_name: bark-beetles

elevation

display_name: elevation

gradient

display_name: gradient

insects

display_name: insects

parasitoids

display_name: parasitoids

pests

display_name: pests

pinus

display_name: pinus

predators

display_name: predators

educ

en: Education, culture and sport

en: Pine insects along elevational gradients


en: conophagous-insects seminivorous picea cone spruce seed

en: See ReadMe

en: In 1989 a nation-wide survey on spruce seed and cone insects was carried out at 29 locations distributed across the 5 main geographic regions of Switzerland. The cones were incubated in a controlled environment chamber and the emerging insects were collected and identified. The cones were kept for three years to allow diapausing insects to emerge. The methods are described in more detail in the corresponding publications.

cone

display_name: cone

conophagous-insects

display_name: conophagous-insects

picea

display_name: picea

seed

display_name: seed

seminivorous

display_name: seminivorous

spruce

display_name: spruce

en: Survey of spruce seed and cone insects in Switzerland