greenland-shrubs-and-microclimate

maintainer: Nathalie Chardon

title_for_slug: greenland-shrubs-and-microclimate

en: sdm accessible-shrub-forest validation arctic climate field-data

en: Nuup Kangerlua

en:
 Collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen)

These data collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen). Please contact J. Nabe-Nielsen (jnn_at_ecos.au.dk) to request access to data on all surveyed shrub species. Available here are only data used in Chardon et al. 2022.

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

year: sampling year

alt: average altitude of grid as recorded during field surveys [m]

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

 Species presence [1] or absence [0] at grid level

pa_bena: _Betula nana_

pa_sagl: _Salix glauca_

 Shrub species cover (calculated from pin hits in 0.7 x 0.7 m frame, based on total of 25 hits) [%]

cov_bena: _Betula nana_ cover

cov_sagl: _Salix glauca_ cover

 Downscaled climate (time series 1984 – 2013)

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

tempcont: yearly maximum – minimum temperature [ºC]

precipmam: cumulative March-May precipitation [mm]

precipjja: cumulative June-August precipitation [mm]

insoljja: average June-August insolation [MJ cm-2 yr-1]

 Topography and terrain wetness (calculated from)

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

tcws: Tasseled Cap Wetness component (Sentinal images)

 Community characteristics

rich: shrub species richness averaged across pin-point frames per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on Betula nana (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on Salix glauca (i.e. summed cover of non-_S. glauca_ species) [% cover]

en: GitLab Repository

en: Zip drive of GitLab repository contents (https://gitlab.com/nathaliechardon/gl_microclim).

en: Kangerluarsunnguaq

en:
 Collected August 2020 (PI: Nathalie Isabelle Chardon)

These data collected August 2020 (PI: Nathalie Isabelle Chardon). Please contact N. I. Chardon (nathalie.chardon_at_gmail.com) for individual quadrat (i.e. 1 m x 1 m) data, which are averaged at the 90 m spatial grid here.

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

id: sampling zone and unique 90 m grid identifier

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

date: sampling day.month.year

 Species presence [1] or absence [0] at grid level

alcr: _Alnus crispa_

bena: _Betula nana_

dila: _Diapensia lapponica_

emni: _Empetrum nigrum_

hahy: _Harimanella hypoides_

lopr: _Loiseleuria procumbens_

oxpa: _Oxycoccus palustre_

phca: _Phyllodoce caerulea_

rhod: _Rhododendron_ sp.

saar: _Salix arctica_

sagl: _Salix glauca_

sahe: _Salix herbacea_

vaul: _Vaccinium uliginosum_

drin: _Dryas integrifolia_

thpr: _Thymus praecox_

 Habitat characteristics [% cover]

shrub: all shrub species

gram: graminoids

forb: forbs

bryo: bryophytes

lich: lichens

litt: litter

bare: bare ground

stone: stones (> 2 cm)

org: organic crust

 Shrub species height (maximum vegetative height) [mm] and cover (estimated in 1x1 m quadrat) [%]

alcr_c: _Alnus crispa_ cover

alcr_h: _Alnus crispa_ height

bena_c: _Betula nana_ cover

bena_h: _Betula nana_ height

dila_c: _Diapensia lapponica_ cover

dila_h: _Diapensia lapponica_ height

emni_c: _Empetrum nigrum_ cover

emni_h: _Empetrum nigrum_ height

hahy_c: _Harimanella hypoides_ cover

hahy_h: _Harimanella hypoides_ height

lopr_c: _Loiseleuria procumbens_ cover

lopr_h: _Loiseleuria procumbens_ height

oxpa_c: _Oxycoccus palustre_ cover

oxpa_h: _Oxycoccus palustre_ height

phca_c: _Phyllodoce caerulea_ cover

phca_height: _Phyllodoce caerulea_ height

rhod_c: _Rhododendron_ sp. cover

rhod_h: _Rhododendron_ sp. height

saar_c: _Salix arctica_ cover

saar_h: _Salix arctica_ height

sagl_c: _Salix glauca_ cover

sagl_h: _Salix glauca_ height

sahe_c: _Salix herbacea_ cover

sahe_h: _Salix herbacea_ height

vaul_c: _Vaccinium uliginosum_ cover

vaul_h: _Vaccinium uliginosum_ height

drin_c: _Dryas integrifolia_ cover

drin_h: _Dryas integrifolia_ height

 Community characteristics

rich_pa: shrub species richness at grid level

rich: shrub species richness averaged across 3 1x1 m sampling quadrats per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on _Betula nana_ (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on _Salix glauca_ (i.e. summed cover of non-_S. glauca_ species) [% cover]

ht_cwm: maximum shrub canopy height (i.e. community weighted mean by abundance of maximum shrub height) [mm]

 Topography and terrain wetness (calculated from)

tcws: Tasseled Cap Wetness component (Sentinal images)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

 Downscaled climate (time series 1984 – 2013)

insoljja: average June-August insolation [MJ cm-2 yr-1]

precipmam: cumulative March-May precipitation [mm]

tempcont: yearly maximum – minimum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

precipjja: cumulative June-August precipitation [mm]

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

en:
 Study Aim

We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns.

 Data Description

Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables.

 Nuup Kangerlua (Godthåbsfjord)

_Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness

 Kangerluarsunnguaq (Kobbefjord)

Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height

 Methods

 Field survey in Nuup Kangerlua

We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022).

 Field survey in Kangerluarsunnguaq

We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022).

 Biotic variables

We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height.

 Climate variables

We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June – August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March – May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021).

 Topography and terrain wetness indicator variables

We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how ‘wet’ an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness.

 Computer code

Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim)

 Third-party data

Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).

accessible-shrub-forest

display_name: accessible-shrub-forest

arctic

display_name: arctic

climate

display_name: climate

field-data

display_name: field-data

sdm

display_name: sdm

validation

display_name: validation

en: Greenland shrubs and microclimate


maintainer: EnviDat Support

en: sea-ice tls terrestrial-laser-scans sea-ice-elevation polar

state: active

type: dataset

en: Data processing documentation

format: SERVICE

en: Post Processed TLS's

media_type: application/zip

en: Compressed dat files

format: ZIP

en:
This dataset comprises of a post-processed set of terrestrial laser scans (TLS’s) of Antarctic sea ice obtained during the Sea Ice Physics and Ecosystem Experiment-2 (SIPEX-2, http://seaice.acecrc.org.au/sipex2012/) in September-November 2012. The post-processing steps include the registration of the individual scans into a single 3-dimensional point cloud, the removal of unwanted noise caused by particles in the air (i.e., snow crystals), and the final generation of surface grids based on the cleaned individual point returns. The final product includes the ‘xyz’ coordinates of the individual point measurements, and gridded surfaces covering study areas of 100m x 100 m, and at resolutions of 0.01 m, 0.1 m, 0.25 m, 0.5 m and 1 m for each of the survey dates. Additionally, subgrid statistics that include the mean elevation, standard deviation, minimum and maximum elevations, range, and number of point returns in each gridcell are generated. The final product is provided in space-delimited text files, with the surface grids provided in Digital Terrain Model (DTM) format ready for visualization in any GIS software.

How to cite:
Please also cite the original publication when using this data set.: Trujillo, E., K. Leonard, T. Maksym, and M. Lehning (2016), Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice, J. Geophys. Res. Earth Surf., 121, doi:[10.1002/2016JF003893](https://dx.doi.org/10.1002/2016JF003893).

polar

display_name: polar

sea-ice

display_name: sea-ice

sea-ice-elevation

display_name: sea-ice-elevation

terrestrial-laser-scans

display_name: terrestrial-laser-scans

tls

display_name: tls

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: High resolution sea ice surface topography from the SIPEX-2 expedition, East Antarctica, 2012


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

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
Abarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. J., Eberhardt, U., Erland, S., … Kõljalg, U. (2010). The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytologist, 186(2), 281–285. doi:10.1111/j.1469-8137.2009.03160.x
Bengtsson-Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., & Nilsson, R. H. (2015). metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15(6), 1403–1414. doi:10.1111/1755-0998.12399
Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., … Nilsson, R. H. (2013). Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution, 4(10), 914–919. doi:10.1111/2041-210X.12073
Bivand, R., Altman, M., Anselin, L., Assuncao, R., Berke, O., Blanchet, G., … Yu, D. (2019). spdep: Spatial dependence, weighthing schemes, statistics. R package version 1.1-3.
Bump, J. K., Peterson, R. O., & Vucetich, J. A. (2009). Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology, 90(11), 3159–3167.
Bump, J. K., Webster, C. R., Vucetich, J. A., Peterson, R. O., Shields, J. M., & Powers, M. D. (2009). Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems, 12(6), 996–1007. doi:10.1007/s10021-009-9274-0
Danell, K., Berteaux, D., & Brathen, K. A. (2002). Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic, 55(4), 389392.
De Caceres, M., & Jansen, F. (2016). indicspecies: relationship between species and groups of species. R package version 1.7.6.
De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119(10), 1674–1684. doi:10.1111/j.1600-0706.2010.18334.x
Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996.
Edgar, R. C., & Flyvbjerg, H. (2015). Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics, 31(21), 3476–3482. doi:10.1093/bioinformatics/btv401
Frey, B., Niklaus, P. A., Kremer, J., Lüscher, P., & Zimmermann, S. (2011). Heavy-machinery traffic impacts methane emissions as well as methanogen abundance and community structure in oxic forest soils. Applied and Environmental Microbiology, 77(17), 6060–6068. doi:10.1128/AEM.05206-11
Frey, B., Rime, T., Phillips, M., Stierli, B., Hajdas, I., Widmer, F., & Hartmann, M. (2016). Microbial diversity in European alpine permafrost and active layers. FEMS Microbial Ecology, 92(3), fiw018.
Frossard, A., Donhauser, J., Mestrot, A., Gygax, S., Bååth, E., & Frey, B. (2018). Long- and short-term effects of mercury pollution on the soil microbiome. Soil Biology and Biochemistry, 120, 191–199. doi:https://doi.org/10.1016/j.soilbio.2018.01.028
Geremia, C., Wallen, R., & White, P. J. (2017). Status report of the Yellowstone bison population, September 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center for Resources.
Gobet, A., Quince, C., & Ramette, A. (2010). Multivariate cutoff level analysis (MultiCoLA) of large community data sets. Nucleic Acids Research, 38(15), e155–e155. doi:10.1093/nar/gkq545
Latter, P., & Howson, G. (1977). The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia, (17), 145–155.
Latter, P., & Walton, D. (1988). The cotton strip assay for cellulose decomposition studies in soil: history of the assay and development. In Cotton strip assay: an index for decomposition in soils (pp. 7–9). ITE Symposium, Institute of Terrestrial Ecology, Natural Environment Research Council, UK.
Lenth, R., & Love, J. (2018). lsmeans: least-squares means. R package version 2.27-62.
Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17(1), 10–12.
Meagher, M. M. (1973). The bison of Yellowstone National Park. NPS Scientific Monograph (Vol. 1). National Park Service, Yellowstone Center for Resources.
Melis, C., Selva, N., Teurlings, I., Skarpe, C., Linnell, J. D. C., & Andersen, R. (2007). Soil and vegetation nutrient response to bison carcasses in Białowieża Primeval Forest, Poland. Ecological Research, 22(5), 807–813. doi:10.1007/s11284-006-0321-4
Nikolenko, S. I., Korobeynikov, A. I., & Alekseyev, M. A. (2013). BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics, 14(1), S7. doi:10.1186/1471-2164-14-S1-S7
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., … Wagner, H. H. (2019). vegan: community ecology package. R package version 2.5-4.
Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2018). nlme: Linear and nonlinear mixed effect models. R package version 3.1-131.1.
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., … Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590–D596. doi:10.1093/nar/gks1219
Quimby, D. C., & Johnson, D. E. (1951). Weights and measurements of Rocky Mountain elk. Journal of Wildlife Management, 15, 57–62.
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


maintainer: Michael Lehning

en: post-depositional-processes latent-heat sublimation snow weissfluhjoch stable-water-isotopes paleoclimate natural-tracer

en: Stable water isotopes in vapor

en:
Stable water isotopes in vapor measurements. Processed (calibrated) and raw data for February - June 2017. Raw data for January and February 2020.
For the time in between July 2017 and April 2020, raw vapor isotopic measurements are available, but no calibrations were performed during that time. For data delivery, please contact one of the authors.
This folder also contains the post-processing scripts for stable water isotopes in vapor measurements.

media_type: application/pdf

en:
Details on the data collected during the different field campaigns and available in this dataset.
Information on the sampling procedure of the snow isotopes samples.

format: PDF

en: Details about the different folders

en: Weather

en: Meteorological data, snow height measurement, and SNOWPACK simulations covering the different field campaigns.

en: Stable water isotopes in snow

en: Stable water isotopes in snow measurements

en:
Notice: Changes to the dataset are still possible. Please do not use this dataset until the final publication with a DOI. Contact the authors if you have questions about this.

This dataset contains measurements of stable water isotopes in snow and vapor on the Weissfluhjoch from different field campaigns (Winter 2017 (Trachsel, 2019), January 2020, December 2020, and March 2021 (Sadowski et al., 2022).
Snow profiles and surface samples are available at different frequencies for each campaign. Please see "Data_description.pdf" for details.

Scripts and SNOWPACK simulations used in (Trachsel, 2019) and (Sadowski et al., 2022) are also provided.

latent-heat

display_name: latent-heat

natural-tracer

display_name: natural-tracer

paleoclimate

display_name: paleoclimate

post-depositional-processes

display_name: post-depositional-processes

snow

display_name: snow

stable-water-isotopes

display_name: stable-water-isotopes

sublimation

display_name: sublimation

weissfluhjoch

display_name: weissfluhjoch

en: Stable Water Isotopes in snow and vapor on the Weissfluhjoch


en: rain-on-snow catchment-data meteorological-data snow-hydrology snowpack

en: The file contains all data needed to run simulations with the snow cover model SNOWPACK.

mimetype: application/zip

media_type: text/plain

format: TXT

mimetype: text/plain

en: Meteorological data used to run SNOWPACK for 58 catchments in the Swiss Alps. The data consists of a 2 km grid of "virtual meteorological stations" for each catchment. It was used to simulate snow cover processes during rain-on-snow events, therefore meteorological data of each catchment contains at least one rain-on-snow event. Further information can be found in the attached readme.txt and in Würzer & Jonas et al. (2017), currently under review in Hydrological Processes.

catchment-data

display_name: catchment-data

meteorological-data

display_name: meteorological-data

rain-on-snow

display_name: rain-on-snow

snow-hydrology

display_name: snow-hydrology

snowpack

display_name: snowpack

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: Meteorological data for investigation of rain-on-snow events in 58 catchments in Switzerland


en: saproxylic-beetles habitat-availability sloss conservation colonisation habitat-heterogeneity forest-management dead-wood biodiversity

en: Forest and environmental variables

en: Data on the branch bundle, forest and environmental variables

en: Community matrix (all beetles)

en: Abundances of all beetle species for each branch bundle

en: Community matrix (saproxylic beetles)

en: Abundances of saproxylic beetle species for each branch bundle

en: Variable description

en: Detailed description of the forest and environmental variables

en:
The data was generated during a landscape experiment performed on 69 plots in Sihlwald forest between 2016 and 2019. Branch bundles of different sizes consisting of beech branches were exposed in the forest for one year to be colonized by saproxylic beetles and later reared for one year in emergence traps. All beetles emerging from the branches were collected. Overall 244 branch bundles are included and we found 66 beetles species (23511 individuals) of which 43 were classified as saproxylic (20873 individuals).
 Species data (2 tables)
Two community matrices (including abundance): one for saproxylic beetles and one for all beetles. Beetles were classfied as saproxylic following an extended list of the list reported in Schmidl & Bussler (2004).
 Model data (1 table)
*Response variables*
Species richness and abundance of (saproxylic) beetles.
*Branch bundles*
Branch bundles consisted of 1, 3, 6 or 12 branches and each bundle size was installed at each of the 69 plots. The bundles consisted of standardized freshly cut beech branches (Fagus sylvatica) with a length of 80 cm and a diameter between 3 and 6 cm. Total surface and volume of each bundle is reported.
*Tree on which the bundle was installed*
Information on the tree to which the bundles were attached (tree species, dbh, distance and azimut from the plot center).
*Temperature and light availability*
Temperature (plot level) and light availability (bundle level) were calculated.
*Deadwood availability in the landscape*
Based on a map of lying dead wood, dead wood amount and isolation were calculated for concentric circles with a radius ranging from 20 to 200 m (in 10 m steps) around each branch bundle.
 Variable descriptions (1 table)

biodiversity

display_name: biodiversity

colonisation

display_name: colonisation

conservation

display_name: conservation

dead-wood

display_name: dead-wood

forest-management

display_name: forest-management

habitat-availability

display_name: habitat-availability

habitat-heterogeneity

display_name: habitat-heterogeneity

saproxylic-beetles

display_name: saproxylic-beetles

sloss

display_name: sloss

en: Beetle communities reared from beech branches and associated forest and environmental variables


en: snow-water-equivalent snow-temperatures weissfluhjoch snow-height enet imis

en: DATA ACCESS

en:
Station WFJ2: merged IMIS + ENET datasets. Provides parameters height of snow, snow surface temperatures and snow temps at 0, 1, 1.5 m above ground surface. Supporting meteo data are also contained in this dataset.

fields      = timestamp TA RH TSG TSS HS VW DW VW_MAX OSWR PSUM VW_DRIFT DW_DRIFT TS1 TS2 TS3 HTS1 HTS2 HTS3

en: This dataset provides HS, TSS and TS50, TS100, TS150 at the station WFJ2 situated on the Weisfluhjoch research site (2536 m asl). It has been created from merging ENET and IMIS datsets to form a continuous timeseries from 1992- present. ENET is at 1 h resolution whereas IMIS is 30 min. This is a level 2 dataset as defined [here](http://models.slf.ch/p/dataset-processing/).

enet

display_name: enet

imis

display_name: imis

snow-height

display_name: snow-height

snow-temperatures

display_name: snow-temperatures

snow-water-equivalent

display_name: snow-water-equivalent

en: WFJ2: Snow measurements from the Weissfluhjoch research site, Davos


en: diurnal-variability fluorescent-particles aerosol-cloud-interactions biological-particles dust-particles ice-nucleation

en: Figure 6.xlsx

en: This file contains data presented in Figure 6 in the main text.

en: Figure 3.xlsx

en: This file contains data presented in Figure 3 in the main text.

en: Figure 4.xlsx

en: This file contains data presented in Figure 4 in the main text.

en: Figure 1.xlsx

en: This file contains data presented in Figure 1 in the main text.

en: Data for figures in the Supplementary.xlsx

en: This file contains data presented in Figure S2 to S36 in the supplementary.

en: Figure 5.xlsx

en: This file contains data presented in Figure 5 in the main text.

en: Figure 2.xlsx

en: This file contains data presented in Figure 2 in the main text.

en: Figure 7.xlsx

en: This file contains data presented in Figure 7 in the main text.

en:
This repository contains all observational data sets during CALISHTO campaign used for the paper: Gao, K., Vogel, F., Foskinis, R., Vratolis, S., Gini, M. I., Granakis, K., Zografou, O., Fetfatzis, P., Berne, A., Papagiannis, A., Möhler, O., Eleftheridadis, K., and Nenes, A.: On the drivers of ice nucleating particle diurnal variability in Eastern Mediterranean clouds, npj Climate and Atmospheric Science, Preprint link: https://www.researchsquare.com/article/rs-4378562/v1.
CALISHTO campaign was conducted between 12 October and 27 November 2021 to observe ice nucleating particles and aerosol properties at the Helmos Hellenic Atmospheric Aerosol and Climate Change station in Eastern Mediterranean, to understand cloud-aerosol interactions. To evaluate the spatial-temporal variabilities and characteristics of ice nucleating particles, a high-time resolution ice nucleation spectrometer was employed and different online aerosol property measurements were conducted in-situ, including particle number concentration, particle size distribution, particle fluorescent properties, and particle chemical composition. In addition, planetary boundary layer conditions at the observation site were determined by remote sensing techniques employed at a lower site.

aerosol-cloud-interactions

display_name: aerosol-cloud-interactions

biological-particles

display_name: biological-particles

diurnal-variability

display_name: diurnal-variability

dust-particles

display_name: dust-particles

fluorescent-particles

display_name: fluorescent-particles

ice-nucleation

display_name: ice-nucleation

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: CALISHTO campaign dataset for the publication On the Drivers of Ice Nucleating Particle Diurnal Variability in Eastern Mediterranean Clouds


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

en: Predictors

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

en: Description

en: Description of the predictors provided in this dataset

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

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

Paper Citation:

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

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

switzerland

display_name: switzerland


media_type: text/csv

format: CSV

mimetype: text/csv

en:
Attention: this data is not updated after 2022 anymore.
This dataset contains the statistics on the number of avalanche fatalities per **calendar year** in Switzerland. The data collection commences with the beginning of the year 1937. After the completion of a hydrological year, which is the standard way avalanche fatalities are summarized in Switzerland and ends on the 30th of September, the new data is appended to the existing dataset.
If you require annual statistics per hydrological year, please download the data from here: [https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936]
The following information is contained (by column and column title):
 - year
 - number of fatalities in the backcountry (=tour)
 - number of fatalities in terrain close to ski areas (=offpiste, away from open and secured ski runs)
 - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors)
 - number of fatalities in or around buildings or in settlements (= buildings)
 - sum (of all four categories)
The definitions for these four categories, as described in the guidelines to the avalanche accident database are:
tour:
activities include back-country ski, snowboard or snow-shoe touring
offpiste:
access from ski area, generally from the top of a skilift with short hiking distances
transportation.corridors (Techel et al., 2016):
people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams)
buildings (Techel et al., 2016):
people inside or just outside buildings, and workers on high alpine building sites

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-fatalities

display_name: avalanche-fatalities

fatalities-in-or-around-buildings

display_name: fatalities-in-or-around-buildings

fatalities-in-settlements

display_name: fatalities-in-settlements

fatalities-in-the-backcountry

display_name: fatalities-in-the-backcountry

fatalities-on-transportation-corridors

display_name: fatalities-on-transportation-corridors

en: Number of avalanche fatalities per calendar year in Switzerland since 1937


kantonaler-nutzungsplan-verkehr

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: kantonaler-nutzungsplan-verkehr

author: Abteilung Verkehr

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

de: AGIS Direktdownload

de: Kantonaler Nutzungsplan Verkehr: Perimeter

format: WMS

de: Der Datensatz bildet die Perimeter aller gültigen Kantonalen Nutzungspläne (KNP) im Bereich Verkehr mit rechtskräftigen Festsetzungen ab. Diese Pläne und die zugehörigen Vorschriften sind in der §10 des Baugesetzes, BauG (altrechtlich auch als "Überbauungspläne" bezeichnet) geregelt. Die KNP Verkehr decken die strategischen Freihalteinteressen für Strassenbauvorhaben ab. Ihre Festsetzungen sind öffentlich-rechtliche Eigentumsbeschränkungen (ÖREB); dieser Datensatz gehört zum Bereich Raumnutzung des ÖREB-Katasters. Sie können folgenden Festsetzungen enthalten: • Baulinien: sie gehen anderen Abstandsvorschriften vor. Sie treten an Stelle der generellen Abstandsvorschriften. Baulinien begrenzen die Bebauung und dienen, insbesondere der Sicherung bestehender und geplanter Anlagen und Flächen sowie der baulichen Gestaltung. • Sichtlinien • Zu-/Wegfahrtbeschränkungen • Strassenlinien • Sondernutzungsvorschriften

de: Abteilung Verkehr

de: AGIS Service Center Kanton Aargau

de: Das AGIS Service Center unterstützt als kantonale Koordinationsstelle die Bereitstellung von Geoinformationen und Metadaten der kantonalen Verwaltung. Die zuständigen kantonalen Fachstellen stellen die öffentlichen Geodaten des Kantons Aargau mittels der Infrastruktur des AGIS Service Center in der Informatik Aargau als Webservices und zum Download bereit.

political_level: canton

value: Aargau-Kt - Geocat Harvester


risikokataster-konsultationsbereiche

title_for_slug: risikokataster-konsultationsbereiche

author: Amt für Verbraucherschutz

de: Risikokataster: Konsultationsbereiche

de: Lage und angrenzende Bereiche gemäss Störfallverordnung im Bereich Kantonsstrassen und Nationalstrassen (Bundesamt für Strassen ASTRA) Die Störfallverordnung (StFV, SR 814.012) bezweckt den Schutz der Bevölkerung und der Umwelt vor schweren Schädigungen infolge von Störfällen. Sie regelt das eigenverantwortliche Umsetzen von Sicherheitsmassnahmen durch die Inhaber eines Verkehrswegs, eines Betriebs oder einer Rohrleitungsanlage und das Kontroll- und Beurteilungsverfahren durch die Behörden. Der Störfallverordnung sind Durchgangsstrassen gemäss Durchgangsstrassenverordnung unterstellt, auf welchen gefährliche Güter nach der Verordnung vom 17. April 1985 über die Beförderung gefährlicher Güter auf der Strasse (SDR) oder den entsprechenden internationalen Übereinkommen (ADR) transportiert oder umgeschlagen werden. Gemäss Artikel 1 der Durchgangsstrassenverordnung sind Durchgangsstrassen für den allgemeinen Durchgangsverkehr offen. Es sind dies Autobahnen, Autostrassen und Hauptstrassen. Der Inhalt der Kantonsstrassen bezieht sich auf das Prognosemodell KVM_AG 15/40 und folgende. Der Schwellenwert für die Zuteilung eines Konsultationsbereiches beträgt 20'000 Fahrzeuge pro Tag. Beim Inhalt der Nationalstrassen handelt es sich um eine manuell nachgeführte Kopie des Geobasisdatensatzes ID 211.1 des ASTRA.

de: Amt für Verbraucherschutz


knp-verkehr-festlegungen-linien

title_for_slug: knp-verkehr-festlegungen-linien

de: Der Datensatz enthält die linienbezogenen überlagernden rechtskräftigen Festlegungen, die im Verfahren einer kantonalen Nutzungsplanung im Bereich Verkehr genehmigt worden sind: • Baulinien: sie gehen anderen Abstandsvorschriften vor. Sie treten an Stelle der generellen Abstandsvorschriften. Baulinien begrenzen die Bebauung und dienen, insbesondere der Sicherung bestehender und geplanter Anlagen und Flächen sowie der baulichen Gestaltung. • Sichtlinien • Zu-/Wegfahrtbeschränkungen • Strassenlinien

de: Kantonaler Nutzungsplan Verkehr: Festlegungen (Linien)


knp-verkehr-festlegungen-flache

title_for_slug: knp-verkehr-festlegungen-flache

de: Der Datensatz enthält die flächenbezogenen überlagernden rechtskräftigen Festlegungen, die im Verfahren einer kantonalen Nutzungsplanung im Bereich Verkehr genehmigt worden sind: • Zu-/Wegfahrtbeschränkungen • Sondernutzungsvorschriften

de: Kantonaler Nutzungsplan Verkehr: Festlegungen (Flächen)


soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

de: Bildung, Kultur und Sport

regi

de: Regionen und Städte

author: Kantonsarchäologie

de: Archäologische Fundstellen

de: Der Datensatz enthält archäologische Fundstellen als interpretierte Flächen, wobei unterschieden wird zwischen Stätten, die sicher grossflächig sind (A-Fundstellen: z.B. Bauten/Siedlungen, Gräber/Friedhöfe), und Einzelfund-Stellen, die auf grossflächige Fundstellen hinweisen können (B-Fundstellen: z.B. Steinbeil). Die interpretierte Darstellung von archäologischen Fundstellen basiert auf Ausgrabungen und auf aktenkundigen Fundmeldungen, die teilweise bis zu 200 Jahre alt sind, vereinzelt auch auf Flurnamen (z.B. Muracher); sehr alte Fundmeldungen lassen sich manchmal nur schwierig interpretieren und ungenau lokalisieren. Der erste Durchgang der kantonsweiten Erfassung ist Ende 2005 abgeschlossen worden; die Ausarbeitung und Nachführung wird laufend zu Ergänzungen und einzelnen Änderungen führen. Angaben über Datierung, Art, Umfang und Erhaltungsgrad einer Fundstelle können über die Fundstellennummer, die als Attribut auch im GIS-Datensatz enthalten ist, auf Anfrage erschlossen werden.

de: Kantonsarchäologie


spezialwurzelraum

agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

envi

de: Umwelt

title_for_slug: spezialwurzelraum

de: baum baumgrube baumschutz baumwurzel wurzel wurzelraum

author: Stadtgrün Bern

de: geocat.ch Permalink

de: Spezialwurzelraum

de: Web Map Services WMS

de: WFS Spezialwurzelraum

de: Web Feature Services WFS

format: WFS

de: Perimeter von speziell unterirdisch erstellten Wurzelräumen für Bäume.

spatial: Stadt Bern

de: Stadtgrün Bern

geoinformation-der-stadt-bern

fr: L’Office du cadastre de la ville de Berne

de: Geoinformation Stadt Bern

en: The Land Surveying Office of the City of Bern

it: L'ufficio del catasto nella Città di Berna

fr: L’Office du cadastre (Geoinformation Stadt Bern) est l’organisme compétent pour la collecte, l’accès et la diffusion d’informations géographiques dans la ville de Berne.

de: Geoinformation Stadt Bern (ehemals Vermessungsamt) ist die Kompetenzstelle für die Beschaffung, den Zugang und die Verbreitung von Geoinformationen in der Stadt Bern.

en: The Surveying Office (Geoinformation Stadt Bern) is the centre of competence in the City of Bern for the procurement of, access to and dissemination of geographic information.

it: L'ufficio del catasto (Geoinformation Stadt Bern) è il centro di competenza per il reperimento, l'accesso e la diffusione di geoinformazioni nella Città di Berna.

political_level: commune

value: Geoinformation Stadt Bern - Geocat Harvester


aufwertungsmassnahmen

soci

de: Bevölkerung und Gesellschaft

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

title_for_slug: aufwertungsmassnahmen

de: Aufwertungsmassnahmen

de: WFS Aufwertungsmassnahmen

de: Aufwertungsmassnahmen im öffentlichen Strassenraum

de: Tiefbau Stadt Bern


tannenareale

agri

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

regi

de: Regionen und Städte

envi

de: Umwelt

title_for_slug: tannenareale

author: Abteilung Wald, Bundesamt für Umwelt

fr: Lien vers le portail

de: Link zum Fachportal

en: Link to portal

it: Link per il portale

fr: Map (Preview) Aperçu map.geo.admin.ch

de: Map (Preview) Vorschau map.geo.admin.ch

en: Map (Preview)  map.geo.admin.ch

it: Map (Preview) Previsione map.geo.admin.ch

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

fr: Service WMS-IFDG, couche "Aires du sapin blanc"

de: WMS-BGDI Dienst, Layer "Tannenareale"

en: WMS-FSDI service, layer "Areas of silver fir"

it: Servizio WMS-IFDG, strato "Areale dell`abete bianco"

fr: Service WMTS-IFDG, couche , Layer "Aires du sapin blanc"

de: WMTS-BGDI Dienst, Layer "Tannenareale"

en: WMTS-FSDI service, layer "Areas of silver fir"

it: Servizio WMTS-IFDG, strato "Areale dell'abete bianco"

format: WMTS

de: STAC Browser

fr: Lien vers la distribution des données

de: Link zum Datenbezug

en: Link for download

it: Link per le fonti dei dati

fr: La carte montre les aires de sapin blanc. Dans l'aire principale, le sapin contribue naturellement dans une large mesure à la structure du peuplement dans les étages de végétation de montagnard supérieur et de haute-montagnard. Dans l'aire secondaire, sa vitalité est plus faible que dans l'aire principale. Selon la région et l'écotype, il est encore impliqué à des degrés divers dans la structure du peuplement. Dans l'aire relictuelle, il se trouve tout au plus dans de petits peuplements isolés comme vestiges d'une zone du sapin autrefois plus grande.Les écorégions forestières ont été élaborées pour "Gestion durable des forêts de protection" (Frehner et al. 2005). Des informations détaillées sont disponibles sur https://www.gebirgswald.ch/de/nais-download.html

de: Die Karte zeigt die Areale der Weisstanne. Im Hauptareal ist die Tanne in der obermontanen und hochmontanen Stufe natürlicherweise in einem erheblichen Masse am Bestandesaufbau beteiligt. Im Nebenareal ist ihre Vitalität im Vergleich zum Hauptareal geringer. Je nach Region und Ökotyp ist sie hier noch unterschiedlich stark am Bestandesaufbau beteiligt. Im Reliktareal kommt sie höchstens noch in kleinen, isolierten Beständen als Relikte eines ehemals grösseren Tannenareals vor.Die Tannenareale wurden erarbeitet für "Nachhaltigkeit und Erfolgskontrolle im Schutzwald" (Frehner et al. 2005). Detaillierte Informationen stehen zur Verfügung unter https://www.gebirgswald.ch/de/nais-download.html

en: The map shows the areas of silver fir occurrence. In the main area, fir is naturally involved to a considerable extent in the stand development in the upper montane and high-montane vegetation belts. In the secondary area, its vitality is lower than in the main area. Depending on the region and ecotype, it is still involved to varying degrees in the stand build-up. In the relict area it occurs at most in small, isolated stands as relics of a formerly larger fir area.The areas of silver fir were developed for "Sustainability and success monitoring in protection forests" (Frehner et al. 2005). Detailed information is available at https://www.gebirgswald.ch/de/nais-download.html

it: La carta mostra gli areali dell'abete bianco. Nell'areale principale, l'abete bianco è naturalmente coinvolto in larga misura nello sviluppo dei popolamenti nelle fasce altitudinali montana superiore e altimontana. Nell'areale secondario, la sua vitalità è inferiore a quella dell'areale principale. A seconda della regione e dell'ecotipo, la specie è ancora coinvolta, in misura diversa, nella costruzione del popolamento. Nell'areale relitto dell'abete quest'ultimo è presente al massimo in piccole aree isolate in forma di relitti di un'areale maggiore nel passato.Le areale dell'abete bianco sono state sviluppate per "Continuità nel bosco di protezione e controllo dell'efficacia" (Frehner et al. 2005). Informazioni dettagliate si trovano su https://www.gebirgswald.ch/de/nais-download.html

spatial: Schweiz

fr: Division forêts, Office fédéral de l'environnement

de: Bundesamt für Umwelt / Abteilung Wald

en: Forest Division, Federal Office of the Environment

it: Divisione foreste, Ufficio federale dell'ambiente

bundesamt-fur-umwelt-bafu

fr: Office fédéral de l'environnement OFEV

de: Bundesamt für Umwelt BAFU

en: Federal Office for the Environment FOEN

it: Ufficio federale dell'ambiente UFAM

fr: L’autorité compétente pour les questions environnementales. Il est à ce titre chargé de gérer les ressources naturelles selon les principes du développement durable et de protéger l’homme contre les dangers naturels et l’environnement contre les pollutions.

de: Die Fachbehörde für die Umwelt. Es ist zuständig für die nachhaltige Nutzung der natürlichen Ressourcen sowie für den Schutz des Menschen vor Naturgefahren und den Schutz der Umwelt vor übermässigen Belastungen.

en: The federal environmental authority. It is responsible for ensuring that natural resources are used sustainably, that the public is protected against natural hazards, and that the environment is protected from unacceptable adverse impacts.

it: Il servizio specializzato in materia ambientale, è competente per quanto riguarda l'utilizzazione sostenibile delle risorse naturali, la protezione delle persone dai pericoli naturali e dell'ambiente da un inquinamento eccessivo.

political_level: confederation

value: BAFU - Geocat Harvester

fr: Aires du sapin blanc

de: Tannenareale

en: Areas of silver fir

it: Areale dell'abete bianco


sturmgefahrdung

regi

fr: Régions et villes

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

en: Environment

it: Ambiente

title_for_slug: sturmgefahrdung

de: naturrisikoanalyse naturrisiko luftbewegung sturm wind bgdi-bundesgeodaten-infrastruktur

de: geocat.ch Permalink

en: geocat.ch permalink

de: Link für Datenbezug

format: HTML

fr: Les cartes des dangers relatifs aux tempêtes montrent les pointes des rafales ainsi que leurs pressions dynamiques avec leurs marges d’erreurs. Elles fournissent une base qui permet d’évaluer le danger régional. À ce titre, la référence concernant les pressions dynamiques (norme SIA 261 annexe E) n’est pas remplacée. Le danger de tempête est calculé sur la base de 83 périodes de tempêtes hivernales, enregistrées de 1871 à 2011, ainsi que de l’ensemble des données globales « 20th Century Reanalysis ». Les calculs ne tiennent pas compte des influences locales telles que les tempêtes estivales ou les bourrasques d’orage. Les indications fournies pour les pointes des rafales ne doivent donc pas être interprétées comme des valeurs exactes et précises géographiquement. Elles ne remplacent en aucun cas une évaluation détaillée d’un lieu spécifique. Dans des sites exposés de haute montagne notamment (p. ex. le Jungfraujoch), les pointes des rafales sont souvent sous-estimées.

de: Die Karten der Sturmgefährdung zeigen die Böenspitzen sowie den daraus abgeleiteten Staudruck mit den entsprechenden Unsicherheiten. Die Karten stellen eine Grundlage dar, um die regionale Sturmgefährdung einzuschätzen, wobei das Referenzwerk des Staudrucks (SIA Norm 261 Anhang E) dadurch nicht ersetzt wird. Berechnungsgrundlagen der Sturmgefährdung bilden 83 Wintersturmperioden zwischen 1871 und 2011 sowie der globale Datensatz „20th Century Reanalysis“. Für die Berechnungen nicht berücksichtigt wurden lokale Einflüsse sowie Sommerstürme und Gewitterböen. Die Werte für Böenspitzen sind deshalb nicht als punktgenaue, exakte Werte zu interpretieren und ersetzen keine standortspezifischen Gutachten. Vor allem für exponierte, hochalpine Standorte (z. B. Jungfraujoch) werden die Böenspitzen unterschätzt.

en: The Windstorm Risk Maps show peak wind gusts and their dynamic pressure and the corresponding uncertainties. The maps provide a country-wide basis for the estimation of the regional windstorm risk, however they do not replace the Dynamic Pressure Reference (Standard SIA 261 Annex E). The windstorm risk is calculated on the basis of 83 winter storm periods between 1871 and 2011 and the 20th Century Reanalysis global data. Local influences, summer storms and thunder squalls were not taken into account in the calculations. The values specified for the peak wind gusts should not, therefore, be interpreted as absolutely accurate values and do not replace any location-specific reports. The peak wind gusts are underestimated for exposed Alpine locations (e.g. Jungfraujoch), in particular.

it: Le carte sul pericolo di tempeste mostrano le velocità massime delle raffiche di vento e le relative pressioni dinamiche compreso il margine d’errore. Le carte costituiscono una base di dati riferita all’intero territorio nazionale che consente di valutare il pericolo di tempeste a livello regionale, ma non sostituiscono tuttavia le pressioni dinamiche di riferimento (norme SIA 261 allegato E). Per il calcolo del pericolo di tempeste sono utilizzati i dati riferiti a 83 periodi di tempeste invernali registrati fra il 1871 e il 2011 e l’insieme dei dati globali «20th Century Reanalysis ». Nei calcoli non sono stati considerati né gli influssi locali né le tempeste estive o le raffiche temporalesche. I valori relativi alle velocità massime delle raffiche di vento non devono quindi essere interpretati come puntuali ed esatti e non sostituiscono in nessun caso le valutazioni specifiche a una determinata zona. I valori relativi in particolare a località di alta montagna esposte (p. es. la Jungfraujoch) sono sovente sottovalutati.

fr: Tempêtes

de: Sturmgefährdung

en: Windstorm

it: Tempesta


agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

regi

de: Regionen und Städte

envi

de: Umwelt

de: Link zum Fachportal

fr: geocat.ch permalien

it: geocat.ch link permanente

fr: Service WMTS-IFDG, couche , Layer "Modèle de surface IFN"

de: WMTS-BGDI Dienst, Layer "Oberflächenmodell LFI"

en: WMTS-FSDI service, layer "Surface model NFI"

it: Servizio WMTS-IFDG, strato "Modello della superficie terrestre IFN"

de: Oberflächenmodell LFI

fr: RESTful API de geo.admin.ch

de: RESTful API von geo.admin.ch

en: RESTful API from geo.admin.ch

it: RESTful API da geo.admin.ch

format: API

fr: Les informations sur la hauteur de la végétation sont obtenues par des méthodes de télédétection utilisant des capteurs actifs et passifs. Le LiDAR est un exemple de capteur actif. Un capteur passif est un capteur optique, par exemple un appareil de prises de vues aériennes. L'acquisition de données LiDAR est réalisée en Suisse à l'échelle régionale, cantonale et nationale. Des photographies stéréoscopiques sont prises de l'ensemble du territoire national selon une périodicité de trois ans. Ces données de base programmées fournies par swisstopo servent à élaborer régulièrement des jeux de données 3D – des modèles numériques de surface (MNS) – à haute résolution dans le cadre de l'inventaire forestier national (IFN). La différence entre le MNS et le modèle numérique de terrain de swisstopo (swissAlti3D) permet de calculer un modèle numérique de surface normalisé (MNSn). Les constructions sont gommées en combinant les surfaces de base du modèle topographique du paysage (MTP) de swisstopo et les informations spectrales des images stéréoscopiques. Le résultat final est un modèle de la hauteur de la végétation (MHV) en Suisse.

de: Informationen zu Vegetationshöhen werden mit Methoden der Fernerkundung mit aktiven und passiven Sensoren ermittelt. Ein aktiver Sensor ist LiDAR. Ein passiver Sensor ist ein optischer Sensor, zum Beispiel eine Luftbildkamera. LiDAR wird in der Schweiz auf regionaler, kantonaler und nationaler Ebene aufgenommen. Stereo – Lufbilder werden auf nationaler Ebene regelmässig in einem 3-Jahres Turnus aufgezeichnet. Aus diesen planbaren Grundlagendaten der swisstopo werden regelmässig 3D Datensätze, digitale Oberlächenmodelle (DOM), mit hoher Auflösung im Rahmen des Landesforstinventars (LFI) erstellt. Aus der Differenz des DOM und dem digitalen Terrainmodell der swisstopo (swissAlti3D) wird ein normalisiertes digitales Oberflächenmodell (nDOM) berechnet. Gebäude werden mit einer Kombination aus den Grundflächen des Topographischen Landschaftsmodells (TLM) der swisstopo und den spektralen Informationen der Stereo – Luftbilder eliminiert. Das Endresutat ist ein Modell der Vegetationshöhe (Vegetationshöhenmodell VHM) über die Schweiz.

en: Information on vegetation height is determined using remote sensing methods with active and passive sensors. LiDAR is an active sensor. An optical sensor, for example an aerial camera, is a passive sensor. LiDAR is incorporated in Switzerland on a regional, cantonal and national level. Stereo aerial photos are regularly recorded on a national level in a three year cycle. Regular 3D data sets and digital surface models (DSM) with a high resolution are created from this predictable core data from swisstopo within the framework of the National Forest Inventory (NFI). From the difference between the DSM and the digital terrain model by swisstopo (swissAlti3D), a normalised digital surface model (nDSM) is calculated. Buildings are eliminated using a combination of the ground areas of the swisstopo topographic landscape model (TLM) and spectral information from the stereo aerial photos. The end result is a model of vegetation height (vegetation height model VHM) in Switzerland.

it: Le informazioni relative all'altezza della vegetazione vengono ottenute con metodi di teledetezione mediante sensori attivi e passivi. Un sensore attivo è il LiDAR. Un sensore passivo è un sensore ottico, come ad esempio un apparecchio fotografico per immagini aeree. L'acquisizione dei dati LiDAR in Svizzera ha luogo su scala regionale, cantonale e nazionale. Vengono effettuate regolarmente riprese fotografiche stereoscopiche a livello nazionale con cadenza triennale. In base a questi dati di base pianificarbili di swisstopo vengono quindi realizzati regolarmente set di dati in 3D - modelli digitali della superficie (MDS) - con una risoluzione elevata nell'ambito dell'Inventario Forestale Nazionale (IFN). In base alla differenza tra il MDS e il modello digitale del terreno di swisstopo (swissAlti3D) è possibile calcolare il modello digitale della superficie normalizzato (MDSn). Le costruzioni vengono eliminate utilizzando una combinazione delle superfici di base del modello topografico del paesaggio (TLM) e delle informazioni spettrali delle immagini aeree stereoscopiche. Il risultato finale è costituito da un modello di altezza della vegetazione (modello di altezza della vegetazione VHM) in Svizzera.

fr: Estompage du relief appliqué au modèle de la hauteur de la végétation de l'IFN

de: Reliefschattierung zum Vegetationshöhenmodell LFI

en: Relief shading of vegetation height model NFI

it: Ombreggiature del rilievo della vegetazione IFN


de: forstwirtschaft klima luft-atmosphare klima luft-atmosphare umweltuberwachung bgdi-bundesgeodaten-infrastruktur

fr: Lien vers le portail

en: Link to portal

it: Link per il portale

fr: Service WMS-IFDG, couche "Temp. de l`air juillet 2085, RCP 8.5"

de: WMS-BGDI Dienst, Layer "Lufttemperatur Juli 2085, RCP 8.5"

en: WMS-FSDI service, layer "Air temperature July 2085, RCP 8.5"

it: Servizio WMS-IFDG, strato "Temp. dell`aria luglio 2085, RCP 8.5"

fr: Service WMTS-IFDG, couche , Layer "Temp. de l'air juillet 2085, RCP 8.5"

de: WMTS-BGDI Dienst, Layer "Lufttemperatur Juli 2085, RCP 8.5"

en: WMTS-FSDI service, layer "Air temperature July 2085, RCP 8.5"

it: Servizio WMTS-IFDG, strato "Temp. dell'aria luglio 2085, RCP 8.5"

fr: Map (Preview) Aperçu map.geo.admin.ch

de: Map (Preview) Vorschau map.geo.admin.ch

en: Map (Preview)  map.geo.admin.ch

it: Map (Preview) Previsione map.geo.admin.ch

fr: La température maximale journalière moyenne de l’air en juillet (période 2070-2099). L’augmentation de température correspondant au scénario climatique a été ajoutée à la carte «Temp. de l’air juillet 1981-2010». Parmi les trajectoires de concentration représentatives, la trajectoire RCP 8.5 avec la combinaison de modèles CLMCOM-CCLM5_HADGEM_EUR44 a été choisie comme projection d’un changement climatique prononcé (NCCS 2018; fourniture des données par Remund et al. 2020) pour calculer l’augmentation de la température dans le futur. La carte qui en résulte présente une résolution spatiale de 25 m x 25 m. Elle a été utilisée pour la modélisation des étages de végétation forestière.

de: Die mittlere Höchsttemperatur der Luft im Tagesverlauf im Juli (Periode 2070-2099). Als Grundlage dient die Karte «Lufttemperatur Juli 1981-2010», an die die Temperaturänderung durch den Klimawandel hinzugefügt wurde. Für die Berechnung der Änderung der Temperatur in der Zukunft wurde der Repräsentative Konzentrationspfad RCP 8.5 mit der Modellkombination CLMCOM-CCLM5_HADGEM_EUR44 als Projektion für einen starken Klimawandel gewählt (NCCS 2018; Bereitstellung der Daten durch Remund et al. 2020). Die resultierende Karte hat eine räumliche Auflösung von 25 m x 25 m. Sie wurde für die Modellierung von Vegetationshöhenstufen verwendet.

en: Mean daily temperature high in July (period 2070-2099). The temperature increase of the climate scenario was added to the map «Air temperature July 1981-2010». Among the representative concentration pathways, RCP 8.5 with the model combination CLMCOM-CCLM5_HADGEM_EUR44 was selected as a projection for strong climate change (NCCS 2018; Provision of the data by Remund et al. 2020) to calculate the temperature increase in the future. The resulting map has a spatial resolution of 25 m x 25 m. It was used for modelling altitudinal belts of forest vegetation.

it: La temperatura massima giornaliera media dell'aria in luglio (periodo 1981-2010). L'aumento di temperatura dello scenario climatico è stato aggiunto alla carta «Temp. dell'aria luglio 1981-2010». Il percorso di concentrazione rappresentativo RCP 8.5 con la combinazione di modelli CLMCOM-CCLM5_HADGEM_EUR44 è stato scelto come la proiezione di un forte cambiamento climatico (NCCS 2018; fornitura dei dati da parte di Remund et al. 2020) per calcolare in futuro l'aumento della temperatura. La carta risultante ha una risoluzione spaziale di 25 m x 25 m. È stata utilizzata per modellare delle fasce altitudinali della vegetazione forestale.

de: Bundesamt für Umwelt / Abteilung Wald

bundesamt-fur-umwelt-bafu

de: Bundesamt für Umwelt BAFU

fr: Températures maximales journalières moyennes de l’air en juillet, période 2070-2099, scénario RCP 8.5

de: Mittlere Höchstwerte des Tagesganges der Lufttemperatur im Juli, Periode 2070-2099, Szenario RCP 8.5

en: Mean daily temperature high in July, period 2070-2099, Scenario RCP 8.5

it: Temperatura massima giornaliera media dell'aria in luglio, periodo 2070-2099, Scenario RCP 8.5


agri

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

de: geocat.ch Permalink

fr: Lien vers la distribution des données

de: Link zum Datenbezug

en: Link for download

it: Link per le fonti dei dati

de: Waldbrandpräventionsmassnahmen

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

fr: Contient les mesures de prévention des incendies de forêt actuellement en vigueur dans les cantons.

de: Enthält die aktuell bestehenden Präventionsmassnahmen der Kantone zur Waldbrandgefahr

en: Contains the forest fire prevention measures currently in force in the cantons.

it: Contiene le misure di prevenzione degli incendi di boschi attualmente in vigore nei Cantoni.

fr: Division forêts, Office fédéral de l'environnement

en: Forest Division, Federal Office of the Environment

it: Divisione foreste, Ufficio federale dell'ambiente

fr: Office fédéral de l'environnement OFEV

en: Federal Office for the Environment FOEN

it: Ufficio federale dell'ambiente UFAM

fr: L’autorité compétente pour les questions environnementales. Il est à ce titre chargé de gérer les ressources naturelles selon les principes du développement durable et de protéger l’homme contre les dangers naturels et l’environnement contre les pollutions.

de: Die Fachbehörde für die Umwelt. Es ist zuständig für die nachhaltige Nutzung der natürlichen Ressourcen sowie für den Schutz des Menschen vor Naturgefahren und den Schutz der Umwelt vor übermässigen Belastungen.

en: The federal environmental authority. It is responsible for ensuring that natural resources are used sustainably, that the public is protected against natural hazards, and that the environment is protected from unacceptable adverse impacts.

it: Il servizio specializzato in materia ambientale, è competente per quanto riguarda l'utilizzazione sostenibile delle risorse naturali, la protezione delle persone dai pericoli naturali e dell'ambiente da un inquinamento eccessivo.

fr: Mesures de prévention des incendies de forêt des cantons

en: Forest fire prevention measures of the cantons

it: Misure di prevenzione degli incendi di boschi nei cantoni


regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

de: Link zum Fachportal

fr: geocat.ch permalien

en: geocat.ch permalink

it: geocat.ch link permanente

fr: Service WMTS-IFDG, couche , Layer "Dégâts de tempête Vivian"

de: WMTS-BGDI Dienst, Layer "Sturmschaden Vivian"

en: WMTS-FSDI service, layer "Vivian storm damage"

it: Servizio WMTS-IFDG, strato "Aree boschive danneggiate Vivian"

fr: Service WMS-IFDG, couche "Dégâts de tempête Vivian"

de: WMS-BGDI Dienst, Layer "Sturmschaden Vivian"

en: WMS-FSDI service, layer "Vivian storm damage"

it: Servizio WMS-IFDG, strato "Aree boschive danneggiate Vivian"

de: STAC Browser

fr: Serveur de téléchargement de geo.admin.ch

de: Download Server von geo.admin.ch

en: Download server from geo.admin.ch

it: Server di download di geo.admin.ch

fr: Inventaire de la Confédération recensant les dégâts causés par l’ouragan Vivian en février 1990.Recensement des dégâts étendus en forêt. Critères de recensement: Dégâts étendus: surfaces forestières détruites avec un degré de recouvrement du peuplement restant égal ou inférieur à 0,2 (20%), ce chiffre ne dépend pas du degré de recouvrement avant l’ouragan. Surfaces minimales: 0,2 hectare par surface recensée. Caractéristiques du site: la pente, l’altitude et l’exposition constituent des données forestières importantes et sont relevées pour chaque surfa-ce sinistrée. Unités de relevé et unités d’exploitation: la plus petite unité de relevé est la surface sinis-trée, la plus petite unité d’exploitation est la commune politique. Pour la détermination des communes, les données numériques de swissto-po sont déterminantes.

de: Sturmschadeninventar des Bundes desSturmes Vivian Februar 1990. Erfassung der Totalschadenflächen im Wald. Erfassungsmerkmale: Totalschadenfläche: Zerstörte Waldfläche mit einem maximalen Deckungsgrad der Restbestockung <= 0.2 (20%), unabhängig davon, welchen Deckungsgrad der Wald dort vorher aufwies. Minimalfläche: Pro erfasste Einzelfläche 0.2 Hektaren. Standortsmerkmale: Als wichtige forstliche Grösse werden pro Schadenfläche die Hangneigung, die Höhenlage und die Exposition aufgenommen. Erfassungs- und Auswerteeinheiten: Kleinste Erfassungseinheit ist die Sturmschadenfläche, kleinste Auswerteeinheit die Politische Gemeinde. Für die territoriale Zuordung der beiden Einheiten werden die digitalisierten Gemeindegrenzdaten der L+T übernommen.

en: Federal government inventory of damage caused by “Vivian” in February 1990: database of areas of total damage in forests. Defined criteria and conditions: Area of total damage: Destroyed forest with a maximum vegetation cover of the residual stand of <= 0.2 (20%), regardless of the previous vegetation cover of the forest. Minimal area: 0.2 hectares per recorded area. Location characteristics: For each area of damage, the main characteristics for input were slope steepness, altitude and exposure. Input and evaluation units: The smallest input unit was area of storm damage, while the smallest evaluation unit was the political municipality. The digitised municipal boundary data of the Federal Office of Topography were adopted for the territorial allocation of these two units.

it: Inventario della Confederazione dei danni provocati dall’uragano Vivian del febbraio 1990. Rilevamento dei danni totali subiti dal bosco.Caratteristiche di rilevamento: Aree con danni totali: aree boschive distrutte con un grado di copertura massimo dei popolamenti rimanenti inferiore o pari a 0,2 (20 %), indipendentemente dal grado di copertura che il bosco presentava in precedenza in tale luogo. Superficie minima: per area singola registrata di 0,2 ettari. Caratteristiche locali: le principali dimensioni forestali considerate per la superficie di danno sono l’acclività, l’altitudine e l’esposizione. Unità di rilevamento e valutazione: l’unità minore di rilevamento è la superficie dei danni da uragano, l’unità minore di valutazione è il Comune politico. Per l’assegnazione territoriale di entrambe le unità si considerano i dati digitali di delimitazione di L+T.

fr: Surfaces forestières sinistrées Vivian 1990

de: Waldschadenflächen Vivian 1990

en: Areas of storm damage caused by “Vivian” in 1990

it: Aree boschive danneggiate "Vivian" 1990


de: naturgefahr gefahren umwelt bgdi-bundesgeodaten-infrastruktur geobasisdaten

author: Abteilung Gefahrenprävention, Bundesamt für Umwelt

fr: Map (Preview) Aperçu map.geo.admin.ch

de: Map (Preview) Vorschau map.geo.admin.ch

en: Map (Preview)  map.geo.admin.ch

it: Map (Preview) Previsione map.geo.admin.ch

fr: Service WMS-IFDG, couche "Axe du profil en travers"

de: WMS-BGDI Dienst, Layer "Querprofilachse"

en: WMS-FSDI service, layer "Cross section securing point"

it: Servizio WMS-IFDG, strato "Asse del profilo trasversale"

fr: Service WMTS-IFDG, couche , Layer "Axe du profil en travers"

de: WMTS-BGDI Dienst, Layer "Querprofilachse"

en: WMTS-FSDI service, layer "Cross section securing point"

it: Servizio WMTS-IFDG, strato "Asse del profilo trasversale"

fr: STAC Browser

en: STAC Browser

it: STAC Browser

fr: RESTful API de geo.admin.ch

de: RESTful API von geo.admin.ch

en: RESTful API from geo.admin.ch

it: RESTful API da geo.admin.ch

fr: La carte indique les tronçons qui sont actuellement levés périodiquement sous la direction de l’OFEV. Le levé se fait en général tous les dix ans ou en fonction de grandes crues avec des variations morphologiques significatives. Les buts du levé périodique sont : - identifier en temps utile les tendances morphologiques (approfondissements, atterrissements) et leurs conséquences sur la stabilité des ouvrages de protection contre les crues et les capacités d’écoulement; - préparer des séries temporelles sur un grand nombre d’années concernant les modifications des lits des cours d’eau pour pouvoir quantifier le régime de charriage; - mettre à disposition les données actuelles pour les planifications et les évaluations des dangers. Les données de mensuration sont gérées par la division Prévention des dangers, section Gestion des risques.

de: Die Karte zeigt die Gewässerabschnitte, welche derzeit unter Federführung des BAFU periodisch vermessen werden. Die Vermessung erfolgt in der Regel alle 10 Jahre oder nach grossen Hochwasserereignissen mit bedeutenden morphologischen Veränderungen. Ziele der periodischen Vermessung sind: - die frühzeitige Erkennung morphologischer Trends (Eintiefungen, Auflandungen) und ihrer Auswirkungen auf die Stabilität wasserbaulicher Schutzbauten oder die Abflusskapazität; - die Bereitstellung langjähriger Zeitreihen zu Gerinneveränderungen als Basis zur Quantifizierung des Geschiebehaushalts; - die Bereitstellung aktueller Daten für Planungen und zur Gefahrenbeurteilung. Die Vermessungsdaten werden durch die Abt. Gefahrenprävention, Sektion Risikomanagement verwaltet.

en: The map shows the reaches of watercourses which are currently surveyed periodically under the leadership of the FOEN. They are usually surveyed every 10 years or after major floods which cause significant morphological changes. The aims of periodic surveying are to identify morphological trends (erosion, aggradation) at an early stage as well as their effects on the stability of flood protection structures or discharge capacity, to provide long-term time series on channel changes as a basis for quantifying the bed-load regime, and to provide current data for planning and risk assessment. The survey data is managed by the Risk Management section of the Hazard Prevention Division

it: La carta mostra i segmenti di corsi d'acqua misurati periodicamente sotto la guida dell'UFAM. La misurazione ha luogo di regola ogni dieci anni o in seguito a grandi piene con cambiamenti morfologici significativi. Gli obiettivi della misurazione periodica sono: - il riconoscimento tempestivo di tendenze morfologiche (sprofondamenti, sovralluvionamenti), e delle relative conseguenze per la stabilità delle opere idrauliche per la protezione contro le piene o la capacità di deflusso; - la predisposizione di serie storiche a lungo termine relative a cambiamenti del letto dei fiumi quale base per la quantificazione del bilancio del materiale solido di fondo; - la predisposizione di dati aggiornati per pianificazioni e la valutazione dei pericoli. I dati misurati vengono amministrati dalla Divisione Prevenzione dei pericoli, Sezione Gestione dei rischi.

fr: Division Prévention des dangers, Office fédéral de l'environnement

de: Bundesamt für Umwelt / Abteilung Gefahrenprävention

en: Hazard Prevention Division, Federal Office for the Environment

it: Divisione Prevenzione dei pericoli, Ufficio federale dell'ambiente

bundesamt-fur-umwelt-bafu

de: Bundesamt für Umwelt BAFU

fr: Banque de données des profils d'écoulement (PE) - Axe du profil en travers

de: Datenbank Querprofile (QP) - Querprofilachse

en: Reaches surveyed - cross section securing point

it: Tratti misurati - Asse del profilo trasversale


regi

de: Regionen und Städte

envi

de: Umwelt

author: Bundesamt für Umwelt

fr: Lien vers le portail

en: Link to portal

it: Link per il portale

de: geocat.ch Permalink

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

fr: Service WMS-IFDG, couche "Zones alluviales - Annexe 2"

de: WMS-BGDI Dienst, Layer "Auengebiete - Anhang 2"

en: WMS-FSDI service, layer "Floodplains - Appendix 2"

it: Servizio WMS-IFDG, strato "Zone golenali - Allegato 2"

fr: Service WMTS-IFDG, couche , Layer "Zones alluviales - Annexe 2"

de: WMTS-BGDI Dienst, Layer "Auengebiete - Anhang 2"

en: WMTS-FSDI service, layer "Floodplains - Appendix 2"

it: Servizio WMTS-IFDG, strato "Zone golenali - Allegato 2"

fr: On entend par zones alluviales les zones inondables en bordure de lacs et cours d'eaux, ces zones constituant des biotopes naturels. En mai 1981, le mandat d'établir l'inventaire scientifique des zones alluviales a été donné par le DFI à l'Institut fédéral des recherches forestières (aujourd'hui Institut fédéral de recherches sur la forêt, la neige et le paysage, FNP). Ce mandat a été élaboré par le groupe "Vegetationskunde" (géographie botanique).Conformément à l'article 18a de la loi fédérale du 1er juillet 1966 sur la protection de la nature et du paysage (LPN) - en vigueur depuis le 1er février 1988 - le Conseil fédéral désigne les biotopes d'importance nationale, en détermine la situation et précise les buts visés par la protection. Il ne le fait toutefois qu'après avoir pris l'avis des cantons. Conformément à l'article 18a LPN, le Conseil fédéral a arrêté en 1992 l'entrée en vigueur de l'inventaire fédéral des zones alluviales en tant que deuxième inventaire fédéral. Cet inventaire compte 169 objets, et a été révisé en 2001, 2003, 2007 et 2017. Entre 1995 et 1997, il a été révisé et complété d'un point de vue objectif par l'inventaire des marges proglaciaires et des plaines alluviales alpines. Il compte actuellement 326 objets. De plus, l'annexe 2 comprend 27 objets dont la mise au net n'est pas terminée. Leur protection est soumise à la décision de leur inclusion dans l'annexe 1 en vertu de l'ordonnance sur la protection de la nature et du paysage (OPN) et de l'article 7 de l'ordonnance sur les zones alluviales.

de: Auen sind als natürliche Lebensräume im Überschwemmungsbereich von Gewässern im Rückgang begriffen. Das wissenschaftliche Aueninventar wurde vom EDI im Mai 1981 in Auftrag gegeben und an der eidgenössischen Anstalt für das forstliche Versuchswesen (heute WSL) in der Forschungsgruppe Vegetationskunde erstellt.Gemäss Art 18a des Bundesgesetzes vom 1. Juli 1966 über den Natur- und Heimatschutz (NHG) - in Kraft seit dem 1. Februar 1988 - bezeichnet der Bundesrat die Biotope von nationaler Bedeutung, bestimmt ihre Lage und legt die Schutzziele fest. Dies geschieht jedoch erst nach Anhören der Kantone. Als zweites Bundesinventar gemäss Art. 18a NHG setzte der Bundesrat 1992 das Bundesinventar der Auengebiete mit 169 Objekten in Kraft, welches in den Jahren 2001, 2003, 2007 und 2017 revidiert wurde. Zwischen 1995 und 1997 wurde das Inventar der Gletschervorfelder und alpinen Schwemmebenen (IGLES), als wissenschaftliches Inventar, die Grundlage der 1. Ergänzung erarbeitet. Aktuell sind 326 Objekte in Kraft. Weiter sind im Anhang 2 27 Objekte aufgeführt, deren Perimeter noch nicht definitiv bereinigt sind. Deren Schutz richtet sich bis zum Entscheid ihrer Aufnahme in Anhang 1 nach Artikel 29 Absatz 1 Buchstabe a der Natur- und Heimatschutzverordnung (NHV) und nach Artikel 7 der Auenverordnung.

en: Floodplains, namely natural habitats in the flood zones of watercourses, are in decline. The scientific inventory of floodplains was commissioned by the FDHA in May 1981 and compiled by vegetation research group of the Swiss Federal Institute for Foresty (now FSL). In accordance with Article 18a of the Federal Act of 1 July 1966 on the Protec-tion of Nature and Cultural Heritage (NCHA), which came into force on 1 February 1988, the Federal Council designates biotopes of national importance, assesses their situa-tion and sets conservation objectives for them. However, this occurs after the cantons have been heard. As the second federal inventory under Article 18a NCHA, in 1992 the Federal Council implemented the Federal Inventory of Floodplains with 169 floodplain sites. It has been expanded in 2001, 2003, 2007 and 2017. Between 1995 and 1997, the Inventory of Glacier Forelands and Alpine Floodplains (IGLES), a scientific inventory, developed the bases for the first expansion. Currently 326 objects are contained in the inventory and protected by law.

it: Le zone golenali, biotopi naturali situati nell'area di inondazione dei corsi d'acqua, sono in diminuzione. L'inventario scientifico delle zone golenali è stato commissionato dal DFI nel maggio 1981 e realizzato presso l'Eidgenössische Anstalt für das forstliche Versuchswesen (oggi WSL) nel gruppo di ricerca sulla botanica. Secondo l'articolo 18a della legge federale del 1° luglio 1966 sulla protezione della natura e del paesaggio (LNP), in vigore dal 1° febbraio 1988, il Consiglio federale determina i biotopi d'importanza nazionale e indica gli scopi della protezione, soltanto però dopo aver sentito il parere dei Cantoni. Come secondo inventario federale secondo l'articolo 18a LPN, nel 1992 il Consiglio federale ha approvato l'inventario delle zone golenali con 169 oggetti, completato con tre integrazioni negli anni 2001, 2003 e 2007. Fra il 1995 e il 1997 è stato elaborato l'inventario scientifico delle zone golenali alpine e dei margini proglaciali (IGLES) che è alla base della prima integrazione. Come quarto inventario federale secondo l'articolo 18a LNP, il Consiglio federale nel 2001 ha approvato l'Inventario dei siti di riproduzione degli anfibi con 701 oggetti, che è stato completato negli anni 2003, 2007 e 2017 con due integrazioni e che ora conta 929 oggetti (835 oggetti fissi e 94 mobili). Nell’Allegato 3 sono inoltre riportati altri 30 oggetti i cui perimetri non sono stati ancora definitivamente rettificati. Sino alla decisione relativa al loro inserimento negli Allegati 1 o 2, la loro protezione si basa sull’articolo 29 capoverso 1 lettera a dell’ordinanza sulla protezione della natura e del paesaggio (OPN) e sull’articolo 10 dell’ordinanza sulla protezione dei siti di riproduzione di anfibi di importanza nazionale (OSRA).

fr: Office fédéral de l'environnement / Division Biodiversité et paysage

de: Bundesamt für Umwelt / Abteilung Biodiversität und Landschaft

en: Federal Office for the Environment / Biodiversity and Landscape Division

it: Ufficio federale dell'ambiente / Divisione Biodiversità e paesaggio

fr: Office fédéral de l'environnement OFEV

en: Federal Office for the Environment FOEN

it: Ufficio federale dell'ambiente UFAM

fr: L’autorité compétente pour les questions environnementales. Il est à ce titre chargé de gérer les ressources naturelles selon les principes du développement durable et de protéger l’homme contre les dangers naturels et l’environnement contre les pollutions.

de: Die Fachbehörde für die Umwelt. Es ist zuständig für die nachhaltige Nutzung der natürlichen Ressourcen sowie für den Schutz des Menschen vor Naturgefahren und den Schutz der Umwelt vor übermässigen Belastungen.

en: The federal environmental authority. It is responsible for ensuring that natural resources are used sustainably, that the public is protected against natural hazards, and that the environment is protected from unacceptable adverse impacts.

it: Il servizio specializzato in materia ambientale, è competente per quanto riguarda l'utilizzazione sostenibile delle risorse naturali, la protezione delle persone dai pericoli naturali e dell'ambiente da un inquinamento eccessivo.

fr: Inventaire fédéral des zones alluviales d'importance nationale - Annexe 2

de: Bundesinventar der Auengebiete von nationaler Bedeutung - Anhang 2

en: Federal Inventory of Floodplains of National Importance - Appendix 2

it: Inventario federale delle zone golenali d'importanza nazionale - Allegato 2