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

en: README

media_type: TXT

en: README file explaining the organization of the data

en: Snowmicroquakes Data

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

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

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

compression-experiments

display_name: compression-experiments

microstructure

display_name: microstructure

snow-mechanics

display_name: snow-mechanics

x-ray-tomography

display_name: x-ray-tomography

education

en: Education and science

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: Compressive stick slip and snow-micro-quakes


maintainer: Avalanche Warning Service SLF

state: active

type: dataset

en: Avalanche fatalities per year in Switzerland since 1936/1937

media_type: CSV

format: CSV

mimetype: text/csv

en:
This dataset contains the statistics on the number of avalanche fatalities per hydrological year in Switzerland. The data set commences with the beginning of the hydrological year 1936/37 on 01/10/1936. After the completion of a hydrological year, the new data is appended to the existing dataset.

The following information is contained (by column and column title):
 - hydrological year
 - number of fatalities in the backcountry (=tour)
 - number of fatalities in terrain close to ski areas (=offpiste)
 - 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 definition for these four categories as described in the guidelines to the avalanche accident database:

**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](http://www.geogr-helv.net/71/147/2016/ )):
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](http://www.geogr-helv.net/71/147/2016/ )):
people inside or just outside buildings, and workers on high alpine building
sites

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-accidents

display_name: avalanche-accidents

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

education

fr: Education, science

de: Bildung, Wissenschaft

it: Formazione e scienza

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Number of avalanche fatalities per hydrological year in Switzerland since 1936-1937


automatic-classification-of-avalanches

maintainer: Alec van Herwijnen

title_for_slug: automatic-classification-of-avalanches

en: avalanche seismic machine-learning localization monitoring

media_type: application/x-tar

en:
This tar file contains classification results for two seismic geophone arrays deployed above Davos, Switzerland. The of the first array located at the Dischma Valley was used to automatically identify all seismic events produced by avalanches, whereas the classification perfomed at the second array located at the Wannengrat field site was used to identify airplanes and earthquakes.
Furthermore, this tar file also contains the median back-azimuth of all classified events at the Dischma field site.

format: SERVICE

mimetype: application/x-tar

en: This dataset contains the classification and localization results obtained during the automatic classification of avalanches during the winter season 2017.

avalanche

display_name: avalanche

localization

display_name: localization

machine-learning

display_name: machine-learning

monitoring

display_name: monitoring

seismic

display_name: seismic

en: Automatic Classification of Avalanches


maintainer: EnviDat Support

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

en: Seasonal sub-canopy radiation data

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

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

format: XLS

en: Radiation Data 18.04.2019

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

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

en: Sky-view fraction along the cablecar transect

media_type: text/plain

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

format: TXT

mimetype: text/plain

en: Radiation Data 25.4.2018

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

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

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

forest

display_name: forest

fractional-snow

display_name: fractional-snow

longwave-radiation

display_name: longwave-radiation

shading

display_name: shading

shortwave-radiation

display_name: shortwave-radiation


en: arma-noise transfer-function deconvolution time-series-analysis irf de-mixing irfnnhsr system-identification greens-function nonlinear-deconvolution impulse-response-function

en: README file for IRFnnhs.R and benchmark demonstration scripts

media_type: flat-ascii text

en: Benchmark demonstration scripts and results

media_type: zip-compressed folder of R scripts and tab-delimited text files

en: Impulse Response Function script

media_type: r script (flat-ascii text)

en:
The R script IRFnnhs.R, which efficiently estimates impulse response functions for environmental systems that are nonlinear, nonstationary, or heterogeneous, based on their input and output time series.

Scripts and results for a series of benchmark tests are also provided, to accompany Kirchner, J.W., Impulse response functions for heterogeneous, nonstationary, and nonlinear systems, estimated by deconvolution and demixing of noisy time series, _Sensors_, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022.

arma-noise

display_name: arma-noise

de-mixing

display_name: de-mixing

deconvolution

display_name: deconvolution

greens-function

display_name: greens-function

impulse-response-function

display_name: impulse-response-function

irf

display_name: irf

irfnnhsr

display_name: irfnnhsr

nonlinear-deconvolution

display_name: nonlinear-deconvolution

system-identification

display_name: system-identification

time-series-analysis

display_name: time-series-analysis

transfer-function

display_name: transfer-function

education

en: Education and science

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: Impulse response functions for nonlinear nonstationary and heterogeneous systems


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

en: Post Processed TLS's

media_type: ZIP

en: Compressed dat files

format: ZIP

en: Link to post-processed TLS’s of Antarctic sea ice

media_type: application/zip

mimetype: application/zip

en: Data processing documentation

media_type: DOC

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

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


maintainer: Anita C. Risch

en: native-plant non-native-plant weed-management herbivory grassland

en: Correction: Data file native and non-native plant interactions in Australian grasslands

media_type: XLSX

en:
taxa tab:	list and overview of all the recorded species

* family:                 family of the plant

* genus:                genus of the plant

* species:                plant species name

* origin:                indicates whether the plant is native to Australia or not (non-native)

* common name:	        common name of the plant species

* functional group:	        functional group of the plant species: forb, graminoid, fern, tree

* functional group detailed:	gives more detail about the type of graminoid and forb: legume, forb (non-leguminous),
                        grass, sedge, rush, fern, tree

* lifeform:                annual or perennial lifeform (species which can have both of these lifeforms where
                        considered as annuals)

plot tab:	list of species found in each plot and their cover (%)

* sampling.session:        provides information about the sampling time:
                        May2020 = data that was collected in May 2020
                        Nov2020 = data that was collected in November 2020

* farm.name:	            name of the farms where the sampling took place:
                        SF = farm of the Southern group
                        NF = farm of the Northern group
                        numbers 1-3 = randomly allocated to the Southern group of farms
                        numbers 4-6 = randomly allocated to the Northern group of farms

* site.type:	                KG: Kangaroo grass dominated site
                        ALG: ALG+KG site (Kangarooo grass and African lovegrass co-dominated site)

* plot.no:                plot number: 9-12

* scientific.name:	        scientific name of the plant species found in a certain plot

* functional group:	        indicates whether the species found is a tree, forb, graminoid or fern

* functional group detailed:	indicates whether the species found is a tree, legume, forb (non-leguminous),
                        grass, sedge, rush, fern

* lifeform:                specifies the lifeform of the species: annual or perennial lifeform
                        (species which can have both of these lifeforms where considered as annuals)

corrections tab:	Detailed description of corrections made in plot.data tab

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

Paper Citation:

_Schlierenzauer, C., Risch, A.C., Schütz, M., Firn, J. 2021. Non-native Eragrostis curvula reduces plant species diversity in pastures of South-eastern Australia even when native Themeda triandra remains co-dominant. Plants 10, 596._

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

Study area
The study was conducted in the lowland grassy woodlands of the Bega Valley Region, which is located in the south-east corner of New South Wales, Australia. Embedded between the Pacific Ocean and the Australian Alps, the lowland grassy woodlands are mostly located on granitic substrates and reach elevations of roughly 500 m above sea level. Typically, these grassy woodlands receive less precipitation (mean annual precipitation between 700-1100 mm) compared to the more elevated areas that surround them (NSW Government - Office of Environment and Heritage 2017).
The vegetation is dominated by an open tree canopy layer consisting of Eucalyptus tereticornis Sm, Angophora floribunda Sm. (Sweet) and a range of other eucalypt species. Sometimes shrub or small trees are also present, whereas grasses and forbs form the ground-cover. In areas without intensive agricultural history, this layer is dominated by perennial, tussock grasses such as Themeda triandra Forssk, Microlaena stipoides R.Br (Weeping Grass), Eragrostis leptostachya Steud. (Paddock Lovegrass) and Echinopogon ovatus P.Beauv (Forest Hedgehog Grass). The remaining inter-tussock spaces are occupied by a diversity of growth-restricted grasses and herbaceous forbs (NSW - Department of Planing, Industries and Environment 2019; NSW Government - Office of Environment and Heritage 2017).
Clearing, pasture sowing, fertilizer application and livestock grazing resulted in a dramatic decrease in the extent of these natural woodlands, with less than five percent within conservation reserves and overall, with only about 20% of their original extent in New South Wales still existing (Tozer et al. 2010). The remaining areas outside of reserves are threatened by altered fire frequencies, habitat clearing, livestock grazing and especially by non-native plant invasion, particularly Eragrostis curvula (Schrad.) Nees. For this reason, the grassy woodlands are listed as an endangered ecological community in the NSW state legislation. Additionally, they are considered as critically endangered by the Commonwealth of Australia (Threatened Species Scientific Committee (TSSC) 2013).

Experimental design and sampling
The study was conducted on six farms and in each of them two sites were chosen, representing a paired design. One of the sites at each farm is dominated by native Themeda triandra, the other one co-dominated by non-native Eragrostis curvula and Themeda triandra. All farms are within a radius of approximately 10 km from the town Candelo. Three of the farms are located North (36°40’ to 36°42’ S and 149°38’ to 149°42’ E) and three of them are located South (36°51’ to 36°49’ S and 149°38’ to 149°42’ E) of Candelo.
Non-native herbivores (mainly cattle, sheep and rabbits) and native herbivorous marsupials (mainly kangaroos, wallabies and wombats) are present in the area of these sites. On each site, data was collected within four plots (each 1 x 1 m) in May and November 2020. All plant species found within a plot were recorded and their relative abundance was estimated.

References
NSW - Department of Planing, Industries and Environment. 2019. “Lowland Grassy Woodland in the South East Corner Bioregion - Endangered Ecological Community Listing.” https://www.environment.nsw.gov.au/topics/animals-and-plants/threatened-species/nsw-threatened-species-scientific-committee/determinations/final-determinations/2004-2007/lowland-grassy-woodland-south-east-corner-bioregion-endangered-ecological-community-l (February 18, 2021).
NSW Government - Office of Environment and Heritage. 2017. “Lowland Grassy Woodland in the South East Corner Bioregion - Profile.” https://www.environment.nsw.gov.au/threatenedSpeciesApp/profile.aspx?id=20070 (January 31, 2021).
Threatened Species Scientific Committee (TSSC). 2013. Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) Conservation Advice for Lowland Grassy Woodland in the South East Corner Bioregion. http://www.environment.gov.au/biodiversity/threatened/communities/pubs/82-conservation-advice.pdf.
Tozer, Mark et al. 2010. “Native Vegetation of Southeast NSW: A Revised Classification and Map for the Coast and Eastern Tablelands.” Cunninghamia : a journal of plant ecology for eastern Australia 11(3): 359–406.

grassland

display_name: grassland

herbivory

display_name: herbivory

native-plant

display_name: native-plant

non-native-plant

display_name: non-native-plant

weed-management

display_name: weed-management

en: Native and no-native plant interactions in Australian grasslands


maintainer: Matthias Haeni

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

en: Data plot of the forest station (BAB)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Field station data (BAF)

media_type: text/csv

en: Data file.

en: Site aerial photo (Beatenberg)

media_type: image/jpeg

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

mimetype: image/jpeg

en: Data plot of the field station (BAF)

en: Site photo of the field station (BAF)

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

en: Forest station data (BAB)

en: Site photo of the forest station (BAB)

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

en:
High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Beatenberg in Switzerland where one station is located within a natural coniferous forest (BAB) with Norway spruce (_Picea abies_; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Beatenberg is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the
Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


maintainer: Giulia Francesca Azzurra Donati

en: urban-spatial-planning ecological-corridors circuit-theory biodiversity human-dominated-landscapes integrated-modelling

en: Amphibian whole life cycle predictors - 10m resolution

media_type: zip-compressed folder of ascii files

en:
Amphibian whole life cycle predictors include 6 variable types:
(1)	Topographic – slope [°]: derived from the Swiss Topographical Landscape model (swissTLM 3D 2015) at 2 m resolution (elevation uncertainty ± 0.5m) and, subsequently, aggregated to a median value;
(2)	Hydrologic: two predictors including nearest distance to water [m] (all water-related objects and rasterized lotic, lentic and reedbeds from the regional cadastral maps – AV: Daten der Amtliche Vermessung) and runoff coefficient [C] (between 0 and 1 relating amount of runoff to amount of precipitation, mapped AV: Daten der Amtliche Vermessung data against literature values for different land covers);
(3)	Edaphic – soil moisture variability (not available): can gathered from the spatial modelling of Ecological Indicator Values in Switzerland (Descombes et al., 2020) and resampled from 100m to 10m;
(4)	Vegetation: we calculated the median and standard deviation of the Normalized Difference Vegetation Index (NDVI) from all available cloud free Sentinel-2 pixels available between April and October for the years 2016 to 2019 (Drusch et al., 2012) and the median vegetation height [m] (not available) can gathered from Ginzler and Hobi (2015);
(5)	Land-use derived: includes three predictors, grassland density [grassland/cell] (not available) - can be gathered from Pazur et al. (2021), total grasslands area evaluated using a spatial weight matrix and a 250m neighborhood radius around each cell; urbanization [buildings/cell] – average building density for a 250m neighborhood around each cell based on the AV: Daten der Amtliche Vermessung ; and traffic intensity [vehicles/day] – average daily vehicle count (Nationales Personenverkehrsmodells);
(6)	Movement-ecology related: includes three predictors of proximity – nearest forest distance [m]; nearest rock-gravel and sandy area (RGS) distance [m] and nearest road distance [m] (considering any road type) – all calculated based on the AV: Daten der Amtliche Vermessung maps.

en: Amphibian monitoring data

media_type: .csv

en: Processed amphibian occurrence data (2017-2019) in the cantons of Aargau and Zürich. The dataset includes information for the following species: _Alytes obstetricans_,_Bombina variegata_, _Bufo bufo_, _Hyla arborea_, _Ichtyosaura alpestris_, _Lissotriton helveticus_, _Lissotriton vulgaris_, _Rana temporaria_, _Salamandra salamandra_, and _Triturus cristatus_.

en: Amphibian breeding sites of national importance - cantons of Aargau and Zürich

media_type: .asc

en: Amphibian breeding sites of national importance (focal nodes) in the cantons of Aargau and Zürich, 30x30 m resolution, binarised ascii format.

en: Binarized "green" and "grey" landcover data (Cantons of Aargau and Zürich)

en: "Green" landcover refers to lawns and vegetated urban surfaces such as, for example private gardens, public parks, cemeteries, green stripes along roadsides, etc. While "grey" landcover refers to impervious grounds such as roads, troittoirs, other impervious. Binarized maps, 30x30m resolution.

en: Amphibian species specific landscape resistance maps.

en: For each amphibian species we used the weighted average Ensemble model suitability map and converted it to a resistance map using an exponential decay function following (Duflot et al., 2018) using the species-specific mpa threshold previously computed (Preau et al., 2020). The function assigns a resistance value of 1,000 when habitat suitability equals zero, and a value of one when habitat suitability is greater than or equal to the binarization threshold. The exponential shape strengthens the barrier effect of less favorable areas.

en: The data includes (1) amphibian occurrence data (2017-2019) for ten species across the cantons of Aargau and Zürich gathered from the Coordination Center for the Protection of Amphibians and Reptiles of Switzerland (http://www.karch.ch), (2) amphibian whole-life cycle environmental predictors (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related), and (3) local urban "green" and "grey" landcover data which can be used to identify opportunities for Blue-Green Infrastructure (through green or grey transitions) in support of regional landscape connectivity.

biodiversity

display_name: biodiversity

circuit-theory

display_name: circuit-theory

ecological-corridors

display_name: ecological-corridors

human-dominated-landscapes

display_name: human-dominated-landscapes

integrated-modelling

display_name: integrated-modelling

urban-spatial-planning

display_name: urban-spatial-planning

education

en: Education and science

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: Amphibian and urban-rural landscape data Swiss Lowlands


en: incoming weissfluhjoch outgoing longwave-radiation shortwave-radiation

en: DATA ACCESS

en: Metadata

en: Processing doc

en: Parameter file

en:
Corrected incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch summit, Davos, CH.

 References
1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.
2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.

incoming

display_name: incoming

longwave-radiation

display_name: longwave-radiation

outgoing

display_name: outgoing

shortwave-radiation

display_name: shortwave-radiation

weissfluhjoch

display_name: weissfluhjoch

en: ASRB_WFJ: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos


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

en: Site photo of the forest station (NOB)

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

en: Site aerial photo (Novaggio)

en: Site aerial photo from both Novaggio stations. Source: Google. 2016. Site aerial photo from both Novaggio stations. Retrieved November 15, 2016, from https://www.google.ch/maps

en: Site photo of the field station (NOF)

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

en: Field station data (NOF)

en: Data file.

en: Data plot of the forest station (NOB)

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

en: Forest station data (NOB)

en: Data plot of the field station (NOF)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Novaggio in Switzerland where one station is located within a natural deciduous forest stand (NOB) with Turkey oak (_Quercus cerris_; 70 yrs), sweet chestnut (_Castanea sativa_; 90 yrs) and silver birch (_Betula pendula_; 70 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Novaggio is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

en: Novaggio, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards


maintainer: Leonie Schönbeck

en: vpd photosynthesis fluorescence drought pinus-sylvestris gas-exchange acclimation

en: Methods ACi curves

en:
Parameters and methods for CO2 response curves of Photosynthesis.

Metatdata format by
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en: Methods fluorescence

en:
Parameters and methodology for Fluorescence measurements

Metadata format from
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en: Methods light response

en: Methods and parameters for light response curves

en: Methods diurnal measurements

en:
Parameters and methodology for diurnal measurements

Metadata format from
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en: Methods survey

en: Parameters and methods for point measurements / survey measurements

en: Variable names and units

en: Variable names, description and units for the Pfynwald Gas exchange data

en: Instrument details

en:
Instrument details for all LiCor instruments used during collection of dataset.

Metatdata created following guidelines from
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en: Pfynwald gas exchange

en:
Full dataset of Pfynwald gas exchange data taken in the period from June 2013 to October 2020.
All metdata can be found in attached documents.
Data uploaded after quality control using guidelines by Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en:
Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds.
All metadata can be found in the attached documents.

acclimation

display_name: acclimation

drought

display_name: drought

fluorescence

display_name: fluorescence

gas-exchange

display_name: gas-exchange

photosynthesis

display_name: photosynthesis

pinus-sylvestris

display_name: pinus-sylvestris

vpd

display_name: vpd

education

en: Education and science

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: 2013-2020 gas exchange at Pfynwald


maintainer: Marcia Phillips

en: meteo-station permafrost ritigraben borehole permos

en: DATA ACCESS

media_type: SMET

en: Meterological station at the [Ritigraben permafrost borehole](http://www.envidat.ch/dataset/rit1) (RIT_0102) in canton Valais, Switzerland. The station is located at 2690 m asl on a flat site.

borehole

display_name: borehole

meteo-station

display_name: meteo-station

permafrost

display_name: permafrost

permos

display_name: permos

ritigraben

display_name: ritigraben

en: RIT2: Meteorological station at Ritigraben borehole site


en: cable-yarding digital-elevation-model digital-planning swiss-geo-downloader geoinformation-system qgis-plugin seilaplan open-source swiss-geodata

en: View video on youtube!

en: Herunterladen des DHM mit SwissGeoDownloader

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. The plugin ‘Swiss Geo Downloader’, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model ‘swissALTI3D’ with the help of the ‘Swiss Geo Downloader’ and how to use it for digital planning of a cable line with the plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link to Seilaplan website: https://seilaplan.wsl.ch

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

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Das Plugin Swiss Geo Downloader, welches für das Open Source Geoinformationssystem QGIS zur Verfügung steht, ermöglicht frei verfügbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgenössischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue Höhenmodell swissALTI3D herunterladen und für die Seillinienplanung mit dem Plugin Seilaplan verwenden kann.

Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geo-downloader

display_name: swiss-geo-downloader

swiss-geodata

display_name: swiss-geodata

en: Seilaplan Tutorial: DTM download with SwissGeoDownloader


maintainer: Frank Techel

en: observations avalanches alps shapefile snow

en: Selected wet snow avalanche activity data Davos, Switzerland (2011-2014)

en: Zip archive with shapefiles for the selected wet snow avalanche polygons, including metadata.

en:
Polygons of wet snow avalanches in the Davos area, as documented by the Swiss avalanche warning service. The georeferenced outlines of the avalanches contain both the release as well as the deposit area, but without separating between both.

The dataset is a subset of the total record of 1615 avalanches classified as wet snow avalanches from October 2011 - September 2014, containing those 255 avalanches exceeding 0.0125 km^2. Every polygon comes with meta data, including the date of occurrence.

This dataset is the underlying dataset to:
Wever, N., Vera Valero, C. and Techel, F. (2018) _Coupled snow cover and avalanche dynamics simulations to evaluate wet snow avalanche activity_. Submitted to J. Geophys. Res., in review.

alps

display_name: alps

avalanches

display_name: avalanches

observations

display_name: observations

shapefile

display_name: shapefile

snow

display_name: snow


maintainer: Benjamin Reuter

en: avalanche-problem-type snow-cover-modeling snow-instability snowpack avalanche snow avalanche-danger avalanche-activity weissfluhjoch crocus

en: 2015-2016 Avalanche problem types from Crocus

en:
Avalanche problem types were derived from a snow cover simulation with the model Crocus for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

en: 1999-2017 Avalanche problem types from Crocus

en:
Avalanche problem types were derived from snow cover simulations with the model Crocus at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are presented in Figure 8 in Reuter et al. (2021).

en: 2015-2016 Avalanche problem types from SNOWPACK

en:
Avalanche problem types were derived from a snow cover simulation with the model SNOWAPCK for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

en: 1999-2017 Avalanche problem types from SNOWPACK

en:
Avalanche problem types were derived from snow cover simulations with the model SNOWPACK at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are shown in Figure 8 in Reuter et al. (2021).

en: 2015-2016 Seismic avalanche acitivity catalogue

en:
Avalanches were indentified in seismic data recorded by two seismic arrays, one deployed at Wannengrat (2,500 m) and another one deployed in the Dischma valley (2,000 m).
The data cover the period from 01.01.2016 - 30.04.2016 and are presented in Figure 7 in Reuter et al. (2021).

en:
Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016.
Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

avalanche-danger

display_name: avalanche-danger

avalanche-problem-type

display_name: avalanche-problem-type

crocus

display_name: crocus

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

weissfluhjoch

display_name: weissfluhjoch

education

en: Education and science

en: Simulated avalanche problem types and seismic avalanche activity around Weissfluhjoch


en: groundwater alptal streamflow runoff stable-water-isotopes

en: Deuterium rainfall event C

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

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

en: Precipitation and discharge event A

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

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

en: Deuterium rainfall extra event

en: Precipitation and discharge event C

en: Deuterium streamwater event A

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

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

en: Precipitation and discharge extra event

en: Precipitation and discharge event B

en: Deuterium rainfall event A

en: Deuterium groundwater and baseflow October 2016

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

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

en: Deuterium groundwater and baseflow August 2017

en: Deuterium streamwater extra event

en: Deuterium streamwater event C

en: Deuterium streamwater event B

en: Deuterium groundwater and baseflow October 2017

en: Deuterium rainfall event B

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

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

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

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

alptal

display_name: alptal

envidat

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

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


maintainer: Athanasios Nenes

en: sigma-w dmt-ccn-chamber cloud-modeling cloud-droplets supersaturation raclets hygroscopicity weissfluhjoch ccn

en: CCN data Weissfluhjoch

en:
In-situ CCN measurements were conducted between February 24 and March 8 2019 at Weissfluhjoch. The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* Filtered CCN number concentrations (in cmˉ³) measured at 6 discrete supersaturations (SS) ranging from 0.09% to 0.74%. The name of each CCN column is indicative of the SS developed within the instrument.
* Instrument characteristics such as sample temperature (in °C) and pressure (in mbar)
* A code that refers to the reason of filtering.

Missing or filtered CCN values are indicated by "-999", while a declaration of each "Error_code" can be found in the following list:

_Error_code_:

0000: Measurements without error

1000: Period of transient operation after a supersaturation change

2000: Sharp change (decrease) in the room temperature housing the instrument, which induces a reset in column temperature

3000: Irregular values in the room temperature housing the instrument

9999: Missing data

en: Predicted cloud droplet number concentrations Weissfluhjoch

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Weissfluhjoch station between February 24 and March 8 2019. For this, a hygroscopicity parameter of 0.25 is assumed, according to the CCN-derived values. The data file contains:

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

en: Hygroscopicity parameter Weissfluhjoch

en:
The aerosol hygroscopicity parameter is derived combining the CCN and SMPS size distribution data collected between February 24 and March 8 2019 at Weissfluhjoch. The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The instrument supersaturation (SS%)
* The calculated hygroscopicity parameter (kappa)
* The calculated critical dry diameter of the particles (Dcr) in nm.

Missing values are indicated by "-999".

en:
Cloud Condensation Nuclei (CCN) data: A Droplet Measurement Technologies (DMT) single-column continuous-flow streamwise thermal gradient chamber (CFSTGC; Roberts and Nenes, 2005) was deployed at the measurement site Weissfluhjoch (2700 m a.s.l., LON: 9.806475, LAT: 46.832964) to record the in-situ CCN number concentrations between February 24 and March 8 2019 for different supersaturations (SS). To account for the difference between the ambient (~735 mbar) and the calibration pressure (~800 mbar), the SS reported by the instrument is adjusted by a factor of 0.92. The CFSTGC was cycled between 6 discrete SS values ranging from 0.09% to 0.74%, producing a full CCN spectrum every hour. The raw CCN measurements are filtered to discount periods of transient operation and whenever the room temperature housing the instrument changed sufficiently to induce a reset in column temperature. Additional information can be found in Section 2.1.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

Hygroscopicity data: The CCN number concentration measurements were directly related to the size distribution and total aerosol concentration data measured by the Scanning Mobility Particle Size Spectrometer (SMPS) instrument at the same station (https://www.envidat.ch/dataset/aerosol-data-weissfluhjoch) to infer the particles hygroscopicity parameter (kappa). For each SMPS scan, the particles critical dry diameter (Dcr) is estimated by integrating backward the SMPS size distribution, until the aerosol number matches the CCN concentration observed for the same time period as the SMPS scan. Assuming the particle chemical composition is internally mixed, the kappa is determined from Dcr and SS, applying Köhler theory. Additional information can be found in Section 2.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

Predicted cloud droplet numbers: Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from the SMPS instrument deployed at Weissfluhjoch. The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at Davos Wolfgang and are extracted for the altitude of interest, being 1100 m above ground level for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

ccn

display_name: ccn

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

dmt-ccn-chamber

display_name: dmt-ccn-chamber

hygroscopicity

display_name: hygroscopicity

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

education

en: Education and science

en: EnviDat: the Environmental Data Portal

en: CCN, hygroscopicity, predicted cloud droplet numbers Weissfluhjoch


radar-wind-profiler-davos-wolfgang

maintainer: Maxim Hervo

title_for_slug: radar-wind-profiler-davos-wolfgang

en: raclets wolfgangpass wind-speed radar wind-direction

en: Radar Wind Profiler Data

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

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

radar

display_name: radar

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wolfgangpass

display_name: wolfgangpass

en: RADAR Wind profiler Davos Wolfgang


maintainer: Achille Capelli

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

en: Snow and meteo data from automatic sensors for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: Manual profiles

media_type: .zip

en: Data of the traditional manual profiles for the locations Weissfluhjoch, Klosters and Küblis. For the location of Küblis there are no traditional manual profile data. We provided the data as both PDF and .CAAML format (See https://niviz.org/ or http://caaml.org/Schemas/V5.0/Profiles/SnowProfileIACS/CAAMLv5_SnowProfileIACS.pdf). Moreover, we added LWC and density profiles when available (all locations).

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

en: Daily data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: GNSS derived data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: GNSS derived SWE, LWC and HS from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-20

en: Daily observer data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: GNSS derived SWE, HS, and LWC for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

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

en: Webcam images

en: Webcam images showing some typical differences in the snow conditions at the four sites (Küblis, Klosters, Laret and Weissfluhjoch) for two time periods (14-18.3.2019 and 27.1-11.2.2020). Both time periods included a precipitation event with partial rain-on–snow occurring at the lower locations.

en: Daily data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: GNSS derived SWE, LWC and HS from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-20

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

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

en: Snow profiles data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

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

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

en: Snow and meteo data from automatic sensors for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Profile data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

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

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

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

gnss

display_name: gnss

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

lwc

display_name: lwc

snow

display_name: snow

envidat

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

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


restoring-grassland-multifunctionality

title_for_slug: restoring-grassland-multifunctionality

en: fungi soil-biodiversity grassland bacteria mineralization biodiversity nematodes restoration ecosystem-multifunctionality plants

en: Please note that C.sto and Corg have been corrected.

en: Please note that EMF, MF.below, MF.abio and Soil.hetero have been corrected.

en: Functional diversity above_belowground fauna

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

Resch, M. C., Schütz, M., Buchmann, N., Frey, B., Graf, U., van der Putten, W. H., Zimmermann, S., Risch, A. C. 2021. Evaluating long-term success in grassland restoration – an ecosystem multifunctionality approach. Ecological Applications 31, e02271.

 Study area

The study was conducted in the Canton of Zurich, Switzerland, in and around two nature reserves Eigental and Altläufe der Glatt (47°27’ to 47°29’ N, 8°37’ to 8°32’ E, 417 to 572 m a.s.l.). All studied grasslands were located with a radius of approximately 4 km. Average monthly temperatures range from 0.7 ± 2.0 °C (January) to 19.0 ± 1.5 °C (July), and monthly precipitation range from 60 ± 42 mm (January) to 118 ± 46 mm (July [maxima]; 1989-2017; MeteoSchweiz 2018). In our study, we focused on semi-dry and semi-wet oligo- to mesotrophic grasslands characterized by high plant species richness and groundwater fluctuations throughout the year (Delarze et al. 2015, see also Resch et al. 2019).

 Experimental design and sampling

A large-scale restoration experiment to expand and reconnect isolated remnants of species-rich grasslands was initiated in the nature reserve Eigental in 1990. Twenty hectares of adjacent intensive grasslands were chosen for restoration. In 1995, three restoration methods of increasing intervention intensities were implemented. The goal of all three methods was to lower the availability of soil nutrients and hence, facilitate ecosystem development towards the targeted nutrient-poor grasslands. These methods were: Harvest only (hay harvest twice a year), Topsoil (removal of the nutrient-rich topsoil), and Topsoil+Propagules (topsoil removal combined with the application of hay from target vegetation). Plant biomass harvest (once a year in late summer/early autumn) commenced in Topsoil and Topsoil+Propagules five years after the soils were removed and is still ongoing today. We measured restoration success by comparing the three restoration methods with intensively managed (Initial) and semi-natural grasslands (Target) 22 years after restoration. Initial grassland sites share the same agricultural history as the restored sites: mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes (Resch et al. 2019). Target sites were the sites from which hay for seeding the Topsoil+Propagules sites was collected. Soil conditions (i.e., soil types, soil texture) were comparable to those found in the restored grasslands (Resch et al. 2019). Additionally, Target sites were selected to represent a variety of semi-natural grasslands, including semi-dry to semi-wet conditions. In Target grasslands, biomass is harvested once a year in late summer or early autumn. Eleven 5 m x 5 m (25 m2) plots were randomly established in each of the five treatments (in total 55 plots; for a detailed map see Neff et al. 2020). An additional 2 m x 2 m (4 m2) subplot was randomly established at least 2 m away from each 25 m2 plot for destructive sampling. Data sampling took place between June and September 2017.

Vegetation properties

All plant species were identified within the 25 m2 plots (nomenclature: Lauber and Wagner 1996) in mid-June 2017 (in total 250 species). Vegetation structure and plant biomass were assessed diagonally on a transect of 2 m x 10 cm within the 25 m2 plot in early July 2017. We measured the maximum and mean height of the vegetation at the start, middle and end of the transect and calculated the standard deviation of these measures to describe vegetation structural heterogeneity (Schuldt et al. 2019). Thereafter, biomass was clipped on the entire transect to 1 cm height, sorted into five functional groups (graminoids, forbs, legumes, litter, and woody species), dried at 60 °C for 48 h, and weighed (Meyer et al. 2015).

 Aboveground arthropods

Aboveground arthropods were sampled at two locations in each 25 m2 plot in early July 2017 (see also Neff et al. 2020). Briefly, two cylindrical baskets (50 cm diameter, 67 cm height; woven fabric) were thrown simultaneously from outside the plot into two opposite corners. A closable mosquito mesh sleeve was mounted to the top of the baskets and an integrated metal ring at the bottom was fixed to the ground with metal stakes to assure that insects could not escape. A suction sampler (Vortis, Burkhard Manufacturing Co. Ltd., Hertfordshire, England) was then inserted into one of the baskets through the opening of the sleeve and the plot was “vacuumed" twice for 105 seconds with a 30 seconds break. The collected animals were immediately transferred into 70% ethanol. Arthropods were sorted and assigned to 23 taxonomic groups. Holometabolic larvae were lumped into one category while hemimetabolic larvae were grouped separately from adults in the respective taxonomic rank. We used mean values of individuals per plot for total abundance. Aboveground arthropod richness was defined by the number of different taxa to lowest taxonomic level (in total 23 taxa). All taxa were assigned to one of five trophic levels: 1) primary producers, 2) primary consumers, 3) secondary consumers, 4) tertiary consumers, and 5) quaternary consumers.

 Belowground fauna

Sampling of all belowground fauna took place in mid-July 2017. Earthworms were sampled in two 30 cm x 30 cm x 20 cm soil monoliths at two opposite corners of the 25 m2 plot (opposite to aboveground arthropod sampling). The excavated soil monolith was broken by hand, all earthworms collected and immediately transferred in a 4% formaldehyde solution. Thereafter, earthworm individuals were identified to species level (in total 10 taxa; Christian and Zicsi 1999) and species assigned to three functional groups (Bouché 1977).
To assess soil arthropod communities, we randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) in each 4 m2 subplot with a slide hammer corer lined with a plastic sleeve (AMS Samplers, American Falls, Idaho, USA). Soil arthropods were extracted using Berlese-Tullgren funnels (3 mm mesh), starting the day of sampling and lasting 14 days. Individuals were stored in 70% ethanol. Soil arthropods were assigned to 41 taxonomic groups and 4 feeding types. Holometabolic and hemimetabolic larvae were treated as previously described for aboveground arthropods. Belowground arthropod richness refers to the 41 taxonomic groups.

For soil nematode sampling, we randomly collected eight soil cores of 2.2 cm diameter (Giddings Machine Company, Windsor, CO, USA) within each 4 m2 subplot to a depth of 12 cm. The eight cores were combined, gently homogenized, placed in coolers, kept at 4 °C and transported to the laboratory at NIOO in Wageningen (NL) within one week after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink 1960) and prepared for morphological identification and quantification as described by Resch et al. (2019). Nematodes were identified to family level (39 taxa) according to Bongers (1988), assigned to 17 functional groups, 5 feeding types and 5 colonizer-persister (C-P) classes (Yeates et al. 1993, Bongers 1990, Resch et al. 2019).

We randomly collected two more soil cores (2.2 cm diameter x 12 cm depth) within each 4 m2 subplot to determine soil microbial communities. Again, the soil cores were combined, homogenized, placed in coolers and transported to the laboratory at WSL in Birmensdorf (Switzerland) where the metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNeasy PowerMax Soil Kit (Quiagen, Hilden, NRW, GER) according to the manufacturer`s instructions. PCR amplification of the V3-V4 region of the prokaryotic small-subunit (16S) and the ribosomal internal transcribed spacer region (ITS2) of eukaryotes was performed with 1 ng of template DNA utilizing PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates and pooled. The pooled amplicons were sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Quality filtering, clustering into operational taxonomic units (OTUs) and taxonomic assignment were performed as described by Frey et al. (2016) and Adamczyk et al. (2019). We used a customised pipeline largely based on UPARSE (Edgar 2013) implemented in USEARCH v. 9.2 (Edgar 2010). After discarding singletons of dereplicated sequences, clustering into OTUs with 97% sequence similarity was performed (Edgar 2013). Quality-filtered reads were mapped on the filtered set of centroid sequences. Taxonomic classification of prokaryotic and fungal sequences was conducted querying against most recent versions of SILVA (v.132, Quast et al. 2013) and UNITE (v.8, Nilsson et al. 2018). Only taxonomic assignments with confidence rankings equal or higher than 0.8 were accepted (assignments below 0.8 set to unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as eukaryotic OTUs assigned other than fungi were removed prior to data analysis. In addition, prokaryotic and fungal datasets were filtered to discard singletons and doubletons. Thereafter, OTU abundance matrices were rarefied to the lowest number of sequences per community, to normalize the total number of reads and achieve parity between samples (Prokaryota: 29,843 reads; Fungi: 26,690 reads). Finally, prokaryotic and fungal observed richness (number of OTUs) were estimated (Prokaryota: 14,010 OTUs; Fungi: 5,813 OTUs). For prokaryotes, we distinguished five and for fungi six functional types based on lowest taxonomic resolution (Nguyen et al. 2016, Tedersoo et al. 2014). Belowground taxon richness was defined by the total number of earthworm, arthropod, nematode, fungi, and prokaryote taxa assigned to lowest taxonomic level. Finally, all belowground taxa were assigned to the same five trophic levels as the aboveground arthropods.

 Soil chemical and physical properties, soil nitrogen mineralization

We randomly collected three 5 cm diameter x 12 cm depth soil samples in each 4 m2 subplot with a slide hammer corer (AMS Samplers, American Falls, Idaho, USA), pooled them and then made two subsamples. One was field-fresh and stored at 3 °C until analysis, the other was dried for 48 h at 60 °C and passed through a 4 mm mesh. From the dried sample, we measured soil pH potentiometrically in 0.01 M CaCl2 (soil:solution ratio=1:2; 30 minutes equilibration time). Total and organic carbon content were measured on fine-ground samples (≤ 0.5 mm) by dry combustion using a CN analyzer NC 2500 (CE Instruments, Wigan, United Kingdom). Inorganic carbon of samples with a pH > 6.5 was removed with acid vapor prior to analysis of organic carbon (Walthert et al. 2010). We calculated total soil carbon (C) storage after correcting its content for soil depth, stone content and density of fine earth (see below). Exchangeable cations were determined on another 5 g dry soil sample with 50 mL unbuffered 1 M NH4Cl solution (soil:solution ratio=1:10, end-over-end shaker for 1.5 hours) and measured by an ICP-OES (Optima 7300 DV, Perkin-Elmer, Waltham, Massachusetts, USA). Thereafter, cation exchange capacity (CEC) was calculated as the sum of exchangeable cations and protons (and expressed as mmolc per 1 kg soil) and used to describe nutrient retention capacity in our plots. Concentrations of exchangeable protons were calculated as the difference between total and Al-induced exchangeable acidity as determined by the KCl-method (Thomas 1982).

Ammonium (NH4+) and nitrate (NO3−) were extracted from a 20 g fresh subsample with 80 mL 1M KCl for 1.5 hours on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnemühle FineArt GmbH, Dassel, Germany). NH4+ concentrations were determined colorimetrically by automated flow injection analysis (FIAS 300, Perkin-Elmer, Waltham, Massachusetts, USA). NO3− concentrations were measured colorimetrically according to Norman and Stucki (1981). Potential soil net nitrogen (N) mineralization was assessed during an 8-week incubation period under controlled moisture (60% of field capacity), temperature (20 °C) and light conditions (dark) in the laboratory. We weighed duplicate samples of fresh soil equivalent to 8 g dry soil (24 h at 104 °C) into 50 mL Falcon tubes. Soil samples were extracted for NH4+ and NO3− at the beginning and after eight weeks as described above. Soil net N mineralization was calculated as the difference between the inorganic nitrogen (NH4+ and NO3−) before and after the incubation (Hart et al. 1994), corrected for the total incubation time and represented per day values expressed as mg N kg-1 soil d-1.

To assess soil physical properties, we randomly collected one undisturbed soil core per 4 m2 subplot (5 cm diameter, 12 cm depth) in a steel cylinder that fit into the slide hammer (AMS Samplers, American Falls, Idaho, USA). The cylinder was capped in the field to avoid disturbance. We then measured field capacity in the laboratory. For this purpose, the cylinder and soil therein were saturated in a water bath and drained on a sand/silt-bed with a suction corresponding to 60 cm hydrostatic head. The moist soil was dried at 105 °C to constant weight. Field capacity was calculated by dividing the mass of water by the total mass of wet soil contained at 60 cm hydrostatic head and used to describe water holding capacity. Thereafter, samples were passed through a 4 mm mesh. Fine-earth and skeleton fractions were weighed separately to assess bulk soil density (fine-earth plus skeleton), density of fine earth, and proportion of skeleton. Particle density was determined with the pycnometer method (Blake and Hartge 1986), and total porosity and proportion of fine pores were calculated (Danielson and Sutherland 1986). Clay, silt, and sand contents were quantified with the sediment method (Gee and Bauder 1986).

Surface and soil temperature (12 cm depth, water-resistant digital pocket thermometer; IP65, H-B Instrument, Trappe, Pennsylvania, USA) as well as volumetric soil moisture content (12 cm depth, time domain reflectometry; Field-Scout TDR 300, Spectrum Technologies, Aurora, Illinois, USA) were measured at five random locations within the 4 m2 subplots every month from June to September. We calculated the standard deviation of each temperature and moisture measure over four months to describe seasonal variations. Slope inclination was determined at plot-level via GPS measurements (GPS 1200, Leica Geosystem, Heerbrugg, Switzerland) and categorized into slope gradient classes according to FAO standards (1990). Thickness of the topsoil horizon (equivalent to Ah or Aa horizon) was determined at one soil monolith (30 x 30 x 30 cm3) per 4 m2 subplot in cm and rounded to next integer.

 References

Adamczyk, M., F. Hagedorn, S. Wipf, J. Donhauser, P. Vittoz, C. Rixen, A. Frossard, J. Theurillat, and B. Frey. 2019. The soil microbiome of GLORIA mountain summits in the Swiss Alps. Frontiers in Microbiology 10:1080-1101.

Blake, G.R., and K. H. Hartge. 1986. Particle Density. Pages 377-382 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Bongers, T. 1988. De nematoden van Nederland. Stichting Uitgeverij van de Koniklijke Nederlandse Natuurhistorische Verenigung (KNNV), Utrecht.

Bongers, T. 1990. The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia 83:14-19.

Bouché, M. B. 1977. Strategies lombriciennes. Ecological Bulletins 25:122-132.

Christian, E., and A. Zicsi. 1999. Ein synoptischer Bestimmungsschlüssel der Regenwürmer Österreichs (Oligochaeta: Lumbricidae). Die Bodenkultur 50:121-131.

Danielson, R. E., and P. L. Sutherland. 1986. Porosity. Pages 443-461 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie ‐ Gefährdung ‐ Kennarten, Ott Verlag, Bern.

Edgar, R. C. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461.

Edgar, R. C. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods 10:996–998.

FAO. 1990. Guidelines for soil description, third ed. Land and Water Development Division at the Food and Agriculture Organization of the United Nations (FAO), Rome.

Frey, B., T. Rime, M. Phillips, B. Stierli, I. Hajdas, F. Widmer, and M. Hartmann. 2016. Microbial diversity in European alpine permafrost and active layers. FEMS Microbiology Ecology 92:fiw018.

Gee, G.W., and J. W. Bauder. 1986. Particle-size analysis. Pages 383-411 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Hart, S. C, J. M. Stark, E. A. Davidson, and M. K. Firestone. 1994. Nitrogen mineralization, immobilization, and nitrification. Pages 985-1016 in R. W. Weaver, S. Angle, P. Bottomley, D. Bezdicek, S. Smith, A. Tabatabai, and A. Wollum, editors. Methods of soil analysis: Part 2—Microbiological and biochemical properties. Soil Science Society of America (SSSA) Inc., Madison.

Lauber, K., Wagner, G., 1996. Flora Helvetica. Flora der Schweiz. Haupt Verlag, Bern.

MeteoSchweiz, 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Zürich.

Meyer, S. T., C. Koch, and W. W. Weisser. 2015. Towards a standardized rapid ecosystem function assessment (REFA). Trends in Ecology and Evolution 30:390-397.

Neff, F., M. C. Resch, A. Marty, J. Rolley, M. Schütz, A. C. Risch, and M. M. Gossner. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands – a functional approach. Ecological Applications 0:e02133

Nguyen, N.H., Z. W. Song, S. T. Bates, S. Branco, L. Tedersoo, J. Menke, J. S. Schilling, and P. G. Kennedy. 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20:241–248.

Nilsson, R. H., K.-H. Larsson, A. F. S. Taylor, J. Bengtsson-Palme, T. S. Jeppesen, D. Schigel, P. G. Kennedy, K. Picard, F. O. Glöckner, L. Tedersoo, I. Saar, U. Kõljalg, and K. Abarenkov. 2018. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research 47:259–264.

Norman, R. J., and J. W. Stucki. 1981. The Determination of Nitrate and Nitrite in Soil Extracts by Ultraviolet Spectrophotometry. Soil Science Society of America Journal 45:347-353.

Oostenbrink, M. 1960. Estimating nematode populations by some selected methods. Pages 81-101 in J. J. Sasser, and W. R. Jenkins, editors. Nematology. Univ. of North Carolina Press, Chapel Hill.

Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O. Glöckner. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41:590-596.

Resch, M. C., M. Schütz, U. Graf, R. Wagenaar, W.H. van der Putten, and A. C. Risch. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56:1782-1793.

Schuldt, A., A. Ebeling, M. Kunz, M. Staab, C. Guimarães-Steinicke, D. Bachmann, N. Buchmann, W. Durka, A. Fichtner, F. Fornoff, W. Härdtle, L. R. Hertzog, A-N. Klein, C. Roscher, J. Schaller, von G. Oheimb, A. Weigelt, W. Weisser, C.Wirth, J. Zhang, H. Bruelheide, and N. Eisenhauer. 2019. Multiple plant diversity components drive consumer communities across ecosystems. Nature Communications 10:1460.

Tedersoo, L., M. Bahram, S. Põlme, U. Kõljalg, N. S. Yorou, R. Wijesundera, L. Villarreal Ruiz, A. M. Vasco-Palacios, P. Q. Thu, A. Suija, M. E. Smith, C. Sharp, E. Saluveer, A. Saitta, M. Rosas, T. Riit, D. Ratkowsky, K. Pritsch, K. Põldmaa, M. Piepenbring, C. Phosri, M. Peterson, K. Parts, K. Pärtel, E. Otsing, E. Nouhra, A. L. Njouonkou, R. H. Nilsson, L. N. Morgado, J. Mayor, T. W. May, L. Majuakim, D. J. Lodge, S. See Lee, K.-H. Larsson, P. Kohout, K. Hosaka, I. Hiiesalu, T. W. Henkel, H. Harend, L.-D. Guo, A. Greslebin, G. Grelet, J. Geml, G. Gates, W. Dunstan, C. Dunk, R. Frenkhan, L. Dearnaley, A. De Kesel, T. Dang, X. Chen, F. Buegger, F. Q. Brearley, G. Bonito, S. Anslan, S. Abell, and K. Abarenkov. 2014. Global diversity and geography of soil fungi. Science 346:1256688.

Thomas, G.W. 1982. Exchangeable cations. Pages 159-165 in A. L. Page, R. H. Miller, and D. R. Keeney, editors. Methods of Soil Analysis: Part 2—Chemical and microbiological properties. Soil Science Society of America (SSSA) Inc., Madison.

Walthert, L., U. Graf, A. Kammer, J. Luster, D. Pezzotta, S. Zimmermann, and F. Hagedorn. 2010. Determination of organic and inorganic carbon, δ13C, and nitrogen in soils containing carbonates after acid fumigation with HCl. Journal of Plant Nutrition and Soil Sciences 173:207-216.

Yeates, G. W., T. Bongers, R. G. M. de Goede, D. W. Freckman, and S. S. Georgieva. 1993. Feeding habits in soil nematode families and genera – an outline for soil ecologists. Journal of Nematology 25:315-331.

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

ecosystem-multifunctionality

display_name: ecosystem-multifunctionality

fungi

display_name: fungi

grassland

display_name: grassland

mineralization

display_name: mineralization

nematodes

display_name: nematodes

plants

display_name: plants

restoration

display_name: restoration

soil-biodiversity

display_name: soil-biodiversity

education

en: Education and science

en: EnviDat: the Environmental Data Portal

en: Restoring grassland multifunctionality


en: food-taste-similarity food-preferences agro-food-trade latent-semantic-analysis national-dishes

en: information on ingredients of national dishes

en: shows information of ingredients of all national dishes of 171 countries and their names

en: National dishes, migration data and data for bilateral food trade flows

media_type: Stata dta file

en:
The data include information on national dishes, migration data and data for bilateral food
trade flows for 5 years as well as importer and exporter GDP, population, distance between
most populated cities, bilateral information on Preferential Trade Agreements, common
official primary langauge, common colonizer post-1945, colonial relationship for overall
171 countries. The final data set contains 145,350 observations (29,070 country pairs for
5 years)

en: dyadic food similarity measures, pairwise similarity and dissimilarity (chord diagram)

en:
The figures show the distribution of two dyadic food tastes similarity measures on the base of
the national dishes: (i) the Manhattan distance and the (ii) latent semantic analysis. The chord diagram
shows the results for country pairs with most and less similarity on basis of the LSA-approach.

en: The data described in this article were collected daily over the period 4 June 2018 to 23 August 2018 and contains information of several data sources. The database includes information on national recipes and their ingredients for 171 countries, measures for food taste similarities between all 171 countries as well as bilateral migration and agro-food trade data for 5 years. The database can be used for analyzing e.g., the relation between food preferences and international trade or food preferences and health outcomes (e.g., obesity) across countries.

agro-food-trade

display_name: agro-food-trade

food-preferences

display_name: food-preferences

food-taste-similarity

display_name: food-taste-similarity

latent-semantic-analysis

display_name: latent-semantic-analysis

national-dishes

display_name: national-dishes

en: Data of national dishes, their similarity and trade flows


maintainer: Jana von Freyberg

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

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

media_type: CSV

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

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

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

ecohydrology

display_name: ecohydrology

endmember-mixing-models

display_name: endmember-mixing-models

plant-water-uptake

display_name: plant-water-uptake

review

display_name: review

root-zone

display_name: root-zone

stable-water-isotopes

display_name: stable-water-isotopes

uncertainties

display_name: uncertainties

envidat

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

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


maintainer: Adam Winstral

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

en: HS and SWE observations

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

media_type: text/plain

en: File/data descriptor

en: Station forcing data

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

en: DEM

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

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

jim

display_name: jim

jules-investigation-model

display_name: jules-investigation-model

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-simulation

display_name: snow-simulation

education

en: Education and science

en: EnviDat: the Environmental Data Portal

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


en: urban-transformations europe strategic-projects spatial-planning land-use-change large-scale-urban-development-projects land-use-transformations strategic-spatial-planning

en: QCA data set large-scale urban development projects

media_type: TXT

en: Please refer to the sections: 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalization in the Metadata for further details on this resource.

en: QCA R script large-scale urban development projects Europe

media_type: R

en:
For the set up of this R script, we used the “QCA” (Duşa, 2019) and “SetMethods” (Oana and Schneider, 2018) R packages.

The following further resources were also used:
Dusa, A. (2019). QCA with R: A comprehensive resource. Springer: Berlin.
Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences. A guide to qualitative comparative analysis. Cambridge: Cambridge University Press.
Thomann, E., Oana, I.-E., & Wittwer, S. (2018). Performing fuzzy- and crisp set QCA with R. A user-oriented beginner's guide. Retrieved from http://www.evathomann.com/links/qca-r-manual

en:
Table of Content: 1. General context of the data set "lsUDPs" ; 2. Background and aims of the study using the data set lsUDPs; 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalisation

1. General context of the data set "lsUDPs"
The data set "lsUDPs" has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2020. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, strategic spatial plans) into quantitative land-change modelling approaches at the urban regional level. The first stage (2016-2017) of the CONCUR project focussed on 21 urban regions in Western Europe. The urban regions were selected through a multi-stage strategy for empirical research (see Hersperger, A. M., Grădinaru, S., Oliveira, E., Pagliarin, S., & Palka, G. (2019). Understanding strategic spatial planning to effectively guide development of urban regions. Cities, 94, 96–105. https://doi.org/10.1016/j.cities.2019.05.032 ).

2. Background and aims of the study using the data set lsUDPs
As part of the CONCUR project, a specific task was to examine the relationship between strategic spatial plans and the formulation and implementation (i.e. urban land change) of large-scale urban development projects in Western Europe.
Strategic urban projects are typically large-scale, prominent urban transformations implemented locally with the aim to stimulate urban growth, for instance in the form of urban renewals of deprived neighborhoods, waterfront renewals and transport infrastructures. While strategic urban projects are referred to in the literature with multiple terms, in the CONCOR project we call them large-scale urban development projects (lsUDPs).
Previous studies acknowledged both local and supra-local (or structural) factors impacting the context-specific implementation of lsUDPs. Local governance factors, such as institutional capacity, coordination among public and private actors and political leadership, intertwine with supra-local conditions, such as state re-scaling processes and devolution of state competencies in spatial planning, de-industrialisation and increasing social inequality. Hence, in implementing lsUDPs, multi-scalar factors act in combination.
Because the formulation and implementation of lsUDPs require multi-scalar coordination among coalitions of public and private actors over an extended period of time, they are generally linked to strategic spatial plans (SSPs). Strategic spatial plans convey collective visions and horizons of action negotiated among public and private actors at the local and/or regional level to steer future urban development, and can contain legally binding dispositions, but also indicative guidelines.
The key question remains as to what extent large-scale urban development projects and strategic spatial plans can be regarded as aligned. By alignment, or “concordance”, we mean that strategic projects are formulated and implemented as part of the strategic planning process (“high concordance”), or that the strategic role of projects is reconfirmed in (subsequent) strategic plans (“moderate concordance”). Lack of concordance is found when lsUDPs have been limitedly (or not at all) acknowledged in strategic spatial plans.
We assume that certain local and supra-local factors, characterising the development of the projects, foster (but not strictly “cause”) the degree of alignment between lsUPDs and SSPs. In this study, we empirically examine how, and to what extent, the concordance between 38 European large-scale urban development projects and strategic plans (outcome: CONCOR) has been enabled by five multi-scalar factors (or conditions): (i) the role of the national state (STATE), (ii) the role of (inter)national private actors (PRIVATE), (iii) the occurrence of supra-regional external events (EVENTS), (iv) the degree of transport connectivity (TRANSP), and (v) local resistance from civil society (RESIST). We adopted a (multi-data) case-based qualitative strategy for empirical research and applied the formalised procedure of within- and cross-case comparison offered by fuzzy-set Qualitative Comparative Analysis appropriate for the goal of this study. Based on set theory, QCA formally integrates contextual sensitivity to case specificities (within-case knowledge) with systematic comparative analysis (across-case knowledge).
The research question the data set has been created to reply to is the following: which conditions, and combinations of conditions, enable the concordance between large-scale urban development projects and strategic spatial plans?
The conditions (“independent variables”) considered are. STATE: the set of large-scale urban projects characterized by a high degree of state intervention and support in their formulation and implementation, PRIVATE: the set of large-scale urban projects characterized by a high degree of involvement of (inter)national private actors in their formulation and implementation, EVENTS: the set of large-scale strategic projects whose formulation and implementation have been strongly affected by unforeseen international events and/or global trends, TRANSP: the set of large-scale strategic projects with a high degree of road and/or transit connectivity, and RESIST: set of large-scale strategic projects whose realization has been characterized by resistances that have substantially delayed or modified the project implementation.
The outcome (“dependent variable”) under analysis is CONCOR: the set of large-scale urban projects having a high degree of concordance/alignment/integration with strategic spatial plans

3. The data set lsUDPs

   3.1 Selection of cases and data collection
To generate the current data set on large-scale urban development projects in European urban regions (data set "lsUDPs"), we identified 35 large-scale urban development projects in a sample of the 21 Western urban regions considered in the CONCUR project (see supra, Hersperger et al. 2019): Amsterdam, Barcelona, Copenhagen, Hamburg, Lyon, Manchester, Milan, Stockholm, Stuttgart. The criteria we followed to identify the 35 large-scale urban development projects are: geographical location, size (large-scale), site (located either in the city core or in the larger urban region) and urban function (e.g. housing, transportation infrastructures, service and knowledge economic functions). Employing these criteria facilitated the selection of diverse large-scale urban development projects while still ensuring sufficient comparability.
In 2016, we performed 47 in-depth interviews with experts in urban and regional planning and large-scale strategic projects and infrastructure (i.e. academics and practitioners) about the formulation, implementation and development (1990s–2010s) of each project in each of the 9 selected urban regions. On average, each interviewee answered questions on 3.1 large-scale urban development projects. Three cases were subdivided into two cases because a clear differentiation between specific implementation stages was identified by the interviewees (expansion of the Barcelona airport, cases “bcn_airport80-90” and “bcn_airport00-16”; realisation of Lyon Part-Dieu, cases “lyo_partdieu70-90” and “lyo_partdieu00-16”; MediaCityUK, cases “man_salfordquays80-00” and “man_mediacityuk00-16”). Therefore, from the initial 35 cases, the final number of analysed cases in the lsUDPs dataset is 38.

    3.2 The data set lsUDPs: Data management and operationalisation
Interviews were fully transcribed and analysed through MAXQDA (version 12.3, VERBI GmbH, Berlin, Germany), and intercoder agreement was evaluated on a sample of nine interviews. We also compiled “synthetic case descriptions” (SCD) for each case (totalling more than 160 SCDs) to spot potential inconsistencies among interviewees’ accounts and to facilitate completion of the “calibration table” for each case (see below). An online expert survey distributed to the interviewees (response rate 78%) helped systematise the information collected during the interviews. We also consulted both academic and gray literature on the case studies to check for possible ambiguity and inconsistencies in the interview data, and to solve discrepancies between our assigned set membership scores and questionnaire values. Site visits were also carried out to retrieve additional information on the selected cases.
For each case (i.e. each of the 38 selected large-scale urban development projects), we operationalised each condition (i.e. STATE, PRIVATE, EVENTS, TRANSP, RESIST) and the outcome (CONCOR) in terms of sets, for subsequent application of Qualitative Comparative Analysis. This process is called “calibration”; we used a number of indicators for each condition to qualitatively assess each large-scale project across the conditions. The case-based qualitative assessment was then transformed into fuzzy-set membership values. Fuzzy-set membership values range from 0 to 1, and should be conceived as “fundamentally interpretative tools” that “operationalize theoretical concepts in a way that enhances the dialogue between ideas and evidence” (Ragin 2000:162, in “Fuzzy-set Social Science”. Chicago: University Press). We employed a four-value fuzzy-set scale (0, 0.33, 0.67, 1) to “quantify” into set membership scores the individual histories of cases retrieved from interview data. Only the condition TRANSP was calibrated as a crisp-set (0, 1).
The translation of qualitative case-based information into numerical fuzzy-set membership values was iteratively performed by populating a calibration table following standard practices recently emerged in QCA when dealing with qualitative (interview) data.

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display_name: urban-transformations

en: lsUDPS Large-scale urban development projects in European urban regions


en: cmip5 orographic-precipitation air-temperature climate-change precipitation-amount

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en:
Predicting future climatic conditions at high spatial resolution is essential for many applications in science. Here we present data for monthly time series of precipitation and minimum and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation sums at ~5km spatial resolution globally for the years 1850-2100. We validated the performance of the downscaling algorithm by comparing model output with observed climates for the years 1950-2069.

CHELSA_cmip5_ts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

air-temperature

display_name: air-temperature

climate-change

display_name: climate-change

orographic-precipitation

display_name: orographic-precipitation

precipitation-amount

display_name: precipitation-amount

envidat

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

en: High resolution monthly precipitation and temperature timeseries for the period 2006-2100