data-broedlin-cnp

title_for_slug: data-broedlin-cnp

en: soil carbon drying-rewetting stoichiometry nitrogen forest mineralization dissolved-organic-matter phosphorus

en: Data Broedlin-CNP

en: Data Broedlin-CNP-Release

en: Raw data of C-N-P mobilization experiment from Oi, Oe/Oa, and A horizons of beech forest soils along a nutrient availability gradient.

en: Mircocosm experiment to identify the individual patterns and controls of C, N, and P mobilization in soils under beech forests. Organic and mineral horizons sampled along a nutrient availability gradient in Germany were exposed to either permanent moist conditions or to dry spells in microcosms and quantified the release of inorganic and organic C, N, and P.

carbon

display_name: carbon

dissolved-organic-matter

display_name: dissolved-organic-matter

drying-rewetting

display_name: drying-rewetting

forest

display_name: forest

mineralization

display_name: mineralization

nitrogen

display_name: nitrogen

phosphorus

display_name: phosphorus

soil

display_name: soil

stoichiometry

display_name: stoichiometry

education

en: Education and science

en: Data Broedlin CNP


maintainer: EnviDat Support

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

state: active

type: dataset

en: Description

en: Description of the predictors provided in this dataset

format: XLS

en: Predictors

media_type: application/zip

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

format: ZIP

mimetype: application/zip

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

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

Paper Citation:

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

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

switzerland

display_name: switzerland

education

fr: Education, science

de: Bildung, Wissenschaft

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester


vegetation-height-model-nfi

maintainer: Christian Ginzler

title_for_slug: vegetation-height-model-nfi

en: photogrammety vegetation-height-model 3d point-cloud forest

en: Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

media_type: PDF

format: PDF

en: 2016 Vegetation Height Model NFI

media_type: geotiff

en:
Data 'Vegetation Height Model NFI 2016' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2011 to 2016.

format: GeoTIFF

en: Thumbnail

media_type: image/jpeg

format: SERVICE

mimetype: image/jpeg

en: 2019 Vegetation Height Model NFI (current)

en:
Data 'Vegetation Height Model NFI 2019' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2013 to 2019.

en: 2012 Vegetation Height Model NFI

en:
Data 'Vegetation Height Model NFI 2012' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2007 to 2012.

en: 2015 Vegetation Height Model NFI

en:
Data 'Vegetation Height Model NFI 2015' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2010 to 2015.

en:
A national vegetation height model was calculated for Switzerland using digital aerial images.
We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution
(1 × 1 m and 0.5 x 0.5 m). The DSM was then normalized to obtain the actual
vegetation heights using a digital terrain model (DTM)
based on laser data with the buildings masked out, and to
produce a vegetation height model (VHM). Such a model
will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very
high level of detail. For covering the whole of Switzerland,
we use summer aerial images from six years.

Latest version is from 2019.

photogrammety

display_name: photogrammety

point-cloud

display_name: point-cloud

vegetation-height-model

display_name: vegetation-height-model

en: Vegetation Height Model NFI


greenland-shrubs-and-microclimate

maintainer: Nathalie Chardon

title_for_slug: greenland-shrubs-and-microclimate

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

en: GitLab Repository

media_type: .zip

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

en: Kangerluarsunnguaq

media_type: .csv

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

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

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

id: sampling zone and unique 90 m grid identifier

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

date: sampling day.month.year

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

alcr: _Alnus crispa_

bena: _Betula nana_

dila: _Diapensia lapponica_

emni: _Empetrum nigrum_

hahy: _Harimanella hypoides_

lopr: _Loiseleuria procumbens_

oxpa: _Oxycoccus palustre_

phca: _Phyllodoce caerulea_

rhod: _Rhododendron_ sp.

saar: _Salix arctica_

sagl: _Salix glauca_

sahe: _Salix herbacea_

vaul: _Vaccinium uliginosum_

drin: _Dryas integrifolia_

thpr: _Thymus praecox_

 Habitat characteristics [% cover]

shrub: all shrub species

gram: graminoids

forb: forbs

bryo: bryophytes

lich: lichens

litt: litter

bare: bare ground

stone: stones (> 2 cm)

org: organic crust

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

alcr_c: _Alnus crispa_ cover

alcr_h: _Alnus crispa_ height

bena_c: _Betula nana_ cover

bena_h: _Betula nana_ height

dila_c: _Diapensia lapponica_ cover

dila_h: _Diapensia lapponica_ height

emni_c: _Empetrum nigrum_ cover

emni_h: _Empetrum nigrum_ height

hahy_c: _Harimanella hypoides_ cover

hahy_h: _Harimanella hypoides_ height

lopr_c: _Loiseleuria procumbens_ cover

lopr_h: _Loiseleuria procumbens_ height

oxpa_c: _Oxycoccus palustre_ cover

oxpa_h: _Oxycoccus palustre_ height

phca_c: _Phyllodoce caerulea_ cover

phca_height: _Phyllodoce caerulea_ height

rhod_c: _Rhododendron_ sp. cover

rhod_h: _Rhododendron_ sp. height

saar_c: _Salix arctica_ cover

saar_h: _Salix arctica_ height

sagl_c: _Salix glauca_ cover

sagl_h: _Salix glauca_ height

sahe_c: _Salix herbacea_ cover

sahe_h: _Salix herbacea_ height

vaul_c: _Vaccinium uliginosum_ cover

vaul_h: _Vaccinium uliginosum_ height

drin_c: _Dryas integrifolia_ cover

drin_h: _Dryas integrifolia_ height

 Community characteristics

rich_pa: shrub species richness at grid level

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

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

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

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

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

 Topography and terrain wetness (calculated from)

tcws: Tasseled Cap Wetness component (Sentinal images)

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

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

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

 Downscaled climate (time series 1984 – 2013)

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

precipmam: cumulative March-May precipitation [mm]

tempcont: yearly maximum – minimum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

precipjja: cumulative June-August precipitation [mm]

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

en: Nuup Kangerlua

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

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

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

year: sampling year

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

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

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

pa_bena: _Betula nana_

pa_sagl: _Salix glauca_

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

cov_bena: _Betula nana_ cover

cov_sagl: _Salix glauca_ cover

 Downscaled climate (time series 1984 – 2013)

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

tempcont: yearly maximum – minimum temperature [ºC]

precipmam: cumulative March-May precipitation [mm]

precipjja: cumulative June-August precipitation [mm]

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

 Topography and terrain wetness (calculated from)

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

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

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

tcws: Tasseled Cap Wetness component (Sentinal images)

 Community characteristics

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

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

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

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

en:
 Study Aim

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

 Data Description

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

 Nuup Kangerlua (Godthåbsfjord)

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

 Kangerluarsunnguaq (Kobbefjord)

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

 Methods

 Field survey in Nuup Kangerlua

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

 Field survey in Kangerluarsunnguaq

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

 Biotic variables

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

 Climate variables

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

 Topography and terrain wetness indicator variables

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

 Computer code

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

 Third-party data

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

accessible-shrub-forest

display_name: accessible-shrub-forest

arctic

display_name: arctic

climate

display_name: climate

field-data

display_name: field-data

sdm

display_name: sdm

validation

display_name: validation

education

en: Education and science

en: Greenland shrubs and microclimate


maintainer: Amt für Abfall, Wasser, Energie und Luft des Kantons Zürich, Abteilung Luft, Klima und Strahlung - Sektion Monitoring

de: luft lufttemperaturen

label: LoRa-Datenbeschrieb

de: LoRa-Sensor-Messwerte 2019/07

media_type: text/csv

format: CSV

mimetype: text/csv

de: LoRa-Sensor-Messwerte 2019/10

de: LoRa-Sensor-Messwerte 2021/04

de: LoRa-Sensor-Messwerte 2022/02

de: LoRa-Sensor-Messwerte 2019/08

de: LoRa-Sensor-Messwerte 2022/12

de: LoRa-Sensor-Messwerte 2020/12

de: LoRa-Sensor-Messwerte 2019/05

de: LoRa-Sensor-Messwerte 2021/02

de: LoRa-Sensor-Messwerte 2022/11

de: LoRa-Sensor-Messwerte 2019/09

de: LoRa-Sensor-Messwerte 2020/07

de: LoRa-Sensor-Messwerte 2020/01

de: LoRa-Sensor-Messwerte 2022/01

de:
Seit Frühjahr 2019 betreibt das AWEL des Kantons Zürich ein Messnetz aus rund 40 Lufttemperatur und -feuchte Sensoren (LoRa). Die Sensor-Standorte befinden sich hauptsächlich im städtischen Raum, verdichtet in der Stadt Zürich, aber auch in weiteren Gemeinden im Kanton Zürich. Ziel des Messnetzes ist es, Messdaten für Stadtklima-Analysen bereitzustellen, damit Aspekte des Lokalklimas und Klimawandels stärker sichtbar werden und in der Raumplanung und Stadtentwicklung berücksichtigt werden können. Mit Hilfe dieser Daten lassen sich u.a. die Auswirkungen von Hitzewellen im städtischen Raum beschreiben. Das Messnetz wird durch das AWEL betrieben, unter Beteiligung der Stadt Zürich; die weiteren Standort-Gemeinden sind über die Messaktivität informiert.
Der hier verfügbare Datensatz wird täglich aktualisiert und umfasst alle, für die Standorte gültigen Messwerte im 10 Minuten Mittel, inklusive entsprechender Standortinformationen. Weitere Angaben zu Datensatz-Struktur und verwendeten Sensoren sind im Datensatz-Beschrieb zu finden.

awel-kanton-zuerich

de: Amt für Abfall, Wasser, Energie und Luft Kanton Zürich

de: Das Amt für Abfall, Wasser, Energie und Luft (AWEL) des Kantons Zürich hat den Auftrag, der Bevölkerung im Kanton Zürich eine gesunde, sichere und attraktive Umwelt zu bieten. Zu den Themen Abfall, Wasser, Energie und Luft stellt das AWEL Fachleuten und Interessierten Informationen zur Verfügung.

de: Lufttemperatur und Luftfeuchte LoRa-Sensor-Messwerte


maintainer: Matthias Haeni

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

en: Data plot of the field station (CLF)

en: Data file.

en: Data plot of the forest station (CLB)

media_type: application/pdf

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

mimetype: application/pdf

en: Site aerial photo (Celerina)

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

en: Site photo of the forest station (CLB)

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

en: Site photo of the field station (CLF)

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

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 Celerina in Switzerland where one station is located within a natural coniferous forest stand (CLB) with Swiss pine (_Pinus cembra_; 210-250 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CLF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Celerina 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

education

de: Bildung, Wissenschaft

envidat

de: EnviDat: das Umwelt-Datenportal

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.

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.

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


maintainer: Roksolana Petrovska

en: fagus acer dominance mortality-rate shade-tolerance regeneration lar

en: Mortality of regeneration

media_type: CSV

en: Acer platanoides, Acer pseudoplatanus and Fagus sylvatica living and dead juveniles (0-500 cm in height) grown in the deep shade of a prime beech-dominated forest.

media_type: r script (flat-ascii text)

en: The R-script of survival analysis for accelerated failure time model

en:
 One individual per species, vitality class (low and high) and height class (eight classes: 0–10, 11–20, 21–35, 36–60, 61–90, 91–130, 131–200 and 201–500 cm) was randomly selected and harvested in each of the six plots. This resulted in a sample of 82, 80 and 89 living individuals of A. platanoides, A. pseudoplatanus and F. sylvatica, respectively.

 Additionally stems of dead Acer spp. and F. sylvatica trees that had died within the last three years (2015–2018) were randomly harvested, matching the height classes of the harvested living trees wherever possible. In total, 179 dead young trees (60 A. platanoides, 72 A. pseudoplatanus and 47 F. sylvatica) were collected.

 Variables:
* species_code: a_pla - Acer platanoides, a_pse - Acer pseudoplatanus, f_syl - Fagus sylvatica
* species: as above
* dummy: 0 - living individual, 1 - dead individual
* LAR_cm2_g: leaf area ratio or ratio of leaf area to total plant biomass, [cm2/g]
* tree_age: in years
* avg_ring_micron: average width of the last 5 rings in tree life excluding the last ring
* dry_mass_g: aboveground and belowground biomass
* DLI: direct light index (measured only under living individuals)
* BLI: diffuse light index (measured only under living individuals)
* GLI: global light index

acer

display_name: acer

dominance

display_name: dominance

fagus

display_name: fagus

lar

display_name: lar

mortality-rate

display_name: mortality-rate

regeneration

display_name: regeneration

shade-tolerance

display_name: shade-tolerance

education

en: Education and science

en: Mortality of regeneration: Acer spp. and Fagus sylvatica


ceilometer-klosters

title_for_slug: ceilometer-klosters

en: raclets cloud-base-height klosters ceilometer vertical-visibility

en: Ceilometer data Klosters

media_type: mat

en: Ceilometer installed at Klosters measuring cloud base height, cloud cover and backscatter profile. The time resolution is 30 seconds, with time given in number of days from January 0, 0000, UTC. More information on the variables in the manufacturer manual.

en: Cloud base height (m) and vertical visibility (m) were measured with the VAISALA Ceilometer CL31 in Klosters (LON: 9.880413, LAT: 46.869019). The CL31 is an instrument with constant reliability for all weather conditions and simultaneous detection of three cloud layers in heights up to 7.6 km.

ceilometer

display_name: ceilometer

cloud-base-height

display_name: cloud-base-height

klosters

display_name: klosters

raclets

display_name: raclets

vertical-visibility

display_name: vertical-visibility

en: Ceilometer Klosters


en: filtering landsca conservation species-distribution hard-limit flags occurrence-records species-observations taxonomy large-scale synonyms accepted-name range-maps r-package gbif ecoregions

en: GitHub URL

media_type: R

en: wsl.gbif R package version 0.1 (deprecated)

media_type: ZIP

en: Test version, not to be used.

en: gbif.range R package version 0.2 (current)

en: Although species range may be obtained using expert maps or modeling methods, expert data is often species-limited and statistical models need more technical expertise as well as many species observations. When unavailable, such information may be extracted from the Global Biodiversity Information facility (GBIF), the largest public data repository inventorying georeferenced species observations worldwide. However, retrieving GBIF records at large scale may be tedious if users are unaware of specific tools and functions that need to be employed. Here we present *gbif.range*, a R library that contains automated methods to generate species range maps from scratch using in-house ecoregions shapefiles and an easy-to-use GBIF download wrapper. Finally, this library also offers a set of additional very useful parameters and functions for large GBIF datasets (generate doi, extract GBIF taxonomy, records filtering...). [gbif.range R project](https://github.com/8Ginette8/gbif.range)

accepted-name

display_name: accepted-name

conservation

display_name: conservation

ecoregions

display_name: ecoregions

filtering

display_name: filtering

flags

display_name: flags

gbif

display_name: gbif

hard-limit

display_name: hard-limit

landsca

display_name: landsca

large-scale

display_name: large-scale

occurrence-records

display_name: occurrence-records

r-package

display_name: r-package

range-maps

display_name: range-maps

species-distribution

display_name: species-distribution

species-observations

display_name: species-observations

synonyms

display_name: synonyms

taxonomy

display_name: taxonomy

en: gbif.range - An R package to generate species range maps based on ecoregions and a user-friendly GBIF wrapper


maintainer: Ionut Iosifescu Enescu

en: jupyter-notebooks open-data hpc open-science open-source reproducibility research-data-management

en: Open Science Support @ WSL (Poster)

en:
This poster was originally created for the swissuniversities Open Science Action Plan: Kick-Off Forum, and showed to the audience on 17.10.2019. It illustrates how the environmental data portal EnviDat provides the tools for fostering Open Science and Reproducibility of scientific research at WSL.

Supporting open science is a highly relevant user requirement for EnviDat and for implementing FAIR (Findability, Accessibility, Interoperability and Reusability) principles at dataset level.
EnviDat encourages WSL scientists to complement data publication with a complete description of research methods and the inclusion of the open source software, code or scripts used for processing the dataset or for obtaining the published results. By openly publishing open software (e.g. as Jupyter notebooks) alongside research data sets, researchers can contribute to mitigate reproducibility issues.

EnviDat also promotes and supports, where possible and practical, the publication of software as Jupyter notebooks. Jupyter notebooks provide a solution for improved documentation and interactive execution of open code in a wide range of programming languages (Python, R, Octave/Matlab, Java or Scala). These programming languages are widely used in environmental research at WSL and well supported by the Jupyter-compatible kernels. We have sucessfully interfaced EnviDat-hosted notebooks with the WSL High-Performance Computing (HPC) Linux Cluster through a JupyterHub/JuypterLab beta installation on the HPC cluster implemented in close collaboration with the WSL IT-Services.
For existing software that cannot be easily migrated to Jupyter Notebooks, the Open Science and Reproducibility is assisted by containerisation. We have proven that several Singularity containers can successfully run on WSL's HPC cluster.

Finally, the researchers can upload the data/results complemented by code (e.g. as Jupyter Notebooks, or Singularity containers) and any additional documentation in EnviDat. Consequently, they will receive a DOI for the entire dataset, which they can reference in their science paper in order to publish a more reproducible research.

_License_: This poster is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 "No Rights Reserved" international license. You can reuse this poster in any way you want, for any purposes and without restrictions.

hpc

display_name: hpc

jupyter-notebooks

display_name: jupyter-notebooks

open-data

display_name: open-data

open-science

display_name: open-science

open-source

display_name: open-source

reproducibility

display_name: reproducibility

research-data-management

display_name: research-data-management

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: Open Science Support at the Swiss Federal Research Institute WSL. The EnviDat Concept


en: air-temperature precipitation free-climate-data maximum-temperature minimum-temperature timeseries climate-layers climate century

en: Data Access

en: CHELSA Technical Documentation

en: As the CHELSA project is continuously expanded, certain new parameters, time scales etc. are not included anymore in the original publication (Karger et al. 2017). We are therefore provide this document as a guideline of the current climatic parameters available and will expand it as new parameters or time periods will become available. Please refer to the version history for changes made in the document.

en:
CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum. Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.

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

air-temperature

display_name: air-temperature

century

display_name: century

climate

display_name: climate

climate-layers

display_name: climate-layers

free-climate-data

display_name: free-climate-data

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

timeseries

display_name: timeseries

education

en: Education and science

en: CHELSAcruts -  High resolution temperature and precipitation timeseries for the 20th century and beyond


aerosol-data-weissfluhjoch

title_for_slug: aerosol-data-weissfluhjoch

en: aerosol ice-nucleation particles raclets inp linda

en: SMPS Dataset Weissfluhjoch

media_type: application/x-netcdf

en: The SMPS file contains aerosol data (particle concentration, total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) in a size range between 12 and 460 nm (electrical mobility diameter) as well as instrument characteristics (sample temperature, pressure, relative humidity, mean free path and gas viscosity) in a temporal resolution of 2 minutes, with time in UTC, measured at Weissfluhjoch.

mimetype: application/x-netcdf

en: OPC-N3 Dataset Weissfluhjoch

en: Particle concentrations in a size range of 0.35 μm to 40 μm were measured with an OPC-N3 (Alphasense, Inc.) in a resolution of 20 seconds (time in UTC). PM₁, PM₂,₅ and PM₁₀ were directly derived.

en: APS Dataset Weissfluhjoch

en: The APS file contains aerosol data (total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) for aerodynamic diameters between 0.5 μm and ~20 μm as well as instrument characteristics, in a temporal resolution of 20 seconds, with time in UTC, measured at Weissfluhjoch.

en: OPC-GT526S Dataset Weissfluhjoch

en: Particle counts were recorded in six size channels (0.3, 0.5, 0.8, 1.5, 2.5 and 5 μm) in a resolution of 20 seconds (time in UTC) by a particle counter (GT-526S, Met One Instruments, Inc.).

en: LINDA Dataset Weissfluhjoch

en: The LINDA file contains information on temperature (in °C) dependent frozen fractions, number of Ice Nuleating Particles (INP, per standard liter) and differential INP concentrations (per standard liter and Kelvin).

en:
Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.

Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0).

Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³).

Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³).

Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

aerosol

display_name: aerosol

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

linda

display_name: linda

particles

display_name: particles

raclets

display_name: raclets

en: Aerosol Data Weissfluhjoch


intratrait

maintainer: Christian Rixen

title_for_slug: intratrait

en: neighbour-interactions plant-traits elevation-range elevation-gradient climate-change cold-adapted-plants

en: Plant trait data along elevation gradients on mountains

en:
This data set was used to test whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries (Switzerland, Australia, New Zealand, China) in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species’ height ratio, its height relative to its nearest neighbour.
The following traits and parameters were measured and are available in this data set:
As an indication of plant stature, we measured vegetative and generative height, where vegetative height was distance from soil to highest vegetative leaf and generative height was distance to the highest point on the reproductive shoot. As a measure of reproductive investment, we noted the presence of flowers on the randomly chosen individuals (see below). As a measure of individual and genet basal area, we measured individual plant and patch diameters, in two dimensions (along the largest diameter and perpendicular to it). In clonal plant species, plant diameter was equivalent to an individual rosette, whereas patch diameter referred to the whole genet and could represent the size of a tuft, tussock or cushion. For genera with more singular growth forms (e.g., some Gentiana species) plant and patch diameter were the same. The two diameter measurements were made at right angles, allowing estimates of patch and plant areas to be calculated as an ellipse (i.e., area = 0.5 a 0.5 b Π). All traits were measured on ten randomly selected individuals per site. Flower count data was considered in a binary fashion on a per individual basis (because for some species individuals only produce one flower when flowering) so that the presence or absence of flower(s) was a nominal value between 0 and 10 for each species at each site. We then collected at least three leaves (up to 30 for small and light leaves) from each of the first three individuals selected from each species for determination of leaf dry matter content (LDMC) and specific leaf area (SLA). For calculations of LDMC and SLA, fresh leaves were scanned on a flatbed scanner to determine leaf area. Leaves were then weighed on a balance to a precision of +/- 0.001g, prior to being air dried and reweighed with a balance to a precision of +/- 0.0001g. LDMC was calculated by dividing dry leaf mass by fresh leaf mass. SLA was calculated by dividing leaf area by dry leaf mass. Additionally, within an area of 10 cm diameter around the target individual, we determined the tallest neighbouring species and measured its vegetative and generative height, and estimated the percent cover of the target species, other vegetation, rock, and bare soil.
For more details see Rixen et al. 2022, Journal of Ecology.

climate-change

display_name: climate-change

cold-adapted-plants

display_name: cold-adapted-plants

elevation-gradient

display_name: elevation-gradient

elevation-range

display_name: elevation-range

neighbour-interactions

display_name: neighbour-interactions

plant-traits

display_name: plant-traits

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: intratrait


maintainer: Avalanche Warning Service SLF

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

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

en: Education and science

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


en: backward-trajectories ice-nucleation aerosol clouds raclets

en: This dataset contains number concentrations of ice-nucleating particles active at -15 °C observed at Weissfluhjoch during February and March 2019, as well as complementary data (measured aerosol number concentrations and modelled total precipitation along air mass trajectories). This data formed the basis of our paper with the title “Towards parameterising atmospheric concentrations of ice-nucleating particles active at moderate supercooling”.

aerosol

display_name: aerosol

backward-trajectories

display_name: backward-trajectories

clouds

display_name: clouds

ice-nucleation

display_name: ice-nucleation

en: Ice nucleating particle concentrations active at -15 °C at Weissfluhjoch


en: longwave-radiation meteo-station gemstock snow-height shortwave-radiation

en: DATA ACCESS

media_type: SMET

en:
Meteo data at Gemsstock [Level 2 dataset](http://models.slf.ch/p/dataset-processing/).

fields      = timestamp TSS HS VW DW VW_MAX OSWR ISWR ILWR RECORD AUX_VOLTAGE LOGGER_VOLTAGE LW_SENSOR_TEMP VENTILATION_FLAG OLWR

en: Meteorological station at Gemstock (3021 m asl) in Canton Uri. The station includes in/out LW/SW and a snow height sensor.

gemstock

display_name: gemstock

longwave-radiation

display_name: longwave-radiation

meteo-station

display_name: meteo-station

shortwave-radiation

display_name: shortwave-radiation

snow-height

display_name: snow-height

en: GEM2: Meteorological and snow station at Gemsstock (3021 m asl), Canton Uri, Switzerland


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

en: Field observations of snow instabilities

en: Read-me file

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

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

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

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

These data are the basis of the following publication: Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, Cryosphere, 15, in review, 2021.

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

avalanche

display_name: avalanche

avalanche-danger

display_name: avalanche-danger

avalanche-forecasting

display_name: avalanche-forecasting

danger-level

display_name: danger-level

snow

display_name: snow

snow-instability

display_name: snow-instability


maintainer: James W. Kirchner

en: sap-flow groundwater ecohydrology snowmelt evapotranspiration streamflow diel-cycle diurnal-cycle

en: 30-minute time series

media_type: text/plain

en: 30-minute hydrometeorological and ecohydrological time series.

format: TXT

mimetype: text/plain

en: Daily time series

en: Daily hydrometeorological and ecohydrological time series

en: Metadata

en: Column descriptions for both data files, and map coordinates for all measurement locations.

en: Subcatchment polygons

en: Subcatchment polygons for use in the Google Earth Engine scripts.

en: MODIS EVI2 script

en: Google Earth Engine script for computing MODIS two-band enhanced vegetation index (EVI2) values and averaging them over subcatchments.

en: MODIS NDSI script

en: Google Earth Engine script for computing MODIS normalized difference snow index (NDSI) values and averaging them over subcatchment areas.

en:
Hydrometerological and ecohydrological time series from Sagehen Creek and Independence Creek, Sierra Nevada, USA, illustrating hydrological responses to daily cycles in snowmelt and evapotranspiration forcing.

Data include 30-minute time series of

- weather variables,

- sap flow fluxes,

- groundwater levels (in two riparian transects of shallow groundwater wells),

- and stream stages (at 12 sites spanning a 500-meter elevation gradient),

and daily time series of

- temperature, precipitation, and snow water equivalent at three nearby snow telemetry stations

- diel cycle index values for groundwater levels and stream stages,

- and MODIS normalized difference snow index (NDSI) and enhanced vegetation index (EVI2) values averaged over selected subcatchments.

Google Earth Engine scripts for extracting the MODIS data are also provided.

diel-cycle

display_name: diel-cycle

diurnal-cycle

display_name: diurnal-cycle

ecohydrology

display_name: ecohydrology

evapotranspiration

display_name: evapotranspiration

groundwater

display_name: groundwater

sap-flow

display_name: sap-flow

snowmelt

display_name: snowmelt

streamflow

display_name: streamflow

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: Daily cycles in solar flux, snowmelt, transpiration, groundwater, and streamflow at Sagehen and Independence Creeks, Sierra Nevada, USA


maintainer: Athanasios Nenes

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

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

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

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

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

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

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

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

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

wolfgangpass

display_name: wolfgangpass

education

en: Education and science

en: Predicted cloud droplet numbers Davos Wolfgang


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

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

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: 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:
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.

europe

display_name: europe

land-use-change

display_name: land-use-change

land-use-transformations

display_name: land-use-transformations

large-scale-urban-development-projects

display_name: large-scale-urban-development-projects

spatial-planning

display_name: spatial-planning

strategic-projects

display_name: strategic-projects

strategic-spatial-planning

display_name: strategic-spatial-planning

urban-transformations

display_name: urban-transformations

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


maintainer: Sven-Eric Hopf

en: monitoring fraxinus ash-dieback forest iap

en: Monitoring of ash trees as part of the Intercantonal Forest Observation Programme

media_type: .pdf

en: Packgroun information on data for the publication: Spread and Severity of Ash Dieback in Switzerland – Tree Characteristics and Landscape Features Explain Varying Mortality Probability, (Klesse et al. 2021, Front. For. Glob. Change 4:645920., doi: 10.3389/ffgc.2021.645920)

en:
In 2013, the Institute for Applied Plant Biology (IAP) started a monitoring programme to study the development and the spatial variation of the ash dieback disease with the aim to find some partially resistant European ash trees (Fraxinus excelsior).
We collaborate as co-authors for the publication: Spread and Severity of Ash Dieback in Switzerland - Tree Characteristics and Landscape Features Explain Varying Mortality Probability (Klesse et al. 2021 in frontiers)

ash-dieback

display_name: ash-dieback

forest

display_name: forest

fraxinus

display_name: fraxinus

iap

display_name: iap

monitoring

display_name: monitoring

envidat

en: EnviDat: the Environmental Data Portal

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


maintainer: EnviDat

en: research-data-management metadata-convention meteorological-data-format csv nead

en: NEAD on GitHub

media_type: HTML

format: HTML

en: MeteoIO (future reference implementation)

media_type: text/html

mimetype: text/html

en: NEAD syntax v0.1 (EBNF)

en: PyNEAD (future reference implementation)

en:
Acknowledgement: The NEAD format includes NetCDF metadata and is proudly inspired by both SMET and NetCDF formats. NEAD is designed as a long-term data preservation and exchange format.

The NEAD specifications were presented at the "WMO Data Conference 2020 - Earth System Data Exchange in the 21st Century" (Virtual Conference).

-----------------------

Summary:
The Non-Binary Environmental Data Archive (NEAD) format is being developed as a generic and intuitive format that combines the self-documenting features of NetCDF with human readable and writeable features of CSV. It is designed for exchange and preservation of time series data in environmental data repositories.

License: The NEAD specifications are released to the public domain under a Creative Commons CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

csv

display_name: csv

metadata-convention

display_name: metadata-convention

meteorological-data-format

display_name: meteorological-data-format

nead

display_name: nead

research-data-management

display_name: research-data-management

en: Non-Binary Environmental Archive Data (NEAD) format


maintainer: Frank Techel

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

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

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

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

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

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

avalanche-accident

display_name: avalanche-accident

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-fatalities

display_name: avalanche-fatalities

avalanche-statistics

display_name: avalanche-statistics

long-term-trends

display_name: long-term-trends

education

en: Education and science

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


maintainer: Leo Gallus Bont

en: accessible-forest geoprocessing seilaplan openstreetmap qgis forest

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

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: Digitised sample perimeter

media_type: application/zip

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

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

en: Juypter Notebook for Data Preparation

media_type: ipynb

en: Preview

media_type: image/png

format: PNG

mimetype: image/png

en: Juypter Notebook for Data Preparation (online)

en: Forest vector data (for sample forest range)

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

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

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

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

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

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

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

accessible-forest

display_name: accessible-forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan

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


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

en: ECT data (Norway, Switzerland)

media_type: text/csv

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

en: Rutschblock data (Switzerland)

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

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

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland

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: Stability tests, avalanche observations, Switzerland, Norway


maintainer: Axel Volkwein

en: rockfall experiments experiment trajectory field-tests natural-hazards mass-movements field-test field-data

en: Block trajectories recorded from local positioning system

media_type: ascii, tab separated, also as Excel table

en: This file contains more than 80 rockfall trajectories projected onto the terrain, i.e. the positions of the single blocks over time during the single tests that were recorded by the local positioning system with about 10Hz. It contains the detected positions as well as a smoothened path (see Volkwein et al., Geosciences, 2018). The table also contains the derived velocities and accelerations along the slope (attention: these accelerations are the once that occur during an impact).

en: These data contain the point clouds obtained from laser scanning the test slope and the six test blocks used for the experiments. The coordinates of the slope are given in CH-1903 format. The coordinates of the block point clouds are kept in a lokal x/y/z coordinate system.

en: Overview of all tests

en:
This table gives an overview on all tests performed including
- block used
- start & end position
- number of jumps
- etc.
-

en: Jumps and impacts

media_type: ascii, tab separated, also as excel table

en: This table contains data of more than 2200 of each jumps and impacts of the test blocks on a grassy slope. It gives there durations in combination with acting accerlartions and the rotational speed of the blocks. The data were retrieved using the block internal accelerometer and rotation sensors. The combination of these data with the measurement from the local positioning system allows the deduction of jump lengths, slope gradient etc.

en:
In summer 2014, 6 rock blocks between 20 and 80kg have been thrown in total 111 times down a slope at the Swiss Oberalppass close to the village Tschamut. The slope was mainly covered by grass and its lower part was flat and large enough to provide natural runouts of the single trajectories.

An extensive measurement program has been set up to measure the block trajectories: With surveyor's instruments the slope and the six used rock blocks were scanned and the start and end positions of each test were recorded. During the single events two cameras filmed the trajectories. A special sensor device located within the blocks recorded the acting accelerations and rotational speeds over time. Further, the device emitted a Wifi signal that got detected from eight receivers around the slope. Based on this signal the block position has been recorded over time.

The dataset contains all data that were gathered through above field campaign.

experiment

display_name: experiment

experiments

display_name: experiments

field-data

display_name: field-data

field-test

display_name: field-test

field-tests

display_name: field-tests

mass-movements

display_name: mass-movements

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

trajectory

display_name: trajectory

education

en: Education and science

en: Repetitive trajectory testing in Tschamut 2014


lidar-davos-wolfgang

maintainer: Patric Seifert

title_for_slug: lidar-davos-wolfgang

en: polly lidar tropos clouds raclets

en: Raman Lidar Davos

en: A portable Raman lidar system (Polly) from Leibnitz Institute for Tropospheric Research (Tropos) was deployed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Please use this [link](http://polly.tropos.de/?p=lidarzeit&Ort=39), to be directly forwarded to the Davos location and select the date of interest from the calendar (bold numbers). The data can be requested directly at the Polly team.

clouds

display_name: clouds

lidar

display_name: lidar

polly

display_name: polly

raclets

display_name: raclets

tropos

display_name: tropos

en: LIDAR Davos Wolfgang


maintainer: EnviDat Support

en: landslides tramm pressure-perturbations landslide-testsite pore-water cces

en: TRAMM Ruedlingen data

en: TRAMM project data from the Ruedlingen site (exp 1 + 2). Measured parameters include soil suction, water level, soil volumetric water content, rain intensity, and soil temperature.

en:
A landslide testsite dataset related to pore water pressure perturbations on the stability of unsaturated silty sand slopes leading to the initiation and propagation of the shear deformations and eventual rapid mass movements. This project was initiated and led by the Institute of Geotechnical Engineering (IGT) of the Swiss Federal Institute of Technology (ETH Zurich) and was incorporated in a Swiss national (TRAMM) and a European Union (SafeLand) multidisciplinary research project. Field site: The experimental slope is 7.5 m wide by 35 m long, located in the Swiss lowlands on an east facing slope over-looking the river Rhine, at an altitude of ~ 350 masl. Originally there were forestry covertures of circa 80%, heights of 5-20 m. Shrubs up to 1-5 m high and a free herb layer covered ~ 50% of the surface. The average gradient was determined to be from 38° to 43° with a slightly concave surface. The underlying rock consists mainly of Molasse, which is formed by alternate layers of sea deposits under the Tethys Sea (Seawater Molasse) and land deposits (Freshwater Molasse). Several augured samples, as well as an outcrop of the bedrock about 20 m above the selected field, revealed horizontal layering of fine grained sand- and marlstone at the test site. The sandstone was later proven to be highly permeable and fissured. Grain-size distributions were determined and the soil was classified as medium-low plasticity silty sand. Site instrumentation:Measurements of soil suction, groundwater level, soil volumetric water content, rain intensity and soil temperature were taken and combined with geophysical monitoring using Electrical Resistance Tomography (ERT) and investigations into subsurface flow by means of tracer experiments. Deformations were monitored during the experiment, both on the surface via photogrammetrical methods and within the soil mass, using a flexible probe equipped with strain gauges at different points and two axis inclinometers on the top and acoustic sensors. Instruments were installed mainly in three clusters at depths of 15, 30, 60, 90, 120, and 150 cm below the ground surface over the slope, including jet-fill tensiometers, TDRs, Decagon TDRs, piezometers, soil temperature sensors, deformation probes, earth pressure cells, acoustic sensors and rain gauges. A ring-net barrier (provided by Geobrugg AG) was set up at the foot of the slope to protect the road. Experiments: A sprinkling experiment was carried out in September 2008 to investigate the hydrological and mechanical response of the slope (Experiment 1), followed by a second one to trigger a landslide in March 2009 (Experiment 2).

Publications

1. Lehmann, P., F. Gambazzi, B. Suski, L. Baron, A. Askarinejad, S. M. Springman, K. Holliger, and D. Or (2013), Evolution of soil wetting patterns preceding a hydrologically induced landslide inferred from electrical resistivity survey and point measurements of volumetric water content and pore water pressure, Water Resour. Res., 49, 7992–8004, doi:[10.1002/2013WR014560](http://dx.doi.org/10.1002/2013WR014560).

2. Springman, S. M., Kienzler, P., Casini, F., & Askarinejad, A. (2009). Landslide triggering experiment in a steep forested slope in Switzerland. In 17th International Conference of Soil Mechanics and Geotechnical Engineering, Alexandria, Egypt (pp. 1698-1701). doi: [10.3233/978-1-60750-031-5-1698](http://dx.doi.org/10.3233/978-1-60750-031-5-1698)

cces

display_name: cces

landslide-testsite

display_name: landslide-testsite

landslides

display_name: landslides

pore-water

display_name: pore-water

pressure-perturbations

display_name: pressure-perturbations

tramm

display_name: tramm

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: TRAMM project Ruedlingen experimental landslide dataset, Switzerland


en: snow-depth snow-cover vertical-wind-motion saltation boundary-layer-winds

en: Data Compilation

en: Compressed TXT files

en:
The data correspond to the experiments presented and discussed in a paper regarding the interaction between turbulent wind fluctuations and snow saltation mass-fluxes (Paterna, 2016). Each of the nine data files corresponds to a different experiment presented in the paper and conducted in the winter 2014/2015 in the WSL/SLF cold wind tunnel in Davos. For each file the five columns indicate the time from the beginning of the experiment, the streamwise (u’) and the vertical (w’) wind velocity fluctuations, the streamwise (qx) and the vertical (qz) snow mass-flux components. From these time-series the scales of the snow saltation and of the turbulent flow are obtained with respect to the eddy-cycles and snow saltation cycles. From spectral analysis of the time-series a decoupling of the snow saltation from the turbulence forcing reveals two regimes of interaction: a turbulence-dependent regime occurring with weak saltation, and a turbulence-independent regime with strong saltation. Further details can be found at the link below.

Publication http://onlinelibrary.wiley.com/doi/10.1002/2016GL068171/abstract

boundary-layer-winds

display_name: boundary-layer-winds

saltation

display_name: saltation

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

vertical-wind-motion

display_name: vertical-wind-motion

en: Wind tunnel measurement data of drifting snow and turbulent wind fluctuations


en: nfi-photos visual-attractiveness adults-and-teenagers perception-of-ecosystem-services online-questionnaire-survey

en: SPSS-Dataset

en: Questionnaire French

en: Dataset in csv-format

en: Codebook to SPSS-file

media_type: application/pdf

en: Questionnaire German

media_type: PDF

en: Questionnaire Italian

en: This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the "WaMos meets LFI" (WML) project.

adults-and-teenagers

display_name: adults-and-teenagers

nfi-photos

display_name: nfi-photos

online-questionnaire-survey

display_name: online-questionnaire-survey

perception-of-ecosystem-services

display_name: perception-of-ecosystem-services

visual-attractiveness

display_name: visual-attractiveness

education

en: Education and science

en: Relationship between physical forest characteristics, visual attractiveness and perception of ecosystem services in urban forests


maintainer: Lisa Hülsmann

en: forest-reserves tree-growth inventory-data empirical-mortality-models tree-mortality

en: Calibration data

en:
A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory.

 Meta data
Reserve: Name of the forest reserve

Plot: Permanent plot number

 Tree data
Species: Latin species name (or species group, cf. _Quercus_ spp.)

y: Status of the tree with 0 = alive and 1 = dead

dt: Length of the mortality period (years)

DBH: Diameter at breast height (mm) of the second inventory

relBAI: Annual relative basal area increment (-) calculated based on the first and the second DBH measurement as the compound annual growth rate of the trees basal area

 Stand data
_All stand variables were calculated for the second inventory._

mDBH: Mean diameter at breast height (mm)

iqrDBH: Interquartile range of diameter at breast height (mm)

BA: Basal area per hectare (m2 ha-1)

N: Number of trees per hectare (ha-1)

 Climate data
_Both climate variables were calculated for the period between the second and the third inventory._

P: Mean annual precipitation sum (mm)

mT: Mean annual air temperature (°C)

en:
The dataset comprises > 90 000 records from inventories in 54 strict forest reserves in [Switzerland](https://www.wsl.ch/de/wald/biodiversitaet-naturschutz-urwald/naturwaldreservate.html) and [Lower Saxony / Germany](http://naturwaelder.de/) along a considerable environmental gradient. It was used to develop parsimonious, species-specific mortality models for 18 European tree species based on tree size and growth as well as additional covariates on stand structure and climate.

 Inventory data
Measurements had been conducted repeatedly on up to 14 permanent plots per reserve for up to 60 years with re-measurement intervals of 4 - 27 years. The permanent plots vary in size between 0.03 and 3.47 ha. The inventories provide diameter measurements at breast height (DBH) and information on the species and status (alive or dead) of trees with DBH ≥ 4 cm for Switzerland and ≥ 7 cm for Germany.

 Data selection
We excluded three permanent plots where at least 80 % of the trees died during an interval of 10 years, and mortality could be clearly assigned to a disturbance agent. Mortality in the remaining stands was rather low, with a mean annual mortality rate of 1.5 % and strong variation between plots from 0 to 6.5 % (assessed for trees of all species with DBH ≥ 7 cm).
We only used data from permanent plots with at least 20 trees per species to obtain reliable plot-level mortality rates even for species with low mortality rates (about 5 % during 10 years), and selected tree species occurring on at least 10 plots to cover sufficient ecological gradients. This led to a dataset of 197 permanent plots and 18 tree or shrub species: _Abies alba_ Mill., _Acer campestre_ L., _Acer pseudoplatanus_ L., _Alnus incana_ Moench., _Betula pendula_ Roth, _Carpinus betulus_ L., _Cornus mas_ L., _Corylus avellana_ L., _Fagus sylvatica_ L., _Fraxinus excelsior_ L., _Picea abies_ (L.) Karst, _Pinus mugo_ Turra, _Pinus sylvestris_ L., _Quercus pubescens_ Willd., _Quercus_ spp. (_Q. petraea_ Liebl. and _Q. robur_ L.; not properly differentiated in the Swiss inventories), _Sorbus aria_ Crantz, _Tilia cordata_ Mill. and _Ulmus glabra_ Huds..

 Predictors of tree mortality
We considered tree size and growth as key indicators for mortality risk. Radial stem growth between the first and second inventory and DBH at the second inventory were used to predict tree status (alive or dead) at the third inventory. To this end, the annual relative basal area increment (relBAI) was calculated as the compound annual growth rate of the trees basal area. Additional covariates on stand structure and climate comprise mean annual precipitation sum (P), mean annual air temperature (mT), the mean and the interquartile range of DBH (mDBH, iqrDBH), basal area (BA) and the number of trees (N) per hectare.

 Further information
For further information, refer to Hülsmann _et al_. (in press) How to kill a tree – Empirical mortality models for eighteen species and their performance in a dynamic forest model. _Ecological Applications_.

empirical-mortality-models

display_name: empirical-mortality-models

forest-reserves

display_name: forest-reserves

inventory-data

display_name: inventory-data

tree-growth

display_name: tree-growth

tree-mortality

display_name: tree-mortality

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: Calibration data for empirical mortality models of 18 European tree species