en: multifunctionality connectivity strategic-planning planning-principles green-infrastructure

en: Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure corresponding to items from 5 to 11 of the protocol. The data was discussed qualitatively in the research paper.

en:
The present dataset is part of the published scientific paper Grădinaru, S. R., & Hersperger, A. M. (2019). Green infrastructure in strategic spatial plans: Evidence from European urban regions. Urban forestry & urban greening, 40, 17-28.
The goal of this research was to conduct a comparative analysis of the integration of green infrastructure concept in strategic spatial plans of European Urban regions. Specifically, the paper has the following objectivs: 1) which principles of GI planning are followed in strategic plans of urban regions? 2) can we identify different approaches to GI integration into strategic planning?. The study focues on a sample consisting of 14 case studies spanning 11 countries. We retrieved the strategic plans from the websites of the planning authorities. The list of the reviewed planning documents can be found in Appendix A of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper.
The planning documents were read in order to address the protocol items. The answer to the protocol items in each of the first two categories (items 1–11) was documented as text, while the answer for the third category, namely items addressing the planning principles (items 12–36), was coded according to Table 1 of the article. As a result, we provide the folowing outputs:
•	GI_Dataset_1_Items_1-12.xlsx – available on request
o	Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure coresponding to items from 1 to 12 of the protocol. The data was discussed qualitativly in the research paper.
•	GI_Dataset_2_Items_12-36.csv – freely available
o	Results of the coding on principles of GI planning followed in strategic plans of urban regions– this data was derived from the coding procedure coresponding to items from 12 to 36 of the protocol. The data served as input for the classifications performed through hierarchical cluster analysis. This data is a detailed version of Appendix C in the paper.

connectivity

display_name: connectivity

green-infrastructure

display_name: green-infrastructure

multifunctionality

display_name: multifunctionality

planning-principles

display_name: planning-principles

strategic-planning

display_name: strategic-planning

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal


ceilometer-klosters

maintainer: EnviDat Support

title_for_slug: ceilometer-klosters

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

state: active

type: dataset

en: Ceilometer data Klosters

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.

format: SERVICE

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

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Ceilometer Klosters


wamos-meets-lfi-ganze-schweiz

title_for_slug: wamos-meets-lfi-ganze-schweiz

en: Explanation Dataset

media_type: application/pdf

format: PDF

en: Questionnaire German

en: Questionnaire French

en: Questionnaire Italian

en: Dataset in Excel-format

format: XLS

en: Codebook to dataset

en: Questionnaire English

en: Dataset SPSS

en: The data consists of a forest visitor survey conducted at 50 plots in the whole of Switzerland, once during the winter- and once during the summer season. Physical forest characteristics according to the Swiss National Forest Inventory NFI were collected from the same plots in winter and summer. Visibility was measured using terrestrial laser scanning. At some plots, sound measurements were also conducted.

forest-characteristics

display_name: forest-characteristics

seasons

display_name: seasons

visitor-preferences

display_name: visitor-preferences

visual-attractiveness-of-forest

display_name: visual-attractiveness-of-forest

en: WaMos meets LFI, ganze Schweiz


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

en: Post Processed TLS's

media_type: application/zip

en: Compressed dat files

format: ZIP

en: Data processing documentation

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

mimetype: application/zip

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

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

polar

display_name: polar

sea-ice

display_name: sea-ice

sea-ice-elevation

display_name: sea-ice-elevation

terrestrial-laser-scans

display_name: terrestrial-laser-scans

tls

display_name: tls

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


en: snowmelt precipitation soil-moisture hydrology climate-change discharge

en: Daily simulated data for the future period

en: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the period 1981-2100 in mm for the 307 catchments.

en: Shapefile of 307 catchments in Switzerland

en: Shapefile of the 307 medium-sized catchments in Switzerland. The "gridcode" provides the ID of the catchment used in the datafiles.

en: Daily simulated data for the reference period

en: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the reference period 1981-2017 in mm.

en:
Daily discharge and the related hydro-meteorological variables precipitation, snowmelt, and soil moisture are provided for current (1981-2017) and for future climate conditions (1981-2100) for 307 medium-sized catchments in Switzerland. The catchments have a median catchment area of 117 km². The 307 catchments together form a set representative of the climatological conditions and runoff characteristics in Switzerland. The four variables were simulated at a daily resolution using the hydrological model PREVAH. PREVAH is a conceptual process-based model that was run in this study in its fully distributed version on a 500 m grid (Viviroli et al. 2009a). For the calibration, runoff time series from 140 mesoscale catchments covering the different runoff regimes were used. The model calibration was conducted over the period 1993-1997. Verification was performed on the period 1983-2005 using (i) volumetric deviation (Viviroli et al. 2007) and (ii) benchmark efficiency (Schäfli et al 2007) as objective functions. The calibration and validation procedures are described in detail in Köplin et al. (2010). The parameters for each model grid cell were derived by regionalizing the parameters obtained for the 140 catchments with a procedure based on ordinary kriging (Viviroli et al. 2009b, Köplin et al. 2010).
The calibrated and validated model was then driven with transient meteorological data (precipitation, temperature, radiation, and wind) representing both reference (1981-2017) and future climate conditions (2018-2099). The data were derived from the CH2018 climate scenarios (NCCS 2018) provided by the Swiss National Centre for Climate Services (NCCS). They were obtained from climate experiments produced with different climate modeling chains, consisting of a global and a regional circulation model each, within EUROCORDEX for three representative concentration pathways (RCP) emission scenarios. Downscaled output of ten climate model chains derived by quantile mapping were considered. The focus was on the chains of the EUR-11 domain with a horizontal resolution of 0.11 degrees (roughly 12.5 km). The climate model chains (GCM, RCM, RCP, and grid resolution) used are listed below:

- ICHEC-EC-EARTH	DMI-HIRHAM5	2.6	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	4.5	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	8.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	2.6	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	4.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	8.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	4.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	8.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	4.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	8.5	EUR-11

*References*:

-	Köplin, N., D. Viviroli, B. Schädler, and R. Weingartner (2010), _How does climate change affect mesoscale catchments in Switzerland? - A framework for a comprehensive assessment_, Advances in Geosciences, 27, 111-119, doi:10.5194/adgeo-27-111-2010.
-	National Centre for Climate Services (2018), CH2018 - _Climate Scenarios for Switzerland_, Tech. rep., NCCS, Zurich.
-	Schäfli, B., and H. V. Gupta (2007), _Do Nash values have value?_, Hydrological Processes, 21, 2075-2080, doi:10.1002/hyp.6825.
-	Viviroli, D., J. Gurtz, and M. Zappa (2007), _The hydrological modelling system PREVAH. Part II - Physical model description_, Geographica Bernensia, 40, 1-89.
-	Viviroli, D., M. Zappa, J. Gurtz, and R. Weingartner (2009a), _An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools_, Environmental Modelling & Software, 24, 1209-1222, doi:10.1016/j.envsoft.2009.04.001.
-	Viviroli, D., H. Mittelbach, J. Gurtz, and R. Weingartner (2009b), _Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland-Part II: Parameter regionalisation and flood estimation results_, Journal of Hydrology, 377 (1), 208-225, doi:10.1016/j.jhydrol.2009.08.022.

climate-change

display_name: climate-change

discharge

display_name: discharge

hydrology

display_name: hydrology

precipitation

display_name: precipitation

snowmelt

display_name: snowmelt

soil-moisture

display_name: soil-moisture

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: Simulated future discharge and climatological variables for medium-sized catchments in Switzerland


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

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

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

avalanches-silvaprotect

display_name: avalanches-silvaprotect

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

increment-star

display_name: increment-star

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

rockfall-silvaprotect

display_name: rockfall-silvaprotect

site-quality

display_name: site-quality

tree-species-woody-species

display_name: tree-species-woody-species

en: Increment*


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

en: climate realizations

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

climate-change-impact

display_name: climate-change-impact

cryosphere

display_name: cryosphere

natural-climate-variability

display_name: natural-climate-variability

rain-on-snow

display_name: rain-on-snow

snow-water-equivalent

display_name: snow-water-equivalent

uncertainty

display_name: uncertainty

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


en: biodiversity records-database fungi-conservation national-data-center swissfungi fungi

en: Data extract 20191122

media_type: text/csv

en: Each record consists of the record ID, species name (Taxon_ID,Latin name, German- French- and Italian name if available), information of the location (Swiss coordinates, precision of the coordinates, elevation above sea level, municipality and canton), the date of observation, the person that has found the fungus (leg.), the person that has identified the fungus (det), the functional group that the fungus belongs to, as well as information on the conservation status of the species (red list status, conservation priority status, status in the [Nature and Cultural Heritage Act](https://www.admin.ch/opc/en/classified-compilation/19660144/index.html) (Nature_Cultural_Heritage_Act)). Furthermore, data on the habitat according to Delarze (Delarze, R., Gonseth, Y., Galland, P., Lebensräume der Schweiz, Ott Verlag, Thun, ISBN 3-7225-6749-1) and substrate is given.

format: CSV

en:
This dataset provides distribution data of fungi in Switzerland of the National Data and Information Centre, called [SwissFungi](https://swissfungi.wsl.ch/en/index.html). SwissFungi is a partner of [InfoSpecies](https://www.infospecies.ch/de/), the network of Swiss data and information centres for [fauna](http://www.cscf.ch/cscf/de/home.html), [flora](https://www.infoflora.ch/en/) and [fungi](https://swissfungi.wsl.ch/en/index.html). One of its main objectives is to document the spatial and temporal distribution of species in Switzerland.

The SwissFungi database currently contains more than 670'000 geo-referenced fungi observations, distributed throughout Switzerland. The oldest observations date back to 1770. A large portion of the records are from the last two decades of the last century to the present day. The database is continuously updated with new fungi records. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature.

The data from the distribution atlas of fungi in Switzerland are available for research and practice (nature conservation projects, environmental impact assessments etc.) and can be obtained via an [application form](https://www.infospecies.ch/de/assets/content/documents/Formular_Datenanfrage20190625.pdf). Please note the tariffs for data requests and submit your request directly to the [InfoSpecies](https://www.infospecies.ch/de/) office. Applications are usually answered within two working weeks. Details on the use of data are regulated in the current guidelines of the national data centers. Please note that the data center SwissFungi is not able to verify all incoming fungal records completely for a correct identification or coordinate errors and therefore cannot guarantee the correctness of the information.

License under [InfoSpecies](https://www.infospecies.ch/de/). Data is free of charge for research projects and available on request.

biodiversity

display_name: biodiversity

fungi

display_name: fungi

fungi-conservation

display_name: fungi-conservation

national-data-center

display_name: national-data-center

records-database

display_name: records-database

swissfungi

display_name: swissfungi

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: SwissFungi - Records Database for the Fungi of Switzerland


radiosondes

maintainer: Jörg Wieder

title_for_slug: radiosondes

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

en: Radiosond Template Files

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

en: Radiosond Sounding Files

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

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

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

wind

display_name: wind

wind-direction

display_name: wind-direction

en: Radiosondes


en: number-of-caught-persons avalanche-accidents number-of-dead-persons avalanche-fatalities avalanche-accident-statistics

en: Fatal avalanche accidents in Switzerland since 1970

en:
**When using this data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/data-and-monitoring/slf-data-service.html)**.

This data collection contains information concerning all known accidents by snow avalanches in Switzerland with at least one person involved (caught). The data set commences on 01/10/1970. After the completion of a hydrological year, the new data is added.

The following information is provided:

 * avalanche identifier
 * date of the accident
 * accuracy of the date in range of days before and after
 * canton
 * municipality
 * start zone point latitude
 * start zone point longitude
 * start zone point accuracy (in meters)
 * start zone point elevation (in meteres above sea level)
 * slope aspect (main orientation of start zone)
 * slope inclination (in degree, steepest point within start zone)
 * forecasted avalanche danger level 1 (first danger)
 * forecasted avalanche danger level 2 (second danger)
 * accident within the core zone (most dangerous aspect and elevation as mentioned in the forecast)
 * number of dead persons
 * number of caught persons
 * number of fully buried persons
 * activity/location of the accident party at the time of the incident

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-accidents

display_name: avalanche-accidents

avalanche-fatalities

display_name: avalanche-fatalities

number-of-caught-persons

display_name: number-of-caught-persons

number-of-dead-persons

display_name: number-of-dead-persons

envidat

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

en: Avalanche accidents in Switzerland since 1970/71


en: gen3sis pantropical-diversity-disparity mechanistic-modeling plate-tectonics paleoclimate

en: Script used to generate the configurations files to run the simulation experiment.

en: Dataset_S4.csv: simulation model parameters and diversity summary statistics. Including rate of temperature niche evolution (Tev), temperature niche width (TW), divergence threshold (S), Dispersal probability distribution shape (WeibullShape), model identifier (model), and random seed (seed). Also includes information on number of extinct species (n_extinct), number of extant species (n_extant), total species (total_diversity), Spearman correlation coefficient between absolute latitude and species richness (latitude_richness_cor), and diversity in the Neotropics (neotropical_diversity), Indomalaya (indomalayan_diversity), Afrotropics (afrotropical_diversity). Also, whether the simulation generated pantropical distribution of species (pantropicality_index), or the pantropical diversity disparity (PDD_index). Finally what time step the simulation exited at (0 for complete simulations) and the number of species in the ending timestep (running_diversity).

en: Dataset_S3.csv: - Paleoenvironmental reconstructions (details in the associated SI appendix) including latitude (y), longitude (x), and temperature data across 200 myrs at 170 kyr intervals.

en: Dataset_S1.csv includes species diversity counts for 237 clades in tropical moist forests (TMF) in the Neotropics (NeotropicalTMF), Indomalaya (IndomalayanTMF), Afrotropics (AfrotropicalTMF), All three TMF regions together (AllTMF), all non-TMF regions (NonTMF), and in total (Total). As well as whether the clade is pantropical (Pantropical_clade) and shows the pantropical diversity disparity (PDD_clade). Pantropical clades are found in all three TMF regions with at least one-third of the clades’ total diversity spread throughout these regions. Pantropical diversity disparity (PDD) clades show lower diversity in TMF in the Afrotropics than in the Neotropics and Indomalaya.

en: Dataset_S2.csv: Environmental and species richness data across 110 km x 110 km grid cells in Neotropical, Indomalayan and Afrotropical moist forest sites. Variables include x and y coordinates in the Behrmann equal area coordinate reference system, potential evapotranspiration (PET), mean annual temperature (MAT), mean annual precipitation (MAP), amphibian, mammal, bird and squamate reptile species richness and biogeographic region, as well as the first two principal components of a principal component analysis on PET, MAT and MAP (PC1, PC2). Environmental data is from CHELSA and ENVIREM and species richness summarised from published data (vertLife, MOL, IUCN, GARD).

en:
Datasets and R scripts

~~~~~Datasets

Dataset_S1.csv: Distribution of species diversity in plant and vertebrate clades. Total clade level diversity and species diversity in tropical moist forests (TMF) across the Neotropics, Indomalaya and Afrotropics. Pantropical clades are found in all three TMF regions with at least one-third of the clades’ total diversity spread throughout these regions. Pantropical diversity disparity (PDD) clades show lower diversity in TMF in the Afrotropics than in the Neotropics and Indomalaya.

Dataset_S2.csv: Environmental and species richness data across 110 km x 110 km grid cells in Neotropical, Indomalayan and Afrotropical moist forest sites. Variables include x and y coordinates in the Behrmann equal area coordinate reference system, potential evapotranspiration (PET), mean annual temperature (MAT), mean annual precipitation (MAP), amphibian, mammal, bird and squamate reptile species richness and biogeographic region, as well as the first two principal components of a principal component analysis on PET, MAT and MAP (PC1, PC2).

Dataset_S3.csv: Global reconstructed paleo-temperature estimates and spatial coordinates across 200 million years at 170,000 year intervals at 2 degree spatial resolution.

Dataset_S4.csv: Gen3sis model parameters and biodiversity summary statistics. Summary statistics include the number of extant species, the number of extinct species, the total number of species, the number of species within the tropical moist forest biome boundaries in the Neotropics, the Afrotropics and Indomalaya, the pantropical index, and the pantropical disparity index, as well as the running time-step and diversity of unfinished simulations.

Dataset_S5.csv: Net relatedness index (NRI) values for vertebrate clades showing an observed disparity in pantropical diversity in the Neotropical, Indomalayan and Afrotropical moist forest regions and associated P-values. Positive values indicate phylogenetic clustering, whereas negative values indicate phylogenetic overdispersion.

~~~~~Scripts

Script_1 - GLS.R. R script to replicate the linear modelling analyses.

Script_2 - Gen3sis_config_template.R. R script to generate the configurations files to run the simulation experiment.

Script_3 - Gen3sis_config_creator.R. R script to generate the configurations files to run the simulation experiment.

mechanistic-modeling

display_name: mechanistic-modeling

paleoclimate

display_name: paleoclimate

pantropical-diversity-disparity

display_name: pantropical-diversity-disparity

plate-tectonics

display_name: plate-tectonics

en: Data from: Earth history events shaped the evolution of uneven biodiversity across tropical moist forests


maintainer: Lyu

en: species-occurrence pinales species-richness fagales species-range-map species-distribution-model

en: Occurrence maps for species of Fagales and Pinales with few records

en: Point_buffer.zip includes 267 occurrence maps of species that do not have enough occurrence data for polygon mapping.

en: Species range maps of species in Fagales and Pinales (with enough occurrences)

en: species_map.zip includes species 1690 range maps generated based on the integrated method described in Lyu et al., (2022). File names with (polygon) indicate that this map is only generated by polygon mapping, as there are not enough occurrences for SDMs.

en:
This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022).
To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details).

fagales

display_name: fagales

pinales

display_name: pinales

species-distribution-model

display_name: species-distribution-model

species-occurrence

display_name: species-occurrence

species-range-map

display_name: species-range-map

species-richness

display_name: species-richness

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Species distribution maps of Fagales and Pinales (GDPlants)


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

en: Download Server

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

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

air-temperature

display_name: air-temperature

climate-change

display_name: climate-change

orographic-precipitation

display_name: orographic-precipitation

precipitation-amount

display_name: precipitation-amount

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

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


dischmex-meteorological-measurements

maintainer: Rebecca Mott

title_for_slug: dischmex-meteorological-measurements

en: dischma meteo-station dischmex meteorological-data meteorological-measurements

en: Meteorological measurements 2016

en: Location of the meteorological stations

mimetype: application/pdf

en: Meteorological measurement 2015

en: Overview sensors

en: data

en: Meteorological measurement 2014

en: Meteorological measurements 2014

en:
Meteorological measurements recorded in the Dischma valley from 2014-2016. In 2014 and 2015 we used 11 mobile weather stations from sensorscope to record meteorological parameter in the upper Dischma valley in the closer surroundings of the Gletschboden area. The meteorological stations are eqiupped with at least air temperature/humidity, wind velocity and wind direction sensors. Some stations are additionally equipped with precipitation, shortwave radiation and snow surface temperature sensors.
Three transects were installed at different aspects and were equipped with air temperature/humidity and wind sensors. Transect 1 (stations 2-4) provides meteorological Information on an east-north-east facing slope at elevations ranging between 2100 m and 2500 m. Transect 2 (stations 5-7) provides meteorological Information on a south-west slope and transect 3 (stations 8-10) on a north-west slope. Station 1 is fully equipped with meteorological sensors (temperature/humidity, wind, IR, up and downwand shortwave radiation and precipitation).
In 2016, mobile stations from sensorscope were replaced with six permanent meteorological stations. Meteorological stations 1-3 are equipped with an air temperature/humidity sensor, two wind speed sensors, a wind direction sensor and an incoming and outgoing shortwave radiation sensor. Stations 4 and 6 are equipped with an air temperature/humidity sensor and a wind speed/direction sensor. Station 5 is a equipped with an air temperature/humidity sensor, a wind speed/direction sensor, a snow surface temperature sensor, an incoming and outgoing shortwave radiation sensor and an incoming longwave radiation sensor.

dischma

display_name: dischma

dischmex

display_name: dischmex

meteo-station

display_name: meteo-station

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

en: DISCHMEX - Meteorological measurements


en: lfi mogli switzerland woody-species sdm

en: Tabelle S2

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

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

en: Map Access

media_type: text/plain

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

format: TXT

en: Tabelle S3

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

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

en: MoGLI Summary

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

en: Tabelle S1

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

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

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

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

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

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

lfi

display_name: lfi

mogli

display_name: mogli

sdm

display_name: sdm

switzerland

display_name: switzerland

woody-species

display_name: woody-species

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Distribution maps of common woody species for Swiss forests


maintainer: Franziska Gerber

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

en: Documentation

en: Documentation of the dataset

en: Static data for WRF - Topography

en: Static data for WRF - Soiltype bottom

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

en: Static data for WRF - Soiltype top

en: Static data for WRF - Landuse

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

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

cosmo-wrf

display_name: cosmo-wrf

cryowrf

display_name: cryowrf

landuse

display_name: landuse

static-data

display_name: static-data

topography

display_name: topography

wrf

display_name: wrf

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

en: High resolution static data for WRF over Switzerland


permanent-vegetation-quadrats-lwf

title_for_slug: permanent-vegetation-quadrats-lwf

en: species-composition icp-forests ground-vegetation swiss-forest-lab long-term-forest-ecosystem-research

en: Data Request

en:
Ground vegetation is an important compartment of the ecosystem in terms of biodiversity and it takes an active part in the general functioning of the ecosystem. It is also a useful bio-indicator of the site conditions and its monitoring may enable the detection of environmental changes. Ground vegetation relevés were carried out repeatedly at 17 LWF plots during in the the period between 1994 and 2011. In 2013, ground vegetation was surveyed at an additional LWF plot (Laegeren). The cover of all plant species occurring in 16 1-m2 quadrats, distributed over the 43 x 43 m intensive monitoring subplot was visually assessed. Seedlings and saplings were also counted and their position within the quadrat was noted in order to assess tree regeneration.

 Purpose:

To assess the species diversity of ground vegetation and detect possible environmental changes using its bio-indicator value.

 Manual Citation:

 * Kull, P., 1997. Kapitel D2. Vegetationsaufnahmen. In: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-9.

 * Canullo R, Starlinger F, Granke O, Fischer R, Aamlid D, 2016: Part VI.1: Assessment of Ground Vegetation. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest Ecosystems, Eberswalde, 12 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)

 Paper Citation:

 * Thimonier A, Kull P, Keller W, Moser B, Wohlgemuth T (2011) Ground vegetation monitoring in Swiss forests: comparison of survey methods and implications for trend assessments. Environmental Monitoring and Assessment, 174: 47-63. [doi: 10.1007/s10661-010-1759-y](http://doi.org/10.1007/s10661-010-1759-y)

ground-vegetation

display_name: ground-vegetation

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

species-composition

display_name: species-composition

swiss-forest-lab

display_name: swiss-forest-lab

en: Permanent vegetation quadrats LWF


en:
Increment with ingrowth minus the mortality. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.

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

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

avalanches-silvaprotect

display_name: avalanches-silvaprotect

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

net-increment-star

display_name: net-increment-star

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

rockfall-silvaprotect

display_name: rockfall-silvaprotect

site-quality

display_name: site-quality

tree-species-woody-species

display_name: tree-species-woody-species

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Net increment*


en: ice laboratory-study snow-atmosphere-interactions atmosphere chemistry

en:
The table lists all NEXAFS raw data with the following columns:

* name:						raw file name
* xRayEkinRange_eV: 		Smallest and largest X-ray photon energy in eV
* scientaPassEnergy_eV: 	Pass energy of electron analyzer in eV
* scientaDwellTime_ms: 		Dwell time of electron analyzer in ms
* regionName:				O K-edge for all data
* scientaAcquisitionMode: 	Fixed energy (snapshot) mode
* scientaEkinRange_eV: 		Smallest and largest energy of the kinetic energy window used for detection in this work
* scientaEkinStep_eV		Not defined in fixed energy mode
* scientaLensMode:			Tune table
* scientaRegionIterations: 	Iterations (sweeps)
* scientaSequenceIterations: Iterations (sweeps)
* sampleTemp_dC: 			Temperature as monitored at the sample holder in °C
* cellPressure_mbar: 		Total pressure in measurement cell in mbar
* iceTemp_dC: 				Temperature of ice sample calculated based on vapour pressure (assuming total pressure equals partial pressure of water) in °C
* smplX_mm: 				positon of measurement spot, x dimension in mm
* smplY_mm: 				positon of measurement spot, y dimension in mm
* smplZ_mm: 				positon of measurement spot, z dimension in mm
* sealingTemp:				temperature at base of sample manipulator
* lastModifiedDatestr: 		time and date when data aquisition was finished
* sample: 					sample description

en: Processed data as shown in Figure 4 of publication. X-ray energy in eV in column one, electron count intensity in arb. units in column 2.

en: Near Ambient Pressure partial Auger-Meitner electron yield X-ray absorption spectra and photon flux data. X-ray energy in eV in column 1, analyzer electron count intensity in arb. units in column 2.

en: Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice.

atmosphere

display_name: atmosphere

chemistry

display_name: chemistry

ice

display_name: ice

laboratory-study

display_name: laboratory-study

snow-atmosphere-interactions

display_name: snow-atmosphere-interactions

en: An ice oxygen K-edge NEXAFS spectroscopy data set on gas-phase processing


maintainer: Virginie Marques

en: fish fjord plankton dna-metabarcoding biodiversity environmental-dna marine

en: Metabarcoding data

en: Raw metabarcoding data for both primers (teleo and 18S) and corresponding file to perform the demultiplexing and primer trimming

en: Code, data and figures

en: Here there are all the data and code to reproduce the figures and analysis presented in the paper, with a README.md file

en:
This dataset contains the raw environmental DNA data associated with the publication *Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord* in the journal Polar Biology (2023).

 Methods

**Sampling**
We sampled the Lilliehöök fjord on the west coast of Spitsbergen (Svalbard, Norway) over 3 days from 3 to 5 of August 2021. Samples were taken from the glacier front up to the fjord mouth of the Krossfjorden system, around 30 km long, after the Lilliehöök fjord merged with the mouth of Möller fjord. The fjord’s maximum depth has been recorded at 373 m (Svendsen et al. 2002) and has no sill at its entrance, thereby facilitating water exchange with the open ocean of the West Spitsbergen Current. We used a research vessel to sample 5 sites for a total of 15 samples, sampling 3 depths per site (3-m, chlorophyll a maximum and 85-m, unless sea floor was shallower). Shallow and intermediate samples between 3-m and 12-m represent ~35-L of water filtered in-situ using long tubing and a peristaltic pump, and all other deeper samples were taken from a total of 3 Niskin bottles (General Oceanics), representing 22-L of water sampled per sample. Water was filtered through a VigiDNA filtration capsule (SPYGEN) with a 0.20-µm pore size using an Athena peristaltic pump (Proactive Environmental Products, Bradenton, Florida) with a flow rate of ~1-L/min. Each sample was handled with single use tubing and gloves.

**Molecular**
To perform the amplification, we used two sets of primers: teleo (forward: ACACCGCCCGTCACTCT, reverse: CTTCCGGTACACTTACCATG; Valentini et al. 2016) and the universal eukaryotic 1389F/1510R primer pair, amplifying the V9-18S rDNA gene (Amaral-Zettler et al. 2009) (forward: TTGTACACACCGCCC, reverse: CCTTCYGCAGGTTCACCTAC).

 Data content:

+ Metabarcoding data: This zip file contains the 2 sequencing libraries filtered to only retain the samples used in the present study.
+ Code, data and figure: This zip file contains all data and code to reproduce the figures and the analysis in the study, with an associated README explaining the content of each folder.

 Additional informations
For more details, please see the Methods in the associated publication: DOI: 10.1007/s00300-023-03187-9.

biodiversity

display_name: biodiversity

dna-metabarcoding

display_name: dna-metabarcoding

environmental-dna

display_name: environmental-dna

fish

display_name: fish

fjord

display_name: fjord

marine

display_name: marine

plankton

display_name: plankton

en: Data: Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord


mimetype: text/csv

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

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-fatalities

display_name: avalanche-fatalities

fatalities-in-or-around-buildings

display_name: fatalities-in-or-around-buildings

fatalities-in-settlements

display_name: fatalities-in-settlements

fatalities-in-the-backcountry

display_name: fatalities-in-the-backcountry

fatalities-on-transportation-corridors

display_name: fatalities-on-transportation-corridors

envidat

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

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


en: swiss-forest-lab ground-vegetation symptoms-of-visible-injuries ambient-air-quality icp-forests long-term-forest-ecosystem-research ozone

en: Data Request

en:
Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone-induced symptoms are being assessed at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone concentrations and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling.

 Purpose:

Ozone risk assessment, i.e. to investigate relationships between ozone exposures and ozone-induced, visible symptoms

 Manual Citation:

 * Schaub M, Calatayud V, Ferretti M, Brunialti G, Lövblad G, Krause G, Sanz MJ, 2016: Part VIII: Monitoring of Ozone Injury. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest Ecosystems, Eberswalde, Germany, 14 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)

 Paper Citation:

 * Schaub M, Häni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu¨nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf)

 * Schaub M and Calatayud V (2013) Assessment of Visible Foliar Injury Induced by Ozone. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 205-221. ISBN: 9780080982229. [doi: 10.1016/B978-0-08-098222-9.00011-X](https://doi.org/10.1016/B978-0-08-098222-9.00011-X)

ambient-air-quality

display_name: ambient-air-quality

ground-vegetation

display_name: ground-vegetation

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

ozone

display_name: ozone

swiss-forest-lab

display_name: swiss-forest-lab

symptoms-of-visible-injuries

display_name: symptoms-of-visible-injuries

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Symptoms of O3 injuries LWF


en: runoff snow alptal first-order-catchment meteorological-data subalpine

en: Manual snow measurements Alptal (1968-2022)

en:
Snow depth, snow density and snow water equivalent have been measured since winter 1968/69 along more than 15 transects distributed across the Alptal valley representing different altitudes, aspects and land use (closed forest and open meadows). Length of the snow courses: 20 to 30 m. The measurement interval has been weekly to monthly.
More details about the locations and methods can be found in Stähli, M.; Gustafsson, D., (2006). Long-term investigations of the snow cover in a subalpine semi-forested catchment. Hydrological Processes, 20, 411-428. doi: 10.1002/hyp.6058.

en: Daily meteorological and snow data (1968-2022) Alptal

en:
Air temperature, dew point temperature, relative humidity, wind speed, precipitation and global radiation have been measured (since 1981) on a meadow (Erlenhöhe) in the Erlenbach catchment. It is located at 1'220 m a.s.l. with an exposure towards west. In order to assess the spatial variability of precipiation, two additional rain gauges have been running in the Vogelbach and Lümpenenbach catchment, respectively.
For the period before 1981, meteorological variables have been extrapolated from the nearby MeteoSwiss station Einsiedeln (ca. 15 km north) to the WSL station Erlenhöhe using a linear correction factor for air temperature, wind speed and precipitation.
Snow depth has been recorded automatically since 2003. Snow water equivalent has been calculated using two numerical models: COUP model (Jansson, 2012) and DeltaSnow model (Winkler et al., 2021)

Jansson, P.-E. (2012). COUP model: model use, calibration, and validation.Transactions of the ASABE, 55(4), 1335–1344.
Winkler, M., et al. (2021): Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model, Hydrol. Earth Syst. Sci., 25, 1165-1187, 10.5194/hess-25-1165-861 2021.

en: Daily runoff data from three steep torrents in the Alptal (1968-2022)

en: Runoff, water temperature and electrical conductivity have been measured since 1968 at the outlet of three torrential catchments (with an area of approximately 1 km2 each) located in the Alptal valley: Erlenbach, Lümpenenbach and Vogelbach. This data set provides daily mean values; data at 10-min resolution can be provided on request for the period 1984-2022 (contact: alptal@wsl.ch).

en:
This data set includes 54 years of hydrometeorological measurements from small (first-order) catchments in the pre-alpine valley Alptal. Here we provide daily mean values; values in sub-daily resolution can be provided on demand.
Runoff has been measured at the outlet of three small (first-order) catchments of approximately 1 km2 area: Erlenbach (two independent runoff measurements), Vogelbach and Lümpenenbach. The catchments are similar with regard to geology (Flysch) and soil conditions (clay soils), but differ in forest coverage (20 to 60%). A detailed description of the catchments can be found at https://www.wsl.ch/alptal .
Runoff in these small catchments is typically very dynamic and can temporally carry large amounts of sediment and large wood. Thus, the accuracy of the measurements at very large flow is limited.
Meteorological variables have been measured on a meadow (Erlenhöhe) located in the Erlenbach catchment at 1220 m a.s.l. using a standard meteorological station (incl. ventilated air temperature and heated rain gauges). In addition, precipitation has also been recorded at two other locations (in the Vogelbach and Lümpenenbach catchments).
Snow measurements have been conducted weekly to monthly since 1968 at more than 15 locations (30-m transects) representing different altitudes, aspects and land uses (meadow, forest). In addition, snow depth has been recorded continuously since 2003 at Erlenhöhe, and for this location we also include a simulation of snow depth and SWE (using the numerical models COUP and DeltaSnow) that assimilates the manual weekly snow-course measurements. Details on these snow measurements can be found in Stähli, M. and Gustafsson, D. 2006. Hydrol. Proc., 20, 411-428. doi: 10.1002/hyp.6058.

Further information on the methods and sensors can be found at https://www.wsl.ch/alptal .

A first version of this data set (for the period 1968-2017) was uploaded in June 2018 at the occasion of the 50-year anniversary. This original data set was updated in February 2021 (with data from 2018 and 2019), and this data set was used for a longterm trend analysis, submitted for publication in a special issue of Hydrological Processes. A second update of the data set (with data from 2020 to 2022) was uploaded in March 2023.

alptal

display_name: alptal

first-order-catchment

display_name: first-order-catchment

meteorological-data

display_name: meteorological-data

runoff

display_name: runoff

snow

display_name: snow

subalpine

display_name: subalpine

en: Longterm hydrological observatory Alptal (central Switzerland)


en: roads jupyter buffer density geospatial gis

en: Road Density Calculation (Python Notebook) online

en: Plots

en: Random plot locations

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

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

buffer

display_name: buffer

density

display_name: density

geospatial

display_name: geospatial

gis

display_name: gis

jupyter

display_name: jupyter

roads

display_name: roads

envidat

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

en: Dataset for OGRS 2018 publication


maintainer: Mountain Hydrology and Mass Movements

en: windstorm-fatalities lightning-fatalities flood-fatalities landslide-fatalities ice-avalanche-fatalities rockfall-fatalities debris-flow-fatalities natural-hazard-fatalities earthquake-fatalities

en: Natural hazard fatalities per year in Swizterland since 1946.

en:
This dataset contains the number of fatalities due to flood, debris flow, landslide, rockfall, windstorm, lightning, ice avalanche, earthquake and other processes like roof avalanche or lacustrine tsunami for each year since 1946.
The following information is contained (by column and column title):

* year
* total number of hazard fatalities
* number of fatalities by flood (German: Hochwasser, Überschwemmung). Flood includes people drowned in flooded or inundated areas or carried away in streams under high-water conditions.
* number of fatalities by debris flow (German: Murgang).
* number of fatalities by landslide (German: Erdrutsch). Landslide includes people killed by landslides and hillslope debris flows (German: Hangmure).
* number of fatalities by rockfall (German: Steinschlag, Fels- und Bergsturz).
* number of fatalities by windstorm (German: Sturm). Windstorm includes people killed by falling objects or trees during very strong wind conditions and people who drowned in lakes because their boat capsized during such conditions.
* number of fatalities by lightning (German: Blitz).
* number of fatalities by ice avalanche (German: Eislawine).
* number of fatalities by earthquake (German: Erdbeben).
* number of fatalities by other processes like roof avalanche, lacustrine tsunami (German: andere Prozesse wie Dachlawine, Tsunami im See).

The data was collected based on newspaper research. For more information please refer to

_Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747-2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016._

The data collection is financed by the FOEN (with exception of the collection of the avalanche fatalities). The data contains the official statistics of the FOEN on fatalities due to flood, debris flow, landslide, rock fall and avalanche.

Restrictions: The data set is not complete. Only fatalities in or around settlements and on open transportation routes are included. More precisely, fatalities were not collected, when persons exposed themselves to a great danger on purpose. Or fatalities during leisure activities which are connected to a higher risk were not included (this includes e.g. canoeing or river surfing during flood, canyoning, mountaineering, climbing, walking or driving on a closed road).

Fatalities by avalanches are collected at the WSL Institute for Snow and Avalanche Research SLF. You can download the avalanche fatalities per hydrological year [here](https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936) and per calendar year [here](https://www.envidat.ch/dataset/avalanche-fatalities-per-calendar-year-since-1936). For a direct comparison with the fatalities presented here, please download the data set with the calendar years and do not consider fatalities in the backcountry (tour) or in terrain close to ski areas (offpiste).

debris-flow-fatalities

display_name: debris-flow-fatalities

earthquake-fatalities

display_name: earthquake-fatalities

flood-fatalities

display_name: flood-fatalities

ice-avalanche-fatalities

display_name: ice-avalanche-fatalities

landslide-fatalities

display_name: landslide-fatalities

lightning-fatalities

display_name: lightning-fatalities

natural-hazard-fatalities

display_name: natural-hazard-fatalities

rockfall-fatalities

display_name: rockfall-fatalities

windstorm-fatalities

display_name: windstorm-fatalities

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Number of natural hazard fatalities per year in Switzerland since 1946


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

en: Moth monitoring

en: See ReadMe

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

flight-traps

display_name: flight-traps

gypsy-moth

display_name: gypsy-moth

larch-budmoth

display_name: larch-budmoth

nitrogen

display_name: nitrogen

nun-moth

display_name: nun-moth

phenol

display_name: phenol

spongy-moth

display_name: spongy-moth

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


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

en: Radiation Data 18.04.2019

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

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

en: Sky-view fraction along the cablecar transect

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

mimetype: text/plain

en: Seasonal sub-canopy radiation data

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

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

en: Radiation Data 25.4.2018

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

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

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

forest

display_name: forest

fractional-snow

display_name: fractional-snow

longwave-radiation

display_name: longwave-radiation

shading

display_name: shading

shortwave-radiation

display_name: shortwave-radiation


en: long-term-forest-ecosystem-research long-term-monitoring wdb swiss-forest-lab ozone

en: Data request

en: In order to quantify effects of ozone on Swiss forest ozone flux calculations are needed. Currently 5 stations are operated to measure the ozone load on forests. These data form the basis for ozone flux calculations. The ozone flux calculation is carried out with the DO3SE model (Büker et al. 2012). This can then be used for epidemiological evaluations for the Swiss forest (Thomas et al. 2005, Braun et al. 2014, Braun et al. 2017, Braun et al. 2020, Braun et al. 2021, Braun et al. 2022).

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

long-term-monitoring

display_name: long-term-monitoring

ozone

display_name: ozone

swiss-forest-lab

display_name: swiss-forest-lab

wdb

display_name: wdb

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Ozone measurement and analysis in the Intercantonal Forest Observation Program


en: swiss-forest-lab stem-diameter tree-growth icp-forests long-term-forest-ecosystem-research

en: Data Request

en:
Continuous stem radius measurements to investigate carbon balance and water relations of trees

 Purpose:

Assessment of growth and water related stem changes

icp-forests

display_name: icp-forests

stem-diameter

display_name: stem-diameter

tree-growth

display_name: tree-growth

en: Automated point dendrometer measurements at LWF sites


en: precipitation drought-2018 prevah low-flow soil-moisture hydrological-simulations discharge

en: Soil moisture

en: Simulated plant-available soil moisture for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

en: Discharge

en: Simulated discharge for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

en: Shapefile of 307 catchments in Switzerland

en: Shapefile of 307 catchments for which simulations of precipitation, discharge, soil moisture, and low flow are provided. The OBJECTID entry corresponds to the data columns in the textfiles of the simulated data provided.

en: Low-flow storage

en: Simulated low-flow storage for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

en: Precipitation

en: Simulated precipitation for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

en: The dataset provides simulated 1) precipitation, 2) discharge, 3) soil moisture, and 4) low-flow simulations for 307 medium-sized catchments in Switzerland for the period 1981-2018. The data were simulated using the hydrological model PREVAH in its gridded-version. The simulated time series are provided at daily resolution. A detailed description of the modeling approach can be found in Brunner et al. 2019 submitted to NHESS.

discharge

display_name: discharge

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

precipitation

display_name: precipitation

prevah

display_name: prevah

soil-moisture

display_name: soil-moisture

en: Hydro-meteorological simulations for the period 1981-2018 for Switzerland


en: vpd 3-pg climate-change drought tree-rings growth-limitation

en: Limiting factors

en: Spatially resolved simulated environmental constraints during the 1961-1990 and 1991-2018 period.

en: Seasonal variation in environmental constraints (vapor pressure deficit – VPD, air temperature, and soil moisture) on tree growth for the potential distribution range of seven widespread Central European tree species. We simulated environmental constraints on growth fusing 3-PG model or the species’ potential distribution range within the forested area of Switzerland on a 1×1 km grid for seven dominant tree species: _Larix decidua_, _Picea abies_, _Abies alba_, _Fagus sylvatica_, _Acer pseudoplatanus_, _Pinus sylvestris_, and _Quercus robur_. For this purpose, we simulated the growth of these tree species in monocultures with the average climate observed during 1961–1990 or 1991-2018. The stands were initialized as 2-year-old plantations with an initial density of 2,500 trees ha-1 and simulated until the age of 30 years. For each simulated month, we obtained the relative contribution of environmental constraints (VPD, temperature, and soil water) on tree growth.

climate-change

display_name: climate-change

drought

display_name: drought

growth-limitation

display_name: growth-limitation

tree-rings

display_name: tree-rings

vpd

display_name: vpd

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Environmental constraints on tree growth


en: snow-depth break-detection snow-measurements homogenisation climate

en: HOMER dHS1

en: Input files for HOMER

en: ACMANT HSavg

en: Climatol HSavg

en: HOMER HSavg

en: Breakpoints (R data file)

en: Break points detected by all three methods and draft metadata table

en: Station list

en: List of stations used in this study. This file also serves as the station input file for HOMER

en: Station relocations

en: Metadata compiled from various sources

en: ACMANT dHS1

en: Climatol dHS1

en:
Data set consists of monthly mean values for snow depth and days with snow on the ground intended for the use of break detection with ACMANT, Climatol and HOMER. List and coordinates of stations used as well as metadata and break detection results from all three methods is included.

 Columns
Monthly means for each hydrological year: Nov, Dec, Jan, Feb, Mar, Apr with May to Oct set to zero

break-detection

display_name: break-detection

climate

display_name: climate

homogenisation

display_name: homogenisation

snow-depth

display_name: snow-depth

snow-measurements

display_name: snow-measurements

en: Input data for break point detection of Swiss snow depth time series


total-timber-volume

title_for_slug: total-timber-volume

en:
Volume of stemwood with bark of all living and dead trees and shrubs (standing and lying) starting at 12 cm dbh. This corresponds to the sum of the volumes of growing stock and deadwood. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.

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

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

ownership

display_name: ownership

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

total-timber-volume

display_name: total-timber-volume

tree-state-alive-dead

display_name: tree-state-alive-dead

tree-status

display_name: tree-status

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Total timber volume


en: whole-tree-volume model nfi allometric branchwood stemwood

en: Datasets for deriving functions for the stem- and branchwood volume in the Swiss National Forest Inventory

en: Documentation of the data and review in 2022.

en: linked data set with stem and branchwood data

en: Dataset comprising all measured variables in order to easily derive complete above-ground tree wood volume based on 38’841 trees including 14’038 trees with measured branchwood data.

en:
In the Swiss National Forest Inventory (NFI) the volume of the stem and of large (≥ 7cm in diameter) and small branches is estimated based on allometric functions. These functions were developed based on data collected within the permanent plot network of the Experimental Forest Management (EFM) sites at WSL (David I. Forrester; Hubert Schmid; Jens Nitzsche (2021). The Experimental Forest Management network. EnviDat. doi: 10.16904/envidat.213). The data were converted to digital format in two separate steps in the mid-1970s for stemwood and the mid-1980s for branchwood.
The dataset on stemwood volume contains 38’864 single tree data for the mean crosswise diameter at two meter sections along the stem plus an additional measurements at 1.3 m (i.e. DBH) where the diameter is greater than or equal to 7 cm (i.e. threshold of merchantable wood) and the lengths of the stem from the base to the threshold of merchantable wood and to the tree top. The measurements were collected on 768 EFM sites in the period 1888 to 1974. The dataset on branchwood is based on a subset of the stemwood data and contains in the raw format information on 14'712 single trees. It includes aggregated data from the stemwood dataset, i.e. the DBH, the stem-diameter at 7 m from the base, and the tree height from the base to the top, as well as the measured volume of large and small branches.
In 2022, the metadata of both datasets were checked, values were examined for plausibility and duplicated entries. Duplicates were removed as far as possible and the branchwood volume data were appended to the stemwood dataset to obtain a final, single file with matching single tree data. Following this evaluation the final dataset consisted of a total of 38’841 trees including 14’038 trees with measured branchwood data.

allometric

display_name: allometric

branchwood

display_name: branchwood

model

display_name: model

nfi

display_name: nfi

stemwood

display_name: stemwood

whole-tree-volume

display_name: whole-tree-volume


maintainer: Amelie Fees

en: snowpack-simulation avalanche-activity time-lapse-photography glide-snow-avalanche avalanche

en: README

en: README.txt explaining the dataset and columns of tables in more detail.

en: Glide distances for opening glide-cracks

en: This folder contains all glide-cracks where glide-distances were extracted from time-lapse photographs. Glide-distances were calculated in x-direction (width) and y-direction (downslope, length) by georeferencing the images.

en: Glide-snow avalanche activity

en: Table that contains all glide-snow avalanche events that were detected in time-lapse photographs (season 2008/09 to 2021/22). The columns are explained in the README.txt

en: SNOWPACK simulations

en: SNOWPACK simulations (.pro and .smet) covering season 2008/09 to 2021/22 at 10 virtual locations on Dorfberg. The locations correspond to the most common glide-snow avalanche paths.

en:
This dataset includes the processed data of the glide-snow avalanche activity and dynamics on Dorfberg (Davos, Switzerland) covering seasons 2008/09 to 2021/22. This dataset was described in the research article:

Fees, A., van Herwijnen A., Altenbach, M., Lombardo, M., Schweizer, J.: Glide-snow avalanche characteristics at different time-scales extracted from time-lapse photography, Annals of Glaciology, 91

We extracted the dynamics of opening glide-cracks and the glide-snow avalanche activity from time-lapse photographs. Glide-snow avalanches were separated into surface and interface events using the liquid water content which was simulated with SNOWPACK at 10 virtual stations on Dorfberg.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

glide-snow-avalanche

display_name: glide-snow-avalanche

snowpack-simulation

display_name: snowpack-simulation

time-lapse-photography

display_name: time-lapse-photography

en: Glide-snow avalanche activity on Dorfberg, Davos, Switzerland


swiss-municipal-spatial-planning-survey

title_for_slug: swiss-municipal-spatial-planning-survey

en: urban-sprawl policy-analysis spatial-planning settlement-development densification

en: Municipal Planning Survey

en: german

en: Municipal Planning Survey Data for SPSS

en: SPSS

en: francais

en: Glossary

en: Municipal Planning Survey Data for Stata

en: STATA 13

en: Survey of spatial planning instruments and the organization of land use planning in Swiss municipalities. In 2014, the survey was sent to all Swiss municipalities in letter and online form. The response rate of 69% (i.e. 1619 of 2352 municipalities at this time) results in a representative sample of Swiss municipalities. The survey contains questions on the implementation of 20 specific planning instruments and the decade they had been implemented at first, as well as details on the local planning regimes.

densification

display_name: densification

policy-analysis

display_name: policy-analysis

settlement-development

display_name: settlement-development

spatial-planning

display_name: spatial-planning

urban-sprawl

display_name: urban-sprawl

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Swiss Municipal Spatial Planning Survey


maintainer: Matthias Haeni

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

en: Field station data (NAF)

en: Data file.

en: Data plot of the forest station (NAB)

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

en: Site photo of the field station (NAF)

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

mimetype: image/jpeg

en: Site aerial photo (Nationalpark)

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

en: Forest station data (NAB)

en: Data plot of the field station (NAF)

en: Site photo of the forest station (NAB)

en: Site photo of the forest 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 Nationalpark in Switzerland where one station is located within a natural coniferous forest stand (NAB) with mountain pine (_Pinus mugo_; 210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Nationalpark is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


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

en: Site aerial photo (Othmarsingen)

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

en: Site photo of the field station (OTF)

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

en: Data plot of the field station (OTF)

en: Forest station data (OTB)

en: Data plot of the forest station (OTB)

en: Field station data (OTF)

en: Site photo of the forest station (OTB)

en: Site photo of the forest station from above (viewing downwards the slope).

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Othmarsingen in Switzerland where one station is located within a natural deciduous forest stand (OTB) with European beech (_Fagus sylvatica_; 120-140 yrs) and lime trees (_Tilia sp._; 120-140 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, OTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Othmarsingen is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Yves Bühler

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

en: Data Access

en:
- DSM (10 cm resolution)
- Ortho (3 cm resolution)
- HS (10 cm resolution)
- original RGB drone images
- Agisoft Metashape Processing Report

All products are also available as a cloud optimized geotiff.

en: To map and assess snow depth on different dates, 9 flights were conducted in the winter season of 2020/21 at the Latschüelfurgga in Davos. The data was captured with a Sony RX1R II mounted on a Wingtra drone and was processed with the Agisoft Metashape software. High-resolution DSMs, orthomosaics and snow height rasters, as well as the original RGB images from each flight are available.

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

uav

display_name: uav

wingtra

display_name: wingtra

en: Photogrammetric Drone Data Latschuelfurgga


en: Script diameter and species modelling

en: This R script can be used to derive the uni- and bimodal Weibull log-likelihood regression models (in short: uni- and bimodal model), the species composition model and the main results presented in Mey et al. 2021 using data from the fourth Swiss NFI ('data_NFI4_diameter_modelling.rds' and 'data_NFI4_species_modelling.rds') and the R functions stored in 'functions_diameter_modelling.R'.

en: Data diameter modelling

en:
This rds file contains a list of 5085 data frames required to reproduce the uni- and bimodal diameter model. Each data frame comprises single-tree diameters ('dbh') and assigned representation factors ('repfa') at a single Swiss NFI4 plot.

* 'dbh' refers to the diameter at breast height (130 cm)
* 'repfa' refers to the representation factor used to scale up to one hectare

en: Functions diameter modelling

en: This R file contains the functions used to produce the generalised diameter distributions in Mey et al. 2021.

en: Data Figures 2 and 4

en:
This csv file contains the data required to reproduce Figure 2 and Figure 4.

* 'stand_type' states whether a row corresponds to NFI4 average summary statistics describing a generalised beech-dominated, spruce-dominated or an unspecified forest stand in Switzerland in its unimodal or bimodal form (e.g. 'beech.uni' refers to a generalised unimodal beech-dominated stand)
* 'md' refers to the mean diameter
* 'dq10' refers to the 10% quantile of the diameter
* 'dq90' refers to the 90% quantile of the diameter
* 'spr' refers to the spread between minimal and maximal diameter
* 'nph' refers to the number of stems per hectare
* 'o.qmd' refers to the overstorey quadratic mean diameter
* 'rho.emp' refers to proportion parameter (rho) determined by breakpoint setting
* 'ba' refers to the basal area per hectare
* 'drel' refers to the relative diameter ('drel' = (d - min(d)) / (max(d) - min(d)))
* 'temp' refers to the mean winter temperature (1988-2017, Dec-Feb)
* 'dev' refers to the development stage of the stand (e.g. 'Young growth')
* 'ftyp' refers to the potential climax forest type (e.g. 'Beech' refers to a beech-dominated stand)
* 'str' refers to the stand structure
* 'tillering' indicates whether the observed tree species on the NFI plot correspond to 'ftyp' ('TRUE') or not ('FALSE')
* 'prodreg' refers to the Swiss production region (ecoregion)
* 'fowner' refers to the forest ownership ('Public' or ' Private')

en: Data species modelling

en:
This rds file contains a data frame with single-tree and stand data required to reproduce the species composition model:

* 'clnr' refers to the plot number
* 'spec' refers to the main tree species group (12 groups)
* 'ba' refers to the basal area per hectare
* 'nph' refers to the number of stems per hectare
* 'drel' refers to the relative diameter ('drel' = (d - min(d)) / (max(d) - min(d)))
* 'temp' refers to the mean winter temperature (1988-2017, Dec-Feb)
* 'dev' refers to the development stage of the stand (e.g. 'Young growth')
* 'ftyp' refers to the potential climax forest type (e.g. 'Beech' refers to a beech-dominated stand)
* 'str' refers to the stand structure
* 'tillering' indicates whether the observed tree species on the NFI plot correspond to 'ftyp' ('TRUE') or not ('FALSE')
* 'prodreg' refers to the Swiss production region (ecoregion)
* 'fowner' refers to the forest ownership ('Public' or ' Private')

en:
The files refer to the data and R code used in Mey et al. "From small forest samples to generalised uni- and bimodal stand descriptions" (2021) _Methods in Ecology and Evolution_.

Generalised stand descriptions are coming from the simultaneous examination of samples that are representative for a specific target area (here, Switzerland) and link available information about forest stand attributes. They combine the modelling of uni- or bimodal diameter distributions and species compositions, i.e. the shares of stems of individual species. Generalised stand descriptions may be used to interpret tree species diversity, regeneration and harvest potentials on a plot-level basis, and to initialise forest models with representative stand data.

The data stored here were derived from the fourth campaigns of the Swiss National Forest Inventory (NFI). The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php).

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The file 'Data Figures 2 and 4' is publicly available and contains the data used to produce the Figures 2 and 4 published in the paper.

The files 'Data diameter modelling' and 'Data species modelling' contain all the data required to reproduce the diameter and species model building. The access to these two files is restricted as they contain raw data from the fourth Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.

The files 'Script diameter and species modelling' and 'Functions diameter modelling' are publicly available and provide the R code used to derive the generalised stand descriptions from the Swiss NFI data.

forest-model-initialisation

display_name: forest-model-initialisation

forest-stand-description

display_name: forest-stand-description

national-forest-inventory

display_name: national-forest-inventory

small-forest-samples

display_name: small-forest-samples

tree-species-composition

display_name: tree-species-composition

weibull-function

display_name: weibull-function

en: Generalised stand descriptions in Switzerland


en: consistency plan-evaluation bucharest land-use-planning strategic-planning

en: Plan statements for external consistency analysis: Evidence from Bucharest’s spatial plans

en: In total, 24 files are available with plan statements extracted from 10 spatial plans. Each file is extracted based on the items of the framework for plan external consistency analysis. Data is in Romanian.

en:
The present dataset is part of the published scientific paper Bacău, S., Grădinaru, S. R., & Hersperger, A. M. (2020). Spatial plans as relational data: Using social network analysis to assess consistency among Bucharest’s planning instruments. Land Use Policy, 92.
The goal of this paper was to first develop a theoretical framework for external consistency assessment in spatial plans and then to test the framework with ten spatial plans of the Bucharest (Romania) region. Specifically, the paper has the following workflow: (i) to develop a framework for consistency assessment covering four categories of external consistency; (ii) to extract relevant plan statements from all plans on the four categories; (iii) to assign one-way, symmetrical and contradictory relationships between the extracted plan statements; and (iv) to assess consistencies, inconsistencies and contradictions between plans using directed and valued network analyses. All results were then validated by applying questionnaires to local experts.
The study focuses on a sample consisting of 10 spatial plans of Bucharest that: (1) are currently in force, (2) have spatial implications, (3) involve different administrative levels and (4) come from different planning sectors. The list of the reviewed planning documents can be found in Table 2 of the paper. The framework of consistency assessment contains 24 items, which can be found in Table 1 of the paper. All planning documents were read in respect to all items of the framework in order to extract plan statements used in the analysis. As a result, we provide the plan statement extracted from 10 plans on the 24 items of the framework. All data is in Romanian. The data was discussed qualitatively in the research paper.

bucharest

display_name: bucharest

consistency

display_name: consistency

land-use-planning

display_name: land-use-planning

plan-evaluation

display_name: plan-evaluation

strategic-planning

display_name: strategic-planning

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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