title_for_slug: salvage-logging-due-to-insects

en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh removed from the forest between two inventories due to damage that occurred, in this case insects, and not due to silvicultural planning. This theme is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).  The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.

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

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

economic-region

display_name: economic-region

production-region

display_name: production-region

salvage-logging-due-to-insects

display_name: salvage-logging-due-to-insects

en: Salvage logging due to insects


maintainer: Matthias Haeni

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

state: active

type: dataset

en: Site photo of the field station (BAF)

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

format: SERVICE

mimetype: image/jpeg

en: Site photo of the forest station (BAB)

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

en: Data plot of the forest station (BAB)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Field station data (BAF)

media_type: text/csv

en: Data file.

format: CSV

mimetype: text/csv

en: Forest station data (BAB)

en: Data plot of the field station (BAF)

en: Site aerial photo (Beatenberg)

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

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

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

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


en: image-matching digital-surface-model photogrammety 3d point-cloud

en: 2016 Hillshade DSM for Vegetation Heigth Model (current)

media_type: text/plain

format: TXT

en: Thumbnail (JPG)

en: Hillshade of the digital surface model (DSM), calculated from digital aerial stereo images. The image data was acquired by the Federal Office of Topography swisstopo. The resolution of the DSM is 1 m x 1 m.

digital-surface-model

display_name: digital-surface-model

image-matching

display_name: image-matching

photogrammety

display_name: photogrammety

point-cloud

display_name: point-cloud

en: Hillshade for Vegetation Height Model NFI


maintainer: Frank Hagedorn

en: dissolved-organic-phosphorus fertilization beech seasons zero-tension-lysimeters nitrogen forest-topsoil nutrient-fluxes phosphorus soil-temperature

format: XLS

en:
Data on dissolved organic and inorganic phosphorus and nitrogen concentrations in leachates and their corresponding
fluxes from the litter layer, the Oe/Oa horizon, and the A horizon of two German beech forest sites.
Leachate samples were taken in April 2018, July 2018, October 2018, Feb./Mar. 2019, and July 2019
with zero-tension lysimeters after artificial irrigation. Soil samples were taken in July 2019.
For more details please refer to the publication.

beech

display_name: beech

dissolved-organic-phosphorus

display_name: dissolved-organic-phosphorus

fertilization

display_name: fertilization

forest-topsoil

display_name: forest-topsoil

nitrogen

display_name: nitrogen

nutrient-fluxes

display_name: nutrient-fluxes

phosphorus

display_name: phosphorus

seasons

display_name: seasons

soil-temperature

display_name: soil-temperature

zero-tension-lysimeters

display_name: zero-tension-lysimeters

en: Phosphorus and nitrogen leaching from beech forest soils


maintainer: EnviDat Support

en: european-alps species-distribution-models global-change phylogenetic-diversity zonation functional-diversity plants land-use-change taxonomic-diversity migclim conservation beta-diversity

en: Data Access

format: GeoTIFF

en:
This repository contains extensive data for the European Alps:
  - Observations of ~3,500 plant species
  - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal)
  - Flora migration rates (categorical) and ecological preferences (continuous indicator values)
  - Regional maps of barriers to migration and water bodies at 100-m resolution
  - Sampling effort, distance to roads and cities predictors at 100-m resolution
  - Present and future abundances over the study region at 1-km resolution (~2,000 species)
  - Present and future multifaceted and uniqueness of the European Alps' Flora at 1-km resolution
  - Present and future conservation recommendations at 1-km resolution (26 current and future strategies)
  - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees)
  - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them

beta-diversity

display_name: beta-diversity

conservation

display_name: conservation

european-alps

display_name: european-alps

functional-diversity

display_name: functional-diversity

global-change

display_name: global-change

land-use-change

display_name: land-use-change

migclim

display_name: migclim

phylogenetic-diversity

display_name: phylogenetic-diversity

plants

display_name: plants

species-distribution-models

display_name: species-distribution-models

taxonomic-diversity

display_name: taxonomic-diversity

zonation

display_name: zonation

en: Present and future multifaceted plant diversity, uniqueness and conservation in the European Alps


maintainer: Zhao

en: planning austin driving-factors urban-land-use land-dynamics

en: Potential driving factors of urban transformations of Austin over 25 years

media_type: application/zip

format: ZIP

en: In this study, the Austin metropolitan area, Texas, U.S., one of the fastest urban transformations and transformations regions, is selected to test the hypothesis that spatial planning and policies are important factors of urban transformations. Despite ample previous work in understanding the interactions between human and urban form transformation at specific areas, the actual interventions and outcomes of planning and policies (e.g., ‘smart growth’) on urban forms have been poorly measured. In this study, the potential influencing factors of urban transformations of Austin over 25 years were selected and collected.

austin

display_name: austin

driving-factors

display_name: driving-factors

land-dynamics

display_name: land-dynamics

planning

display_name: planning

urban-land-use

display_name: urban-land-use


en: nitrogen picea-abies co2 soil face forest

en: Resin bags

en: Ammonium, nitrate and their 15N in resin bags

en: Gross N mineralisation and nitrification

en: Gross rates of N mineralisation and nitrification (15N dilution)

en: Collection points

en: Collection points under the trees

en: Soil C and N

en: Analyses of C, N and their stable isotopes in the soil, and soil pH

en: Water analyses

en: Analyses of water, especially soil solution

en: Collection of soil solution

en: Soil solution collected by suction cups

en: Data obtained in the free-air CO2 enrichment (FACE) experiment at Hofstetten, NW Switzerland, between 2009 and 2016. This dataset contains analyses of the soil solution throughout the experiment, especially for nitrate, as well as different analyses done at the end of the experiment: ammonium and nitrate captured by ion-exchange resin bags and extracted from soil cores, gross N mineralisation and nitrification measured by isotope dilution.

face

display_name: face

forest

display_name: forest

nitrogen

display_name: nitrogen

picea-abies

display_name: picea-abies

soil

display_name: soil

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: Nitrogen availability under trees exposed to CO2 enrichment (FACE)


full-content-of-wsl-fauna-database

title_for_slug: full-content-of-wsl-fauna-database

en: collections database arthropods invertebarates wsl

en: Updated export with semicolon-delimited columns instead of comma delimited

en:
Query executed to retrieve the complete dataset from WSL Fauna Database
FIDO_global_extract 2023-03-08_16-27-28 WSL-AllData-AllTaxa.sql compressed in zip-archive

en: Complete dataset of all projects and data collected in WSL Fauna Database

en: Complete extract of Fauna Database of WSL, containing all projects and all taxa. Meant as exchange and citation platform for sharing the data with the national data centre 'Centre Suisse de la Cartographie de la Fauna (CSCF)', and Info Fauna.

arthropods

display_name: arthropods

collections

display_name: collections

database

display_name: database

invertebarates

display_name: invertebarates

wsl

display_name: wsl

en: Full content of WSL Fauna Database


maintainer: Anita C. Risch

en: mineralization global soil-biology grassland nutrient-network soil-chemistry worldwide soil

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

bioclim.T.ann: Mean annual temperature (°C)

bioclim.T.var: Temperature variability (°C)

bioclim.T.q.wet: Temperature of the wettest quarter (°C)

bioclim.T.q.dry: Temperature of the driest quarter (°C)

bioclim.T.q.warm: Temperature of the warmest quarter (°C)

bioclim.P.ann: Mean annual preciptiation (mm)

bioclim.P.q.wet: Precipitation of the wettest quarter (mm)

ch.pH: pH

ch.WSK.vol: Water holding capacity (vol%)

ch.bulkdensity.gcm3: Bulk density (g cm-3)

ch.porespace: Pore space (vol%)

ch.sand.perc: Soil texture: sand (%)

ch.silt.perc: Soil texture: silt (%)

ch.clay.perc: Soil texture: clay (%)

ch.NO3start.mgg: NO3 at the beginning of the incubation (mgkg)

ch.Ntot.perc: Total soil N (%)

ch.Ctot.perc: Total soil C (%)

ch.Corg.perc: Soil organic C (%)

ch.netNminfield.mgkgd: Realised soil net N mineralisation (mg kg-1 d-1)

ch.netNminlab.mgkgd: Potential soil ne N mineralisation (mg kg-1 d-1)

archaea.cpg.log: Ammonia oxidising archaea (archaeal amoA gene copies g-1 soil)

bacteria.cpg.log: Ammonia oxidising bacteria (bacterial amoA gene copies g-1 soil)

micr.biom: Microbial biomass (ug g-1 soil)

ch.CtoN: C to N ratio

ch.netNnitriffield.mgkgd.sqrt: Realised soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniffield.mgkgd.sqrt: Realised soil net ammonification, transformed (mg kg-1 d-1)

ch.netNnitriflab.mgkgd.sqrt: Potential soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniflab.mgkgd.sqrt: Potential soil net ammonifiation, transformed (mg kg-1 d-1)

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

Paper Citation:

Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2

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

We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide


en: Request Data

en: This dataset is hosted by IAP and therefore, access to the dataset must be requested to the original data provider.

en: Additional publications

media_type: text/html

format: HTML

en:
The IAP has been monitoring forest health since 1984 on behalf of cantonal forest and environmental offices and in collaboration with the Federal Office for the Environment. In the Intercantonal Forest Observation Program (WDB) project, the health, growth and nutrient supply of beech, spruce and oak in managed forest ecosystems are studied on about 190 forest plots. The plots cover the most important gradients of Swiss forests, such as different climatic conditions in precipitation, temperature, as well as differences in soil and in the input of air pollutants such as nitrogen and ozone. Currently (date 2020) 94 plots with 6268 beech trees, 75 plots with 4547 spruce trees, and 50 plots with 1858 oak trees are surveyed in the Intercantonal WDB. Annual investigations include crown condition and tree loss by mortality, uprooting and stem breakage. Every 4 year stem increment is measured on all 12’673 trees and 8 branches are harvested by helicopter very 4 years and subsequently examined for the following parameters:

- Nutrient analysis in foliage (N, P, K, Ca, Mg, Mn)
- Leaf area and weight
- Shoot growth (retrospective for Norway spruce, beech and oak)
- Discoloration and parasite infestation
- Fructification of beech (retrospectively)

In addition, soil samples for analysing the chemistry of the solid phase are taken every 12 years. Also changes in the ground vegetation is assessed every 12 years. On a subset of the plots soil solution is sampled monthly *[(Soil solution monitoring of the Intercantonal Forest Observation Program)](https://www.envidat.ch/dataset/soil-solution-monitoring-of-the-intercantonal-forest-observation-program)*. In addition, soil water potential and soil moisture are measured in a subset of sites of the Intercantonal Forest Observation Program *[(Soil moisture and soil water potential measurements of the Intercantonal Forest Observation Program)](https://www.envidat.ch/dataset/soil-water-measurements-wdb)*.

These data allow to gain a unique understanding of the main factors influencing the forest and the most important changes, which are very valuable especially in the context of climate change. The insights are of great importance for cantonal and regional forest planning as well as for national and international determinations of critical loads and limits (e.g. CLempN, UNECE CLRTAP).

iap

display_name: iap

long-term-data-sets

display_name: long-term-data-sets

long-term-monitoring

display_name: long-term-monitoring

swiss-forest-lab

display_name: swiss-forest-lab

wdb

display_name: wdb

en: Intercantonal Forest Observation Program – Interkantonale Walddauerbeobachtung (WDB)


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

en: Standing dead trees 2003 - 2017

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

en: Lying deadwood 2017

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

en: Standing living trees 2017

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

en: Data Description

en: Describes the available tables

en: Regeneration 2017

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

en: Standing dead trees 2017

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

en: Standing living trees 1981 - 2017

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

en:
 Supplementary Data Sample Plot Inventory Sihlwald

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

fagus-sylvatica

display_name: fagus-sylvatica

forest-inventory

display_name: forest-inventory

forest-reserves

display_name: forest-reserves

natural-forest-dynamics

display_name: natural-forest-dynamics

sihlwald

display_name: sihlwald

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal


en: lichen moss climate-change photobiont bryophyte-life-strategy liverwort madeira-island biodiversity endemic lichen-growth-form functional-taxonomic-groups functional-taxonomic-group endemic-species climate-change-impact bryophyte elevational-gradient lichen-growthform disturbance

en: MadCrypto – Bryophyte and macrolichen diversity in laurel forests of Madeira

en:
This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).

The dataset was used for the paper
Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science.

en:
This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).

The dataset was used for the paper
Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science 30: 1122–1133.

The excel file contains 5 sheets:
1) Plot information
2) Bryophyte data with species specific information, separated per substrate
3) Macrolichen data with species specific information, separated per substrate
4) Bryophyte data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 2 and 4 might therefore differ slightly.
5) Macrolichen data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 3 and 5 might therefore differ slightly.

biodiversity

display_name: biodiversity

bryophyte

display_name: bryophyte

bryophyte-life-strategy

display_name: bryophyte-life-strategy

climate-change

display_name: climate-change

climate-change-impact

display_name: climate-change-impact

disturbance

display_name: disturbance

elevational-gradient

display_name: elevational-gradient

endemic

display_name: endemic

endemic-species

display_name: endemic-species

functional-taxonomic-group

display_name: functional-taxonomic-group

functional-taxonomic-groups

display_name: functional-taxonomic-groups

lichen

display_name: lichen

lichen-growth-form

display_name: lichen-growth-form

lichen-growthform

display_name: lichen-growthform

liverwort

display_name: liverwort

madeira-island

display_name: madeira-island

moss

display_name: moss

photobiont

display_name: photobiont

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.


snow-drift-station-snow-and-air-data

maintainer: Benjamin Walter

title_for_slug: snow-drift-station-snow-and-air-data

en: snow-height raclets snow snow-depth temperature snow-temperature

en: Snow Surface Temperature

en: The snow surface temperature (°C) was measured with a Campbell SI-111 at 0.7 m above ground. The time is given in UTC+1.

en: Relative Humidity

en: The relative humidity (%) was measured with a Campbell CS-215 at a height of 2.2 m above ground. The time is given in UTC+1.

en: Snow Depth

en: The snow depth (cm) was measured with a Lufft SHM-31 which was mounted at 1.7 m above ground. The time is given in UTC+1.

en: Air Temperature

en: The air temperature (°C) was measured with a Campbell CS-215 which was mounted at a height of 2.2 m above ground. The time is given in UTC+1.

en: Snow and air data was monitored at Gotschnagrat (LON: 46.859 LAT: 9.849) by an infrarot radiometer (Campbell SI-111) for snow temperature (°C), a snow height sensor (Lufft SHM-31) for snow height change (cm) and a temperature and humidity sensor (Campbell CS-215) for air temperature (°C) and relative humidity (%). No filter was applied to the sensors and the smapling frequency was 1 Hz.

raclets

display_name: raclets

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-height

display_name: snow-height

snow-temperature

display_name: snow-temperature

temperature

display_name: temperature

en: Snow Drift Station - Snow and Air Data


permanent-vegetation-quadrats-lwf

title_for_slug: permanent-vegetation-quadrats-lwf

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

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

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Permanent vegetation quadrats LWF


en: roads buffer geospatial jupyter density gis

en: Road Density Calculation (Python Notebook) online

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

mimetype: application/zip

en: Plots

en: Random plot locations

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


en: rcm cryosphere toposcale cordex software downscaling

en:
Model code and documentation for the downscaling model TopoCLIM which provides methods to downscale climate timeseries from CORDEX RCM data. This scheme specifically addresses the need for hillslope scale atmospheric forcing timeseries for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps.

A working example is provided in this code archive but for full running of the scheme TopoSCALE is required https://doi.org/10.5194/gmd-7-387-2014 with code available at https://github.com/joelfiddes/tscaleV2. Standard library dependencies are given in the python requirements.txt of the archive with installation instructions in the README.md.

cordex

display_name: cordex

cryosphere

display_name: cryosphere

downscaling

display_name: downscaling

rcm

display_name: rcm

software

display_name: software

toposcale

display_name: toposcale

en: TopoCLIM v1.1 code


maintainer: Tobias Wechsler

en: ecosystem-protection hydropower decision-making integral-water-management gis

format: SHP

en: Indicators

en: The indicators capture supporting ecosystem functions.

en:
 A spatial dataset and tool to simultaneously assess hydropower potential and ecological potential of the Swiss river network (Version 2016)

 Introduction
The steadily growing demand for energy and the simultaneous pursuit of decarbonisation are increasing interest in the expansion of renewable energies worldwide. In Switzerland, various funding projects have been launched to promote technologies in the field of renewable energies and their application as quickly as possible. With the introduction of a funding instrument in 2009, the number of projects submitted to produce renewable energies increased rapidly. The applications for small hydropower plants (≤ 10 MW) were correspondingly numerous. However, the assessment of the environmental impact and its comparison with hydropower importance is still not standardized.
To provide a basis for decision-making, a methodology was developed to determine the overall hydropower potential of a region. A detailed assessment of each river reach, and the systematic and holistic assessment of small hydropower projects at a regional scale are combined here. The assessment of a river reach is conducted at the river space (i.e., the river and adjacent areas) and at the surrounding landscape level. The HYDROpot_integral methodology was developed as part of Carol Hemund's dissertation (2012) at the University of Bern. It allows the evaluation of river reaches holistically, regarding ecological, social, economic and cultural criteria. As a second part of the overall project, the theoretical hydropower (or hydraulic) potential was calculated for the entire river network, which complemnets the spatial assessment. In particular, it is possible to classify river reaches into those that are more suitable for hydropower production (=”use”) and those that are more suitable for protection.

 Material and method
The HYDROpot_Integral method was developed and tested on the basis of cantonal and national data (Hirschi et al. 2013). The method relies on 73 geodata sets. This holistic assessment is the key element of the entire assessment procedure. Its aim is to quantify the importance of the ecosystem functions in terms of services. The river network (GWN07) is divided into reaches of about 450m and for each reach two study units are defined. The river space (RS) records the ecosystem functions of the water body and the nearby riparian area. The length of the RS is 315 m on average in Switzerland and a maximum of 450 m, whereas the width is based on the FOEN definition (BWG 2001: 18f) and varies between 7-107 m. The surrounding landscape (SLS) is the second survey unit that records the ecosystem functions of the surrounding area over a range of 21 m to 321 m. The SLS is calculated over three times the RS width. The length of the SLS is identical to the length of the RS. The ecosystem functions are divided into three types: regulating (service A), cultural (service B) and provisioning (service C) functions. Accordingly, the assessment of the functions is divided into three parts and three values are assigned to each river reach. The more functions there are and the greater their performance, the higher these values are and the more important the corresponding functions are. Hence, these values quantify the importance of the ecosystem functions and the ecological, cultural and economic ecosystem services of each river reach. The concatenation of ecosystem services results in a value (ABC) that can occur in 125 different versions due to the chosen five-level value scale; i.e. each digit of the three-digit number sequence can be assigned a value between 1 and 5. Each of the 125 combinations, and thus each river reach, has its own characteristics determined by the assessments of the three function types. To record the suitability, the combinations are ranked according to their ecological, cultural and economic ecosystem services. These rules mean that the combination that is most suitable for hydropower production at minimum cost in terms of ecological and cultural ecosystem services and has a high economic potential is ranked first; rank 125 indicates the highest ecological and cultural ecosystem services and the lowest economic services and is therefore most suitable for protection. A river reach that is excluded from hydropower use due to legislation, a so-called priority reach, is given rank 126 from the outset and specially marked. A more detailed description of the methods can be found in Hirschi et al. 2013 [Link]. The dataset presented here presents the latest state of the HYDROpot_integral methodology applied at the national level. Only national data that is easily accessible was used in the preparation of the dataset. The cantonal data, such as renaturation and revitalization, would have to be requested by each canton individually and was excluded here. The nationwide value synthesis was made with R.
A list of data sources can be found here [link to text file]
A list of all parameters can be downloaded here [link to PDF and text files]

 Dataset description
Data is presented as a single shapefile. It contains the river network and all assessment results obtained with HYDROpot_Integral.

 Changes in the methodology compared to the original method (Hirschi et. al 2013)

 * RS_A11 Ecomorphology: recorded for the whole of Switzerland and zero values equated with NA;
individual cantons such as Zug and St. Gallen have no mapped values according to the modular
concept of the federal government, Valais and Graubünden only the main valleys, Ticino and Fribourg not
completely (BAFU 2009).
 * RS_A14 Renaturation and revitalization data: not centrally available at the time of data collection.
centrally available, therefore values in GR were equated with NA.
 * RS_A15 Dilution ratio at wastewater treatment plants (WWTPs) for discharges: Zero values
equal to NA.
 * RS_A20 Water flow: use WASTA (2013) with hydroelectric power plants (> 300 kW) under
Federal control and dams serving hydroelectricity (Dam, as of 2013).
 * RS_C05 Synoptic hazard maps: are cantonally managed at the time of data collection,
Values in GR are marked with a 5 so that the systematics in the decision tree is not affected.
is affected.
 * Water quality (RS_A15, RS_A16, RS_A17, RS_A18, RS_A19): for the evaluation of the function type.
A Nature, it is important whether the median of the five values is less than or equal to 3 in total. This
evaluation is based on the decision tree for evaluating GR (Hirschi et al. 2013:22).
Therefore, an evaluation of the station data is made where critical and possible river segments
with poor quality (median less than 3) exist. Only two longer and one short
sections in Switzerland receive a lower median than 3 for water quality.
 * SLS_B06 Visibility: For 99 percent of the river segments (30,733 of 31,062) in the canton of
Bern (2015 reduced version), the landscape area is considered to be visible. Due to this high number
of sections, a large number of viewpoints in the layer of Swisstopo and
the computation time and computability in ArcGIS, the landscape area is classified as generally viewable
(equal to 1).
16 Method
Additional indicators were added (see Appendix B.2):
 * SLS_A21 Dissection
 * SLS_A22 Forest areas
 * SLS_B03 Hiking trails
 * SLS_B10 Residential and vacation homes
 * SLS_B11 Tourist infrastructure
 * SLS_C01 Landfill
 * SLS_C03 Infrastructure
 * SLS_C05 Industry
 * SLS_C06 Agricultural land
Not to be added, although present to some extent:
 * SLS_B06 Cultural assets of national importance: here, too, the calculability of the visibility analysis is
for the whole of Switzerland is limited
 * SLS_A15 Legally binding protection and land use planning: the individual river sections are not
clearly designated, i.e. no geodata exist
The following data are also not supplemented, as they are cantonal data:
 * SLS_A10 Cantonal nature reserves
 * SLS_A16 Forest reserves
 * SLS_A17 Cantonal inventories and contractually protected areas

decision-making

display_name: decision-making

ecosystem-protection

display_name: ecosystem-protection

hydropower

display_name: hydropower

integral-water-management

display_name: integral-water-management


en: switzerland land-use urbanization land-use-change landscape-services modeling statistical-model

en: Scripts for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

en:
Scripts ordered by their main purpose within sequential folders in this ReadMe marked with

Within the folders the capital letters refer to the parallel or sequential use of the scripts; scripts here in ReadMe are marked with

 00_Function Scripts
 Functions.r
Script containing several functions for analyzing statistical models: cohen.kappa, meva.table, D2, max-Tss, plot-TSS

 Service_functions.R
Script containing functions for calculating all services and returning sums (and median for bd and rec) as well as rasters

 01_Data Preparation
 A_AS_raster_preparation.R
Preparation of the land use rasters for the years 1985, 1997, and 2009 based on the original Arealstatistik (AS/ASCH); Assigning our categories and assining new values to all "construction sites" for calibrating the models
For model projection starting point (2009) and for service calculations (all ASCH years) the "other" categories were assigned to the land use categories based on dominant neighbor, so that there were no empty cells
Creation of water & mountainous area raster that stayed stable throughout the projections
Creation of MFH raster (2009), to have as base for all new projection years, as this category never becames any other

 B_Factors_preparation.R
Preparation of the factor rasters that were used to calculate service values per cell (see Table 2): LWfactor, Transportation, Vol, Rivers, Lakes, slope, TWW, Hazard_risk

 B_Predictors_preparation.R
Preparation of the raster of the predictors used for calibrating (ASCH years) and projecting (stable) the transition models that do not depend on the land use categories: Temperature (ASCH years), solar radiation (stable), Public transportation access (stable), Elevation (stable), Workers density (ASCH years), Population change (ASCH transitions)

 B_Regions_preparation.R
Preparation of bioregions as raster to be used to mask asxxxx later to calculated services and land use area just for one region

 C_Pred_proj.R
Projecting the dynamic predictors into the future for projecting the land use transitions with the model: Linear extrapolations for workers densities and population; more detailed extrapolation of temperature (based on data for all stations that matched between the temp table we used for the calibration period and the projections from CS2M, and then making a spatial interpolation for all years based on the interpolation of temperature for all years per station)

 02_Model calibration
 A_Bioreg_pred_corr.R
Calculating correlation between the predictors for each bioregion and land use category, Output are those with too high correlation so that one predictor can be removed from transitions were both predictors occur

 A_trans_numbers_bioreg.R
Calculate transition numbers for each bioregion to decide which transitions to primarily consider

 B_Bioreg_ext_eval.R
Calculating TSS based on the external evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 85-97, evaluation data: 97-09)

 B_Bioreg_int_eval.R
Calculating TSS based on the internal evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 70%, evaluation data: 30%)

 C_Bioreg_TSS_analysis.R
Extracting TSS numbers from internal and external evaluation, calculating mean and median for each transition for each region for each evaluation method and saving as csv per region to be able to select only the transitions with high enough model performance

 D_Bioreg_Models_for_prediction.R
Calibration of the models that were then used to project land use transitions into the future; calibrated separately for each bioregion

 E_Residuals_trans_models.R
Calculation of Moran's I statistics to analyze the residuals of the land use transition models

 03_Projections
 A_Bioreg_Projections_run.R
Master script for: Extrapolaitng into the future with the regional models and then assembling for each time step to calculate new AS predictors; Several Outputs: txt file to test for NAs in Predictors, txt file with random seed number for sampling, txt file with times for calculation steps (check progress of script while running), csv sheet with projected transition frequencies, tif-rasters for every region and time step, pdf with raster plot for every region and time step

 04_Service calculation
 A_Bioreg_as_services.R
Calculate services per bioregion for years 1985, 1997, 2009

 A_Service_calculation_bioregions.R
Extracting service numbers(sums) per bioregions for all projection runs and storing them in a list of matrixes to be available for further plotting; can be run for swiss wide projections or regional ones

 05_Analysis
 A_Bioreg_ASCH_development.R
Extracting the occurrence of each category per region for all runs (first part), then for ASCH years, and then plotting them nicely; only works for regional projections

 A_Bioreg_Predictors_importanceD2.R
Calculating D2 for each transition and each considered predictor from bivariate models to get value of importance

 B_Bioreg_Pred_imp_analysis.R
Using calculated D2 to sort Predictors according to their importance for each transition

 A_Bioreg_Spatial_results_analysis.R
Plotting randomly sampled runs for ASCH and services to analyses their spatial development

 A_Bioreg_Transition_numbers_check_v1.R
Analyzing the frequency for all transitions for all regions and producing the point plots for each transition separately

 A_Bioreg_Transition_numbers_check_v2.R
Analyzing percentage and numbers of all transitions but plotting them per region and originating ASCH category

 B_Transition_Analysis.R
Further Analysis of transition numbers

 A_Service_analysis.R
Plotting of the service development for bioregions in several ways for analyzing

en: Data for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

en:
The provided folder structure follows the set up of the scripts.
Data is only provided when it has been fully produced by us, otherwise dummies are given.
For data that will be produced by one of the scripts, no dummies are given.
The folders may contain sub-folders, which would be filled if the scripts were all run

 00_Analysis_Data
Data produced throughout the process of the calibration and projection that is later needed for analysis

 01_Raw_Data
Primary data from external sources that were used to compile the rasters and numbers used during the calibration and projection of landscape transitions as well as service calculation

 02_Prepared_Data
Data that will be prepared for further use by scripts are put in here
Additionally it contains the parameter sheets for the transition predictors that we compiled

 03_Model_Data
 Model outputs
Folder were the projection models statistics will be saved
 Models
Folder into which the actual transition models will be saved
 Residuals
Folder into which data on models residuals will be saved for future use

 04_Projected_Data
The output of the projections will be saved into this folder

 05_Service_Data
Any service calculations will be saved in here

 06_Results_Analysis
Any Analysis plots and tables as well as intermediate data will be saved here in the respective sub-folder

en:
Data and scripts of publication:

Madleina Gerecke, Oskar Hagen, Janine Bolliger, Anna M. Hersperger, Felix Kienast, Bronwyn Price, Loïc Pellissier (2019) Assessing potential landscape service trade-offs driven by urbanization in Switzerland. Palgrave communications.

Contains land use projections for Switzerland and scripts and data for these projections as well as the calculation of landscape services.

Data Folder:
Contains sub-folder with the data necessary for this study (provided were no copyright issues, otherwise placeholders with descriptions), and folders where produced data may be stored

Scripts Folder:
Contains scripts organized into subfolders depending on their purpose

Note: Some abbreviations within the scripts and data are derived from German words and not English.

land-use

display_name: land-use

land-use-change

display_name: land-use-change

landscape-services

display_name: landscape-services

modeling

display_name: modeling

statistical-model

display_name: statistical-model

switzerland

display_name: switzerland

urbanization

display_name: urbanization

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Land use projections and services for Switzerland


en: hysteresis snow-depth subgrid-topography snow-water-equivalent source-code snow-cover-modeling

en: Seasonal fractional snow-covered area algorithm

en: Release Version 1 from https://gitlabext.wsl.ch/snow-models/fractional-snow-covered-area

en:
This is the source code for computing the seasonal fractional snow-covered area. It is written in Fortran 90. The code reads snow depth (HS) and snow water equivalent (SWE) data from the provided example file HS_SWE.txt and writes the computed fractional snow-covered area (fSCA) to a file fSCA.txt.

The current version can be found in the WSL/SLF Gitlab repository:
https://gitlabext.wsl.ch/snow-models/fractional-snow-covered-area

hysteresis

display_name: hysteresis

snow-cover-modeling

display_name: snow-cover-modeling

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

source-code

display_name: source-code

subgrid-topography

display_name: subgrid-topography

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.


lwf-tea-bag-sites

maintainer: Marcus Schaub

title_for_slug: lwf-tea-bag-sites

en: modeling tea-bag litter decomposition roiboos carbon green-tea forest verification

en: Site Information

en: Descriptive information on sites

en:
Decomposition of plant litter is a key process for the transfer of carbon and nutrients in ecosystems. Carbon contained in the decaying biomass is released to the atmosphere as respired CO2, and may contribute to global warming. Litterbag studies have been used to improve our knowledge of the drivers of litter decomposition, but they lack comparability because litter quality is plant species-specific. The use of commercial tea bags as a standard substrate was suggested in order to harmonize studies, where green tea and rooibos represent more labile and more recalcitrant C compounds as surrogates of local litter.
The tea bag approach was implemented on eight sites of the Swiss long-term Forest Ecosystem Research (LWF) network (https://www.wsl.ch/LWF). This allowed us to take advantage from the existing infrastructure and data from a previous litterbag study with local litter. In Beatenberg and Schaenis, additional elevation transects were established (1200-1800 m and 540-1150 m, respectively) to examine particularly the effect of temperature on decomposition. In Pfynwald (https://www.wsl.ch/de/ueber-die-wsl/versuchsanlagen-und-labors/flaechen-im-wald/pfynwald.html) and Salgesch, infrastructure of running projects was used to examine the effect of drought and understory removal, respectively. In Novaggio, tea bags were incubated in summer and winter to study the effect of seasonality particularly precipitation. Tea bags are collected after 3, 12, 24, and 36 months; for the two time-shifted experiments additionally after 6 and 9 months.
The study has two primary objectives. Firstly, it contributes to TeaComposition initiative (http://teacomposition.org/) which aims at investigating long-term litter decomposition and its key drivers at present as well as under different future climate scenarios using a common protocol and standard litter (tea) across nine terrestrial biomes. Secondly, the data are used to further develop decomposition models such as Yasso (http://en.ilmatieteenlaitos.fi/yasso) which is used by several countries, including Switzerland to estimate the annual carbon fluxes in dead wood, litter, and soil for reporting in National Greenhouse Gas Inventories under the United Nations Framework Convention on Climate Change and the Kyoto Protocol.

carbon

display_name: carbon

decomposition

display_name: decomposition

forest

display_name: forest

green-tea

display_name: green-tea

litter

display_name: litter

roiboos

display_name: roiboos

tea-bag

display_name: tea-bag

verification

display_name: verification

en: LWF-Tea bag sites


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

en: Net increment*


en: integrative-forest-management timber-production multifunctional-forestry europe biodiversity

en: Link to DORA WSL

en: How to balance forestry and biodiversity conservation? A view across Europe.

en:
Forests cover about a third of the European land area and provide a multitude of ecosystem services. They are subject to constant change and increasing demands with respect to the ecosystem services they provide. As well as provision of timber, non-timber forest goods (e.g. berries and mushrooms), clean water, carbon sequestration, and protection against natural disasters, there is increasing emphasis being placed on conservation and promotion of biodiversity within forests. However, the state of biodiversity in European forests varies widely. Because of the different biogeographic, sociocultural, political, and climatic conditions at small scales, there is a wide variety of situations, approaches, and legacies across the continent.
This book strives to show the challenges that forest managers are faced with to fulfil the societal demands with regard to forests, and especially to integrate the promotion of biodiversity. In a first section (12 chapters), 44 authors from science, policy, and practice describe the driving factors of forest management; these factors include national laws and legacies, ownership structures, forest history, and socio-structural conditions. In a second section, 113 authors present 32 case examples from 19 European countries that demonstrate the different approaches to integrate the locally requested ecosystem services. These experiences are synthesised in a final section, and a toolbox of integrative measures is presented that is intended to support forest owners and managers to choose appropriate measures for targeted integrated management in their forests.
Copyright reserved WSL and EFI.

biodiversity

display_name: biodiversity

europe

display_name: europe

integrative-forest-management

display_name: integrative-forest-management

multifunctional-forestry

display_name: multifunctional-forestry

timber-production

display_name: timber-production

en: How to balance forestry and biodiversity conservation? - A view across Europe


en: water-demand water-scarcity water-supply reservoirs climate-change

en: Water supply

en: Simulated discharge series for a reference period 1981-2010 and till the end of the century. Data are provided for 39 climate model chains and a control run based on the observed meteorology. Data are provided at daily resolution (mm/d).

en: Reservoir volumes

en: Reservoir volume for the 307 medium-sized catchments for different storage categories: natural lakes, artificial reservoirs, reservoirs for artificial snow production, drinking water reservoirs, and usable volumes of natural lakes. Unit: Mio m³. One column per catchment (see shapefile).

en: Shapefile of 307 catchments in Switzerland

en: Shapefile of 307 catchments for which estimates of water supply, demand, and reservoir volumes are provided. The OBJECTID entry corresponds to the catchment ID which is used as a reference in the other datasets provided.

en: Water demand version 2 (corrected version)

en: Water demand (deprecated)

en:
Warning: please use the corrected version 2, since this file contains errors for certain categories such as Livestock, Industry or Houshold.

Water demand estimates for the 307 medium-sized catchments for different categories for current and future conditions. One text file is provided per demand category. One column corresponds to one catchment (IDs compare to shapefile), The data are provided at monthly resolution in mm/d and can be converted to volumes by using the catchment area. E.g.
Q[mm/d] * catchment area [m²] /1000 = Q[m³/d]
or to obtain discharge in m³/s
Q[mm/d] * catchment area [m²]/1000/24/3600 = Q [m³/s].
Future irrigation and hydropower water demand are provided per climate model chain.

en:
The dataset Hydro-CH2018 reservoirs provides estimates of current and future water supply, water demand, and storage volumes for 307 medium-sized catchments in Switzerland.
Water supply for current (1981-2010) and future (2070-2099) climate conditions was simulated using the hydrological model PREVAH. For modeling current water supply, observed meteorological time series were used as input, while simulated meteorological time series derived from 39 model chains of the CH2018 initiative were used as an input for simulating future climate conditions.

Water demand was estimated for six categories:

- 1) Drinking water (households and tourism),
- 2) industry (second and third sector),
- 3) artificial snow production,
- 4) agriculture (irrigation and livestock feeding),
- 5) ecology (residual flows), and
- 6) hydropower.

Future estimates consider changes in demand related to population growth and changes in the hydrological conditions.
Storage volumes are provided for natural lakes (storage capacities and usable volumes), artificial reservoirs, reservoirs for artificial snow production, and drinking reservoirs.

A detailed description of the simulation and estimation procedures can be found in

*  Brunner, M.I., Björnsen Gurung, A., Zappa, M., Zekollari, H., Farinotti, D., Stähli, M., 2019. Present and future water scarcity in Switzerland: Potential for alleviation through reservoirs and lakes. Sci. Total Environ. 666, 1033–1047. https://doi.org/10.1016/j.scitotenv.2019.02.169.

This dataset contains the following information:

1.	Shapefile with the 307 medium-sized Swiss catchments.
2.   Textfiles with the water supply simulations for the control run and the 39 climate model chains (one file per chain) at daily resolution for the 307 catchments.
3.	Textfiles with the current and future demand estimates per category at monthly resolution for the 307 catchments.
4.	Textfiles with the storage volumes per category and catchment.

climate-change

display_name: climate-change

reservoirs

display_name: reservoirs

water-demand

display_name: water-demand

water-scarcity

display_name: water-scarcity

water-supply

display_name: water-supply

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Hydro-CH2018 reservoirs


maintainer: Franziska Koch

en: snow-depth snow snow-cover gps liquid-water-content

en: Dataset with GPS-derived snow cover properties and validation data

en: This file includes the data as described in the Read-me file.

en: Read-me

en: The read-me file contains the description of the data provided.

en:
This data set includes GPS-derived snow water equivalent (SWE), snow depth (HS) and liquid water content (LWC) data for three entire snow-covered seasons (2015-2016, 2016-2017, 2017-2018) at the study plot Weissfluhjoch 2540 m a.s.l. (Davos, Switzerland). The procedure to derive these snow properties is described in Koch et al. (2019). The novel approach is based on a combination of GPS signal attenuation and time delay.

The dataset also includes corresponding validation data for SWE and HS measured at Weissfluhjoch, and some additional meteorological data used for interpretation of the snow cover evolution.

Please refer to the Read-me file for further details on the data.

These data are the basis of the following publication:
> Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M., Schweizer, J., and Mauser, W., 2019. Retrieval of snow water equivalent, liquid water content and snow height of dry and wet snow by combining GPS signal attenuation and time delay. Water Resources Research, 55(5), 4465-4487. https://doi.org/10.1029/2018WR024431

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

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: GPS-derived data of SWE, HS and LWC and corresponding validation data


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

en: SedCas Input/Output Data

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

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

The input file includes:

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

The output files include:

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

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

awe-gen

display_name: awe-gen

debris-flow

display_name: debris-flow

geomorphology

display_name: geomorphology

illgraben

display_name: illgraben

modelling

display_name: modelling

sedcas

display_name: sedcas

sediment-transport

display_name: sediment-transport

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


en: abstracts forest-ecosystems genomics conference evoltree adaptation

en: Book of Abstracts

en:
The first EVOLTREE Conference, taking place in hybrid format (on-site and online) at WSL Birmensdorf (Switzerland) from 14-17 September, 2021, focuses on the genomics of trees and interacting species from evolutionary, demographic, and ecological perspectives.

EVOLTREE is a European network of institutions engaged in studying the evolution and functioning of forest ecosystems, in particular trees as the foundation species in forest stands. A prime topic in the face of ongoing climate change is to elucidate how trees, together with their associated organisms such as mycorrhizal fungi, respond to rapid environmental changes.

The conference includes contributions that apply innovative approaches and consider the relevance of their research in the context of biodiversity conservation through natural dynamics or silvicultural interference.

abstracts

display_name: abstracts

adaptation

display_name: adaptation

conference

display_name: conference

evoltree

display_name: evoltree

forest-ecosystems

display_name: forest-ecosystems

genomics

display_name: genomics

en: Genomics and Adaptation in Forest Ecosystems. Book of Abstracts. EvolTree Conference 2021, 14 – 17 September 2021, Birmensdorf, Switzerland


stemflow-lwf

title_for_slug: stemflow-lwf

en: stemflow long-term-forest-ecosystem-research chemical-composition swiss-forest-lab icp-forests

en: Data Request

en:
Stemflow (portion of precipitation running down the branches and the trunk and depositing at the base of the tree) can represent a substantial fraction of the total water and nutrient input in stands of smoothbarked species with upright branches. Stemflow was measured with silicone gutters installed on the trunk of 5 trees at three LWF plots during 1-2 years. High capacity containers were used at Novaggio. An automated tipping bucket system, allowing continuous recording of volumes and sampling of representative proportional fraction, is currentlx used at the LWF sites Laegeren, Lausanne, Othmarsingen and Schänis.

 Purpose:

To quantify the contribution of stemflow to the water and nutrient budget and to the atmospheric deposition in selected forests stands.

 Manual Citation:

 * Thimonier, A., Brang, P., Wenger, K., 1997. Kapitel C4. Atmosphärische Deposition: Freiland- und Bestandesniederschläge, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-48.

 * Clarke N, Žlindra D, Ulrich E, Mosello R, Derome J, Derome K, König N, Lövblad G, Draaijers GPJ, Hansen K, Thimonier A, Waldner P, 2016: Part XIV: Sampling and Analysis of Deposition. 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,  32  p.  +  Annex  [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)

chemical-composition

display_name: chemical-composition

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

stemflow

display_name: stemflow

swiss-forest-lab

display_name: swiss-forest-lab

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Stemflow LWF


maintainer: Yohann Chauvier

en: climate phylogenetic-diversity ecological-indicator-values grain-size soil functional-diversity taxonomic-diversity

en: Data access

en:
This dataset comprises a large array of ecological data for the European Alps:

  (1) Current soil and climate predictors at various resolutions.
  (2) GBIF observations of the European Alps Flora (~4,000 species).
  (3) Species habitat suitability maps (1,109 species; based on species observations filtered at 40x40-km)
  at various resolutions used in the study to generate (4); except 'expert'...
  (4) Expert, Taxonomic, phylogenetic and functional diversity of the study region at various resolutions
  (from 100-m to 40-km --> 100-m aggregated & mean to km + non-aggregated/predicted) for CLIM,
  SOIL and CLIM-SOIL models.
  (5) Ecological and altitudinal preferences of the European Alps Flora.
  (6) Data outputs of the related published article.
  (7) All scripts used for analyses.
  (8) Additional files used for analyses.
  (9) Improved set of species habitat suitability maps (~2,600 species; based on species observations
  filtered at 1x1-km) and related taxonomic diversity at 100-m resolution (aggregated to km +
  non-aggregated/predicted) for CLIM, SOIL and CLIM-SOIL models ---> not incorporated in the study.

climate

display_name: climate

ecological-indicator-values

display_name: ecological-indicator-values

functional-diversity

display_name: functional-diversity

grain-size

display_name: grain-size

phylogenetic-diversity

display_name: phylogenetic-diversity

soil

display_name: soil

taxonomic-diversity

display_name: taxonomic-diversity

en: Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity


maintainer: Leo Gallus Bont

en: forest geoprocessing accessible-forest seilaplan openstreetmap qgis

en: Juypter Notebook for Data Preparation (online)

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

media_type: image/tiff

format: TIFF

mimetype: image/tiff

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

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

en: Preview

media_type: image/png

format: PNG

mimetype: image/png

en: Forest vector data (for sample forest range)

en: Juypter Notebook for Data Preparation

en: Digitised sample perimeter

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

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

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

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

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

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

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

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan

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


maintainer: Esther Frei

en: mountain-ecology literature-review treeline-ecosystems long-term-experiment stillberg

en: The Stillberg bibliography (Stillberg_bibliography_v1.zip) comprises a comprehensive list of 276 scientific publications, 91 of them published in peer-reviewed ISI journals (Stillberg_bibliography_data_v1.csv) along with the metadata file (Stillberg_bibliography_metadata_v1.csv). Currently the bibliography comprises literature about the main afforestation experiment, the FACE × warming experiment, the nutrient addition experiment, and the G-TREE experiment, as well as further publications related to the Stillberg research site that have been published until August 2023. The bibliography can be filtered for different categories, e.g., experiment, peer-review, source repository or database, and source title.

en:
 Background information

The Stillberg ecological treeline research site in the Swiss Alps was established in 1975, with the aim to develop ecologically, technically, and economically sustainable reforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Over almost fifty years, research at the Stillberg site combined long-term monitoring of the large-scale high-elevation afforestation with experimental manipulations simulating global change impacts. Besides providing a scientific basis and practical guidelines for high-elevation afforestation, this research has contributed to a comprehensive understanding of ecological processes in the treeline ecotone across different compartments and scales, from individual trees, non-tree vegetation and soils to whole ecosystems, in the context of global change resulting in more than 150 publications.

 Dataset generation

We compiled a comprehensive list of scientific publications covering research at the Stillberg research site by conducting searches in the literature databases Web of Science and Google Scholar, as well as in the Digital Object Repository of the Swiss Federal Institute for Forest, Snow and Landscape Research WSL (DORA). We compiled all publications about the afforestation experiment, the FACE × warming experiment, the nutrient addition experiment, the G-TREE experiment, as well as other studies related to the Stillberg research site.

 Data description

The Stillberg bibliography (Stillberg_bibliography_data_v1.csv) comprises a comprehensive list of 276 scientific publications, 91 of them published in peer-reviewed ISI journals. Currently the bibliography comprises literature about the main afforestation experiment, the FACE × warming experiment, the nutrient addition experiment, and the G-TREE experiment, as well as further publications related to the Stillberg research site that have been published until August 2023. The bibliography can be filtered for different categories, e.g., experiment, peer-review, source repository or database, and source title. The bibliography is described in a metadata file (Stillberg_bibliography_metadata_v1.csv). The bibliography along with the metadata file are provided in a ZIP-folder (Stillberg_bibliography_v1.zip).

literature-review

display_name: literature-review

long-term-experiment

display_name: long-term-experiment

mountain-ecology

display_name: mountain-ecology

stillberg

display_name: stillberg

treeline-ecosystems

display_name: treeline-ecosystems

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Bibliography of the long-term treeline research site Stillberg, Switzerland


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

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

en: Othmarsingen Meteorological Data LWF

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

LWF Plot Othmarsingen

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

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

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

en: LWF Othmarsingen long-term research site


en: treeline air-temperature soil latitude nitrogen soil-temperature climate biomass phosphorus permafrost elevational-gradient

en: Urals: latitudinal decline in treeline biomass and productivity

en:
1. Stand characteristics along 18 elevational gradients in treeline ecotones from the South to Polar Urals
2. Climate data of 5 major regions along the Urals extrapolated from meteo stations (1976.2006)
3. Air and Soil temperatures monitored in situ in the South and Polar Urals
4. Soil data (stone contents, sand fraction, carbon stocks, plant available nitrogen and phosphorus) South, North, Polar Urals
5. Element concentration needles of Larix sibirica (South and Polar Urals)

en:
1. Stand characteristics of treeline ecotone along 18 elevational gradients of the Ural mountains.
2. Extrapolated climate data at treeline using nearby meteo station (1976-2006).
3. Air and soil temperatures measured in situ at treeline in the South and Polar Urals. Soil temperature sensors were placed at 10 cm depth in open areas in between tree clusters but not under tree canopy.
4. Further plot specific information is available upon request.

air-temperature

display_name: air-temperature

biomass

display_name: biomass

climate

display_name: climate

elevational-gradient

display_name: elevational-gradient

latitude

display_name: latitude

nitrogen

display_name: nitrogen

permafrost

display_name: permafrost

phosphorus

display_name: phosphorus

soil

display_name: soil

soil-temperature

display_name: soil-temperature

treeline

display_name: treeline


en: antagonists univoltine spruce scolytinae parasitoids bivoltine predators ips-typographus

en: Bark beetle infestation spots

en: see ReadMe

en: In an expanding bark beetle (Ips typographus) infestation spot emergence traps were installed on the stems of newly infested spruce trees capturing all emerging insects during several consecutive years. Two locations were sampled on elavations with univoltine and bivoltine generations, respectively. Bark beetles and their insect predators and parasitoids were identified to species level by specialists.

antagonists

display_name: antagonists

bivoltine

display_name: bivoltine

ips-typographus

display_name: ips-typographus

parasitoids

display_name: parasitoids

predators

display_name: predators

scolytinae

display_name: scolytinae

spruce

display_name: spruce

univoltine

display_name: univoltine

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: Emergence dynamics of natural enemies of spruce bark beetles


en: avalanche-danger-level stability-tests avalanche avalanche-observations avalanche-danger

en: Human-triggered avalanches

en:
This CSV contains the density of human-triggered avalanches relative to the total potential release area for each day and region with the same forecast sub-level. The following variables are contained:

- *date* - date
- *dangerLevel* - the danger level forecast valid on the day of interest
- *levelBullSub* - the sub-level forecast valid on the day of interest
- *rhoSz2* - density of avalanche larger or equal than size 2 per *pra*
- *rhoSz3* - density of avalanche larger or equal than size 3 per *pra*
- *pra* - total surface area in km^2, which is considered as a potential release area within the warning regions forecast with the same sub-level on the day of interest
- *nRegions* - number of warning regions with the same sub-level

en: Observed danger signs

en:
This CSV contains observational data on danger signs. The following variables are contained:
- *date* - date of the observation
- *sectorId* - this is the sector id as used in the Swiss avalanche forecast
- *whumpfCracksFrequency* - observations with three classes NONE, RARELY, OFTEN
- *dangerLevel* - danger level as forecast in the avalanche forecast the evening before
- *levelBullSub* - sub level as forecast in the avalanche forecast the evening before

en: Rutschblock test results

en:
This CSV contains the Rutschblock results used in the study. The following variables are provided:

- *date* - the date the Rutschblock was observed
- *sectorId* - the warning region identifier
- *rbStab* - the stability class, as described in Figure 3
- *dangerLevel* - the danger level as forecast for the respective warning region on the day of the observation
- *levelBullSub* - the danger level as forecast for the respective warning region on the day of the observation

en:
This is the freely available part of the data used in the publication by Techel et al. (2022): _On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger_

- danger signs
- human triggered avalanches
- rutschblock test results (still to be added)
- extended column test results  (still to be added)

avalanche

display_name: avalanche

avalanche-danger

display_name: avalanche-danger

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-observations

display_name: avalanche-observations

stability-tests

display_name: stability-tests

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Observational data: avalanche observations, danger signs and stability test results, Switzerland (2016/2017 to 2020/2021 )


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

en: Site photo of the forest station (LEB)

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

en: Data plot of the forest station (LEB)

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

en: Forest station data (LEB)

en: Data file.

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 one meteorological station in Lens in Switzerland which is located within a natural coniferous forest with Scots pine (_Pinus sylvestris_; 150-170 yrs)) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lens 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: Lens, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


maintainer: Maria Elvira Murazzi

en: uptake polystyrene forest-trees nanoplastic 13c-labelling

en: Nanoplastic in forests experiment

en:
Data embargoed until August 2022, and it will be provided upon request

-  excel file containing: two working sheets (leaf supply and root supply experiment) with a table with the values 13C-nanopolystyrene in the different tissues of the two forest tree species; graph showing the differences between controls and treatments of 13C-nanopolystyrene enrichment for each tissue of each forest tree species; ANOVA table showing significant values after having exposed the tree species to 13C-nanopolystyrene

en:
The fate of plastic in the environment is of global concern, because its production recently has
increased strongly and it accumulates in terrestrial and aquatic ecosystems. Although some knowledge on
its role in aquatic and terrestrial ecosystems was gained in the recent decade, hitherto very little is known
about the impact of micro and nanoplastics in forest ecosystems.

The aim of this pioneering project was to explore if nanoplastics are taken up by forest trees species
through leaves or roots. In greenhouse experiments, we exposed leaves or roots of seedling of two forest
trees species to solutions with highly 13C-labelled polystyrene nanoparticles (13C-nPS, 99 atom%) and
examined if they were incorporated in different above- and belowground tissues.

Treated part of the trees for both species showed significant 13C-enrichment, indicating that trees take up nanoparticles. However, the overall 13C signal strength in tissues that were not exposed to the 13C label remained low (Δδ13C<1‰) and was confined to a few seedlings, leaving it ambiguous whether nanoplastic transport occurs or not.

We acknowledge that the new method developed might be not sensitive enough to unequivocally detect
mechanisms of nanoplastic uptake and transport at environmentally realistic concentrations.

forest-trees

display_name: forest-trees

nanoplastic

display_name: nanoplastic

polystyrene

display_name: polystyrene

uptake

display_name: uptake

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: Nanoplastics in forests: Exploring the effects of nanoplastics on forest soils and tree physiology (NanoPlast)


en: airborne-laser-scanning snow-distribution canopy-height forest-structure snow-depth

en: Canopy height maps

en: Canopy height maps of each of the three field areas Davos, Engadine and Grand Mesa

en: Readme

mimetype: text/plain

en: Terrain elevation maps

en: Digital elevation model computed from snow-off flights for each of the field areas Davos, Engadine and Grand Mesa

en: Snow depth maps

en: Snow depth maps from two snow-on flights at each of the three field areas Davos (March 20th and 31st 2017), Engadien (March 20th and 31st, 2017) and Grand Mesa (February 8th and 16th, 2017)

en:
This dataset includes snow depth, canopy height and terrain elevation maps of forest stands in the Grisons (CH) and at Grand Mesa (CO,USA) derived from airborne lidar. Data were acquired i) within a pilot mission of NASA's Airborne Snow Observatory in the Swiss Alps in March 2017 and ii) during NASA’s SnowEx campaign at Grand Mesa in February 2017. Snow depth maps are available for two dates separated by approx.1 week, and include an area of ca. 0.5km2 for each of the three sites Davos, Engadine and Grand Mesa.
All data were presented and analyzed in the publication 'Revisiting Snow Cover Variability and Canopy Structure within Forest Stands: Insights from Airborne Lidar Data' (Mazzotti et al., 2019, WRR, doi: 10.1029/2019WR024898). This publication must be cited when using this dataset.

Paper Citation:
 > _Giulia Mazzotti; William Ryan Currier; Jeffrey S. Deems; Justin M. Pflug; Jessica D. Lundquist; Tobias Jonas (2019). Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data. Water Resources Research, 55, 6198– 6216, [doi: 10.1029/2019WR024898](https://doi.org/10.1029/2019WR024898)._

airborne-laser-scanning

display_name: airborne-laser-scanning

canopy-height

display_name: canopy-height

forest-structure

display_name: forest-structure

snow-depth

display_name: snow-depth

snow-distribution

display_name: snow-distribution

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: ALS-based snow depth and canopy height maps from flights in 2017 (Grisons, CH and Grand Mesa, CO)


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

en: Data Request

en:
Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA).

 Purpose:

Monitoring of the soil water matrix potential

 Paper Citation:

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

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

soil-moisture

display_name: soil-moisture

soil-water-content

display_name: soil-water-content

en: 10-HS Pfynwald


en: climate-change-impact sdm treemig treeline mountain-forest

en: Data for bioclimatic envelope model

en: This spreadsheet provides the data used to calibrate and evaluate the bioclimatic envelope models (SDMs) for our 16 target tree species. For copyright reasons the geographic coordinates of the points have been removed and only relevant information in the environmental space is provided.

en: Bioclimatic predictors used for future projections

en: The seven bioclimatic predictors (mean annual temperature, minimum temperature of the coldest month, temperature of the driest quarter, annual sum of precipitation, sum of precipitation of the driest quarter, slope and topographic position (concave-convex) used for correlative SDMs in Scherrer et al. 2020. Data for climate is available for both current (1981-2010) and future (2020-49, 2045-74,2070-99).

en: Bioclimatic data for TreeMig

en: Bioclimatic input data used for the TreeMig models in Scherrer et al. 2020. Data spans the whole simulation period of 1500 until 2500 and covers three different climate change scenarios.

en:
Species range limits are expected to be dramatically altered under future climate change and many species are predicted to shift their distribution upslope to track their suitable conditions (i.e. based on their niche). However, there might be large discrepancies between the speed of the upward shift of the climatic niche and the actual migration velocity of the species, especially in long-lived organisms such as trees. Here, we compared the simulations of the upslope displacement of the bioclimatic envelope of 16 tree species inhabiting temperate mountain forests under ongoing and future climate change obtained by correlative species distribution models (SDMs) to those from a dynamic forest model accounting for dispersal, competition and demography. We then partitioned the discrepancy in upslope migration velocity between the SDMs and the dynamic forest model into different components by manipulating dispersal limitation, interspecific competition and demography.

This dataset contains the calibration and evaluation data used to create the bioclimatic envelope models, the predictors for the future scenarios (raster layers) and the bioclimatic input data used in the dynamic forest models used in the following publication (Scherrer et al. 2020).

Paper Citation:
Scherrer, D., Vitasse, Y., Guisan , A., Wohlgemuth, T., & Lischke, H. (2020). Competition and demography rather than dispersal limitation slow down upward shifts of trees’ upper elevation limits in the Alps. Journal of Ecology, in press.

climate-change-impact

display_name: climate-change-impact

mountain-forest

display_name: mountain-forest

sdm

display_name: sdm

treeline

display_name: treeline

treemig

display_name: treemig

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: Factors slowing down upward shifts of trees’ upper elevation limits


en: drought microbial-biomass soil allocation pfynwald rhizosphere forest

en:
Data of 13C pulselabelling experiment
1. Climate and soil respiration in the year following pulse labelling
2. Temporal patterns in 13C signal (D13C) and 13Cexcess
3. Spatial distribution of 13C in the soil

en: Data from pulse-labelling experiment with 100-year old trees of a naturally dry pine forest exposed to a 15-year-long irrigation experiment. Canopies of 10 trees were labelled for 3 hours with 13CO2 and the fate of this label was traced for one year in stem and soil respiration and in microbial biomass around these trees. Data include (1) microclimatic data and soil respiration rates of the year following pulse labelling. (2) Temporal patterns of the 13C signal and 13C excess in soil respired CO2 and microbial biomass. (3) Spatial distribution of 13C signal in the soil.

allocation

display_name: allocation

drought

display_name: drought

forest

display_name: forest

microbial-biomass

display_name: microbial-biomass

pfynwald

display_name: pfynwald

rhizosphere

display_name: rhizosphere

soil

display_name: soil

educ

en: Education, culture and sport

en: Drought alters C footprint of trees in soil: tracking the spatio-temporal fate of 13C labelled assimilates in the soil