en: ensemble-modelling shrub-forest modelling milieux-naturels random-forest typoch lebensraumkarte grassland remote-sensing wetlands habitat forest lebensraum

en: The Habitat Map of Switzerland VD v1 20220316, Canton Vaud, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Vaud, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland NW v1 20220316, Canton Nidwalden, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Nidwalden, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland UR v1 20220316, Canton Uri, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Uri, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland OW v1 20220316, Canton Obwalden, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to the borders of canton Obwalden, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert)

en: The Habitat Map of Switzerland GL v1 20220316, Canton Glarus, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Glarus, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland TI v1 20220316, Canton Ticino, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Ticino, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland SH v1 20220316, Canton Schaffhausen, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Schaffhausen, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland AG v1 20220316, Canton Aargau, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Aargau, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland ZG v1 20220316, Canton Zug, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Zug, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland SO v1 20220316, Canton Solothurn, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Solothurn, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland AI v1 20220316, Canton  Appenzell Innerrhoden, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Appenzell Innerrhoden, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland NE v1 20220316, Canton Neuchatel, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Neuchatel, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland GE v1 20220316, Canton Geneva, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Geneva, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland TG v1 20220316, Canton Thurgau, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Thurgau, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland CH v1 20220316, file geodatabase

en:
The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert).
V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland LU v1 20220316, Canton Lucerne, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Lucerne, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland GR v1 20220316, Canton Grisons, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Grisons, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland BE v1 20220316, Canton Bern, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Bern, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland BL v1 20220316, Canton Basel-Landschaft, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Basel-Landschaft, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland ZH v1 20220316, Canton Zurich, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Zurich, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland FR v1 20220316, Canton Fribourg, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Fribourg, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: Data Description

en: Metadata document describing the methodology used to develop the dataset

en: The Habitat Map of Switzerland BS v1 20220316, Canton Basel-Stadt, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Basel-Stadt, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland VS v1 20220316, Canton Valais, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Valais, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland SG v1 20220316, Canton St Gallen, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton St Gallen, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland JU v1 20220316, Canton Jura, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Jura, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland SZ v1 20220316, Canton Schwyz, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Schwyz, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland AR v1 20220316, Canton Appenzell Ausserrhoden , file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Appenzell Ausserrhoden, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en:
Lebensraumkarte der Schweiz/La carte des milieux naturels de Suisse
The FOEN funded project ‘Developing a Habitat Map of Switzerland’ conducted at the WSL, has produced a map of Swiss habitats according to the TypoCH classification (Delarze et al. 2015) wall-to-wall across the whole of Switzerland, to at least the classification’s 2nd level of detail (where possible to the 3rd level of detail). The implementation of the Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only.
Habitats are mapped through a variety of approaches that can be grouped as either:
1: Derived from the existing Swiss-wide high quality landcover mapping from Swisstopo’s Topographical Landscape Model (TLM), 2: Modelled within the project using Random Forest or Ensemble Modelling techniques to model the spatial distribution of individual habitat types, 3: Combining existing species distribution models to determine habitat types, or 4: Classification with relatively simple rule-sets based on auxiliary spatial datasets, i.e. vegetation height models, the digital terrain model, the normalised difference vegetation index (NDVI) derived from aerial imagery and/or time-series of growing season Sentinel-2 satellite imagery. Further detail on the methodology can be found within the README document.

ensemble-modelling

display_name: ensemble-modelling

forest

display_name: forest

grassland

display_name: grassland

habitat

display_name: habitat

lebensraum

display_name: lebensraum

lebensraumkarte

display_name: lebensraumkarte

milieux-naturels

display_name: milieux-naturels

modelling

display_name: modelling

random-forest

display_name: random-forest

remote-sensing

display_name: remote-sensing

shrub-forest

display_name: shrub-forest

typoch

display_name: typoch

wetlands

display_name: wetlands

educ

en: Education, culture and sport


en: atree stakeholder-analysis forest-sink carbon-sequestration wood-sink social-network-analysis

state: active

type: dataset

en: Questionnaire for ATREE SNA survey Lucerne

media_type: application/pdf

format: PDF

en: SNA survey data stakeholder analysis Lucerne of the ATREE project

en:
The survey was conducted online in a non-anonymized manner, however, the data provided here is anonymized, hence only the name and function of the stakeholder organization are contained but not the name of the person that answered the survey. Only one answer per organization has been collected. The data contains only the answers to the standardized questions (choice of predefined answer categories) but not those answers to open questions (comments). These are not relevant for interpretation of the rest of the data and may contain indications about the author.
The data is available as an SPSS Data file. It can be imported into R with:

library(foreign)
 ?read.spss explains options that might be adjusted, particularly regarding the value labels of variables
data <- read.spss("ATREE_SNA_Lucerne.sav",to.data.frame=T)
 variable labels contain the original question as posed in the questionaire
#?attributes
attributes(data)

format: SERVICE

en: In January 2020 a social network analysis survey was conducted among forest policy stakeholders (at the organizational level) from the Canton of Lucerne as well as the national level. The aim was to elicit positions relative to a set of policy options currently discussed with respect to carbon mitigation and sequestration services of the forest, i.e. forest management and to establish information and collaboration network relations in order to identify actor coalitions as inspired by the "actor coalition framework" approach to policy analysis. Of the 66 questionnaires sent out, 51 were answered (77%). Only one additional organization was indicated as being missing from the provided list of stakeholder organizations.

atree

display_name: atree

carbon-sequestration

display_name: carbon-sequestration

forest-sink

display_name: forest-sink

social-network-analysis

display_name: social-network-analysis

stakeholder-analysis

display_name: stakeholder-analysis

wood-sink

display_name: wood-sink

fr: Éducation, culture et sport

de: Bildung, Kultur und 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: ATREE Social Network Analysis survey on policy options regarding CO2 mitigation and sequestration in wood and forest


en: multi-stage balanced-sampling vegetation unequal-probability habitat

en: data

en: R script

en:
License: GPL-v2

The R script presents an advanced sampling approach for monitoring biodiversity on agricultural land by combining multiple objectives and integrating environmental and geographic space.

The example demonstrates the first-stage selection of squares (km2) in the ALL-EMA sampling design using modern sampling techniques such as unequal probability sampling with fixed sample size, balanced sampling, stratified balancing and geographic spreading. Sampling is done with unequal probabilities and weights defined by power allocation to give equal weight to extrapolations to the total agricultural area of Switzerland and two stratifications of predefined interest (regions and agricultural production zones). Calibration is used to limit the distribution of the sampling weights. The sample sizes are almost fixed within the strata and evenly distributed across the years of a temporal rotation plan, which is favourable for the organisation of the field survey. Sampling also ensures an optimal (annual) distribution across geographic space, including altitude. Despite the complexity of the sampling, estimation based on probability theory is straightforward.

Ecker, K.T., Meier, E.S. & Tillé, Y. 2023. Integrating spatial and ecological information into comprehensive biodiversity monitoring on agricultural land. Environmental Monitoring and Assessment 195.

balanced-sampling

display_name: balanced-sampling

multi-stage

display_name: multi-stage

unequal-probability

display_name: unequal-probability

vegetation

display_name: vegetation

en: R script and input data for "ALL-EMA sampling design"


hymenoptera

title_for_slug: hymenoptera

en: agricultural hautfluger arthropoda urban forests hymenoptera

media_type: text/plain

en: Data extract in TAB-delimted format. First row is header, first column is counter.

format: TXT

en: Data query used on Fauna Database to retrieve the submitted data.

en:
Hymenopteran data from all historic up to the recent projects (29.10.2019) of WSL, collected with various standardized methods in landscapes of different types.

Data are provided on request to contact person against bilateral agreement.

agricultural

display_name: agricultural

arthropoda

display_name: arthropoda

forests

display_name: forests

hautfluger

display_name: hautfluger

hymenoptera

display_name: hymenoptera

urban

display_name: urban

en: Hymenoptera


sensitivity-of-modeled-snow-instability

maintainer: Bettina Richter

title_for_slug: sensitivity-of-modeled-snow-instability

en: snow-instability global-sensitivity-analysis snowpack snow-cover-modeling numerical-avalanche-forecasting

en: Reference run

media_type: application/zip

en: Reference run for the sensitivity analysis at Weissfluhjoch field site for winter season 2016-2017. Meteorological data from the automatic weather stations are used. No biases were introduced to the input data.

format: ZIP

mimetype: application/zip

en: Case ALL

en: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the whole simulation period. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Case WL

en: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the period of weak layer formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Manually observed snow profiles

en: Manually observed snow profiles at Weissfluhjoch field site for winter season 2016-2017, including layer depth, hand hardness, grain size and grain shape for each layer.

en: Case SL

en: Extracted output data for case SL. Here biases were introduced to meteorological input data during the period of slab formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: We investigated the sensitivity of modeled snow instability to meteorological input data using SNOWPACK. We therefore used input data from the automatic weather station at the Weissfluhjoch field site for the year 2016-2017. We investigated three scenarios and performed 14'000 simulations for each scenario. The dataset contains extracted output data from modeled SNOWPACK simulations, including setup files to reproduce the simulations. For further information read the README file.

global-sensitivity-analysis

display_name: global-sensitivity-analysis

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

educ

en: Education, culture and sport

en: Sensitivity of modeled snow instability


latent-reserves-within-the-swiss-nfi

title_for_slug: latent-reserves-within-the-swiss-nfi

en: switzerland national-forest-inventory tree-mortality forest-reserves near-natural-forest

en: Data for mortality analyses

media_type: text/csv

en:
This table contains the tree- and plot-level data necessary to conduct the tree mortality analyses:

* "DEAD" takes the value '0' when the tree was alive at NFI3 and '1' when it was observed dead
* "SPECIES" refers to which species group the tree belongs to
* "relBAL" is the relative basal area of larger trees
* "DBH" is the diameter at breast height
* "relBAI" is the relative basal area increment of the tree
* "BA" is the basal area in squared meters per hectare (stand-level)
* "STAND_DENSITY_LOG" is the logarithm of the stand density in number of stems per hectare
* "BEERS_ASPECT_CORR" is the Beers aspect corrected for slope of the plot
* "SLOPE_SQRT" is the squared root of the slope of the plot in percentage
* "ELEVATION" is the elevation of the plot in meters above sea level
* "AWC" is the available soil water-holding capacity in millimeters
* "VEGYEAR" is the number of vegetation years between the measurements of the plots at NFI2 and NFI3.

format: CSV

en:
This table contains the tree-level information required to reproduce the charaterisation of 'latent reserves' and comparison to managed forests:

* "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
* "SOURCE" differentiate inventoried trees ("W", DBH greater or equal to 12 cm) and young-growth trees measured on subplots ("JW").
* "SPECIES" refers to the species code of the Swiss NFI.
* "SPECIES_GROUP" refers to which species group a tree belongs to (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves)
* "ALIVE_NFI2" takes the value '1' when the tree was alive at the previous inventory (NFI2). Trees with no value (NA) were not measured at NFI2. This variable should be used to calculate mortality rates, as only trees that were present at NFI2 can be used to calculate the mortality rate between NFI2 and NFI3.
* "DEAD" takes the value '1& when a tree was observed dead at NFI3, and the value '0' when it was observed alive.
* "INCLUSION_PROB" represents the inclusion probability of a tree i.e. the number of trees that a given tree represents per hectare. This number depends on the DBH of the tree and on whether the plot area was reduced by a boundary (e.g. a forest edge). This variable should be used to weight the trees in some calculations.
* "DBH" is the diameter at breast height of a tree in centimeters.
* "LIGHT", "TEMPERATURE", "MOISTURE" refer to the value of the Ellenberg indicators for light, temperature and moisture that are species-specific. 'NA' is attributed when the value of an indicator is not available for a given species.

en: Data figure 7

en:
This file contains the data required to reproduce Figure 7:

- "SPECIES_GROUP" refers to the species groups used in the mortality analyses (spruce, fir, pine, larch, stone pine, beech, maple and ash, oak, chestnut, other broadleaves)
- "VARIABLE" refers to the explanatory variables used in the model and presented in the figure: DBH (diameter at breast height) and relBAI (relative basal area increment).
- "VALUE" refers to the the values of the variables for which predictions were made.
- "PRED_MORTALITY_PROB" refers to the predicted mortality probability for a given species group and value of either DBH or relBAI.

en: Data figure 4

en:
This file contains the data required to reproduce Figure 4:

- "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
- "ELEV_VEG_ZONE" refers to the elevation vegetation zone (colline and submontane, montane or subalpine)
- "SPECIES_GROUP" refers to all the trees of a given species group (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves)
- "DBH_CL" refers to the DBH (diameter at breast height) class to which a tree belongs to, in centimetres.
- "DENSITY" refers to the proportion of trees (corrected for the inclusion probability of each tree) within each combination of forest type, elevation vegetation zone, species group and DBH class.

The inclusion probability of each tree was accounted for in the density calculation.

en:
This table contains the plot-level information required to reproduce the charaterisation of 'latent reserves' and comparison to managed forests:

* "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
* "ELEV_VEG_ZONE" refers to the elevation vegetation zone (colline and submontane, montane or subalpine)
* "DOMINANT_SPECIES" refers to the dominant species in a given plot (spruce, fir, other conifers, beech, other broadleaves)
* "TIME_SINCE_INTERVENTION" refers to the numbers of years since the last human intervention on the plot. This variable should be used to set the threshold of time since last intervention one wants to use to define 'latent reserves'.
* "VEGYEAR" refers to the number of vegetation years between the measurements of a plot for NFI2 and NFI3.
* "BA" provides the basal area in squared meters per hectare of a plot calculated from trees having a DBH of at least 12 cm.
* "BA_WITH_YG" provides the basal area in squared meters per hectare of a plot calculated from trees having a DBH of at least 12 cm as well as young growth trees.
* "STAND_DENSITY" provides the stand density in number of stems per hectare of a plot calculated from trees having a DBH of at least 12 cm.
* "STAND_DENSITY_WITH_YG" provides the stand density in number of stems per hectare of a plot calculated from trees having a DBH of at least 12 cm as well as young growth trees.
* "SITE_INDEX" refers to the site index of a plot, i.e. the total biomass productivity calculated according to Keller (1976).
* "DEADWOOD" refers to the amount of deadwood of a plot in tons per hectare.

******

Reference
Keller, W. (1976). Einfacher ertragskundlicher Bonitätsschlüssel für Waldbestände in der Schweiz. Zurich, Switzerland: ETH Zurich.

en: Data figure 2

en:
This file contains the data required to reproduce Figure 2:

- "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
- "ELEV_VEG_ZONE" refers to the elevation vegetation zone (colline and submontane, montane or subalpine)
- "SPECIES_GROUP" refers to all the trees of a given species group (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves)
- "DENSITY" refers to the proportion of trees (corrected for the inclusion probability of each tree) within each combination of forest type, elevation vegetation zone and species group.

Young-growth trees are not included in the density calculation.

en: Data figure 8

en:
This file contains the data required to reproduce Figure 8:

* "SPECIES_GROUP" refers to the species groups used in the mortality analyses (spruce, fir, pine, larch, stone pine, beech, maple and ash, oak, chestnut, other broadleaves)
* "BASAL_AREA" refers to the values of the basal area in squared meters per hectare for which predictions were made
* "STAND_DENSITY" refers to the values of stand density in number of stems per hectare for which predictions were made
* "NORM_PRED_MORTALITY_PROB" refers to the normalised predicted mortality probability corresponding to a given species group and values of basal area and stand density.

en: Data figure 5

en:
This file contains the data required to reproduce Figure 5:

- "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
- "DOMINANT_SPECIES" refers to the dominant species in a given plot (spruce, fir, other conifers, beech, other broadleaves)
- "VARIABLE" refers to the attributes described in the figure: basal area in squared meters per hectare, median DBH in cm, stand density in number of stems per hectare, deadwood in tons per hectare, site index in tons per hectare per year. Basal area, median DBH and stand density are given both with and without including young-growth trees.
- "mean" refers to the mean value of each attribute for each combination of forest type and dominant species (corrected for the inclusion probability of each tree).
- "se" corresponds to the standard error.

For these calculations, site index was constrained to the range observed in ‘latent reserves’ with at least 40 years without intervention. Additionally, only NFI sample plots from the montane and subalpine zones were used as colline and submontane NFI plots from ‘latent reserves’ and managed forests are located in different geographical regions.

en: Data figure 3

en:
This file contains the data required to reproduce Figure 3:

- "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
- "ELEV_VEG_ZONE" refers to the elevation vegetation zone (colline and submontane, montane or subalpine)
- "VARIABLE" refers to the indicators described in the figure: Ellenberg indicators for light, temperature and moisture, as well as species richness.
- "mean" refers to the mean value of each indicator for each combination of forest type and elevation vegetation zone (corrected for the inclusion probability of each tree).
- "se" corresponds to the standard error.

For these calculations, site index was constrained to the range observed in 'latent reserves' with at least 40 years without intervention.

en: Data figure 6

en:
This file contains the data required to reproduce Figure 6:

- "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
- "SPECIES_GROUP" refers to all the trees of a given species group (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves)
- "MORTALITY_RATE" refers to the mean annual mortality rate of each species group calculated according to Sheil and May (1996)
- "LOWER_CI" and "UPPER_CI" refer to the lower and upper confidence intervals on the mortality rates, calculated according to Sheil and May (1996).

Reference
Sheil, D., & May, R. M. (1996). Mortality and recruitment rate evaluations in heterogeneous tropical forests. Journal of Ecology, 84(1), 91–100. doi: 10.2307/2261703

en:
The files refer to the data used in Portier et al. "‘Latent reserves’: a hidden treasure in National Forest Inventories" (2020) *Journal of Ecology*.

**'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database:

* Shrub forests were excluded.
* Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested.
* To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994).
* Plots had to originate from natural regeneration.
* Intensive livestock grazing must never have occurred on the plots.

The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php).

****

The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper.

The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.

forest-reserves

display_name: forest-reserves

national-forest-inventory

display_name: national-forest-inventory

near-natural-forest

display_name: near-natural-forest

switzerland

display_name: switzerland

tree-mortality

display_name: tree-mortality

envidat

en: EnviDat: the Environmental Data Portal

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

en: 'Latent reserves' within the Swiss NFI


maintainer: Paraskevi Georgakaki

en: ice-crystal-number-concentration ice-water-content wrf swiss-alps jungfraujoch secondary-ice-production blowing-snow liquid-water-content

en: Observational data

en:
The .zip file contains 6 .csv files including all microphysical measurements taken at the Jungfraujoch station during the two periods of interest:

* 25-28 January 2014 : northwesterly-winds (NW)
* 29 January - 1 February 2014 : southeasterly-winds (SE)

The total Ice Crystal Number Concentration (ICNC; reported in L⁻¹) is measured by the two-dimensional stereo hydrometeor spectrometer (2D-S; Lawson et al., 2006) over the size range 10-1280 μm. The total ice water content (IWC; reported in gm⁻³) is also derived by the 2D-S data using the Brown and Francis (1995) mass-diameter relationship. Finally, the quantification of the liquid water content (LWC; reported in gm⁻³) is based on the liquid droplet size distribution data derived from a DMT cloud droplet probe (CDP; Lance et al., 2010) over the size range 2-50 μm. The frequency of the measurements in the .csv files is 30 min, to match the output frequency of the model. Datetime is also reported in the files (DD/MM/YYYY h:mm) in UTC.

Acknowledgment: Collection and pre-processing of the microphysical measurements has been performed by Gary Lloyd (gary.lloyd@manchester.ac.uk). More details about the instrumentation during the CLACE2014 campaign can be found in Lloyd et al. (2015).

en: WRF outputs

en:
The .zip file contains all the NetCDF outputs produced by the mesoscale Weather Research and Forecasting (WRF) model (version: 4.0.1). Two case studies were simulated in this study, between 25-28 January 2014 (00:00 UTC) and 29 January - 1 February 2014 (00:00 UTC), respectively. The former is denoted as "NW" and the latter as "SE", owing to the prevailing wind conditions at the Jungfraujoch (JFJ) station.

The outputs come from the innermost domain of the simulations with a spatial resolution of 1 km and are extracted from the nearest to JFJ model grid point (46.55°N 7.98°E). The dimensions of each model variable is [145,100], which corresponds to time and height, respectively. The frequency of the outputs is 30 min. Note that the first 24 hours of each simulation (i.e., the first 48 outputs) were considered spin up time.

The following sets of sensitivity simulations were presented in this study:

* CNTRL_NW.nc, CNTRL_SE.nc : Sensitivity simulations performed with the default version of WRF
* DS_PHILL_NW.nc, DS_PHILL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Phillips et al. (2018)
* DS_SULL_NW.nc, DS_SULL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Sullivan et al. (2018)
* PHILL_NW.nc, PHILL_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Phillips et al. (2017)
* TAKAH_NW.nc, TAKAH_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Sullivan et al. (2018) based on Takahashi et al. (1995)
* TAKAHrim_NW.nc, TAKAHrim_SE.nc : Same set-up as before but applied only to collisions between rimed ice particles
* BIPS_Xk0_DY_NW.nc, BIPS_Xk0_DY_SE.nc : Sensitivity simulations including a source of Xk0 (either 10.0 or 100.0 L⁻¹) ice crystals with sizes of DY (either 10 or 100 μm) in the first atmospheric level of the model
* BIPS100_PHILL_NW.nc, BIPS100_PHILL_SE.nc : Sensitivity simulations where the combined effect of blowing snow and ice-ice collisional break-up is examined

en:
This repository contains all WRF model outputs and observational data sets used for the paper:
Georgakaki, P., Sotiropoulou, G., Vignon, É., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021.

blowing-snow

display_name: blowing-snow

ice-crystal-number-concentration

display_name: ice-crystal-number-concentration

ice-water-content

display_name: ice-water-content

jungfraujoch

display_name: jungfraujoch

liquid-water-content

display_name: liquid-water-content

secondary-ice-production

display_name: secondary-ice-production

swiss-alps

display_name: swiss-alps

wrf

display_name: wrf

educ

en: Education, culture and sport

en: Data for the publication "Secondary ice production processes in wintertime alpine mixed-phase clouds"


en: permafrost temperature permos ground borehole

en: DATASET & PLOTS

en: PERMOS DATA POLICY

en: Map of PERMOS borehole network

en: Processed ground temperature measurements at the Fluelapass permafrost borehole A (FLU_0102) in canton Graubunden, Switzerland. The borehole is located at 2394 m asl on a moderate (26°) North-east slope (45°). The surface material is talus and borehole depth is 23 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.

borehole

display_name: borehole

ground

display_name: ground

permafrost

display_name: permafrost

permos

display_name: permos

temperature

display_name: temperature

en: Processed permafrost borehole data (2394 m asl), Fluelapass A, Switzerland


maintainer: EnviDat Support

en: unmanned-aerial-vehicle-laser-scanning 3d-point-cloud structure-from-motion forest-inventory terrestrial-laser-scanning terrestrial-photogrammetry mobile-laser-scanning lidar

en:
Close Range Remote Sensing Benchmark for different LiDAR and photogrammetric Sensors in a mixed temperate forest.
Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices.

forest-inventory

display_name: forest-inventory

lidar

display_name: lidar

mobile-laser-scanning

display_name: mobile-laser-scanning

structure-from-motion

display_name: structure-from-motion

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

terrestrial-photogrammetry

display_name: terrestrial-photogrammetry

unmanned-aerial-vehicle-laser-scanning

display_name: unmanned-aerial-vehicle-laser-scanning

envidat

en: EnviDat: the Environmental Data Portal

en: Ramerenwald Close Range Remote Sensing Benchmark


maintainer: Christian G. Sommer

en: wind-tunnel snow wind-crust wind-packing kinect

en: README

en: Describes the dataset in detail

mimetype: text/plain

en: Processing scripts

en: Contains the Matlab- and R-scripts used to process the Kinect data

en: Kinect data

en: Links to the dataset

en:
This data sets contains the Microsoft Kinect data from 15 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winter 2016/17. The Kinect measures distributed snow depth.

The Kinect data is organized per experiment. Each experiment subfolder contains the processed Kinect depth images and some additional files.

Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.

This dataset is the basis of the following publication:

Sommer C.G., Lehning M. & Fierz C. (2018). Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow. Front. Earth Sci. 6:4. doi: 10.3389/feart.2018.00004

kinect

display_name: kinect

snow

display_name: snow

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

wind-tunnel

display_name: wind-tunnel

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: Wind crust formation: Microsoft Kinect data


aerosol-data-davos-wolfgang

title_for_slug: aerosol-data-davos-wolfgang

en: raclets particles ice-nucleation inp aerosol drincz

en: SMPS Dataset Davos Wolfgang

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

mimetype: application/x-netcdf

en: OPC-N3 Dataset Davos Wolfgang

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

mimetype: text/csv

en: DRINCZ Dataset Davos Wolfgang

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

en: APS Dataset Davos Wolfgang

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

en: OPC GT-526S Dataset Davos Wolfgang

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

en:
Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.

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

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

Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³).

Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

aerosol

display_name: aerosol

drincz

display_name: drincz

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

particles

display_name: particles

raclets

display_name: raclets

educ

en: Education, culture and sport

en: Aerosol Data Davos Wolfgang


maintainer: Marco Moretti

en: climate beta-diversity urbanization switzerland biodiversity community-indices protected-areas resource-availability elevation-gradient anthophila wild-bee predictive-modelling functional-diversity hymenoptera

en: Taxonomic and functional metrics in the community plots

en:
Response data used for the modeling.
Lat: latitude community plot
Lon: longitude community plot
Richness: species richness
Shannon: Shannon diversity index
FDis: functional dispersion
TOP: Trait Onion Peeling (functional richness)
TED: Trait Even Distribution (functional evenness)
LCBD_taxo: local community contributions to taxonomic beta diversity
LCBD_fun: local community contributions to functional beta diversity
ITD: community weighted means inter-tegula distance
feeding_specialization: community weighted means feeding specialization
tong_length: community weighted means tongue length
phenostart: community weighted means start of the phenology
phenoduration: community weighted means duration of the phenoloy
cleptoparasite: community weighted means cleptoparasites
belowgound: community weighted means proportion belowground
solitary: community weighted means proportion solitary

en: Variable importance, model evaluation, partial dependence plots

en: The compress file containes a set of txt files with the variable importance, the model evaluation metrics and the partial dependence plots of each of the responses used (alpha and beta taxonomic and functional diversity metrics; community weighted means of 8 functional traits)

en: Spatial environmental predictors and responses

en:
The .zip file contains the rasters of the environmental predictors and the predicted responses:
-  **Environmental predictors.**:

  -  The raster on the climate contains the PCA axes on the CHELSA variables.

  -  The raster on resource availability contains the PCA axes on plant communities

  -  The raster on land-use contains the land-use composition at 2500 m.

  -  The raster on beekeeping intensity contains thedensity of hives within 2500 m

-  **Responses**:
- The predicted responses without masking the waterbodies (Diversity_stack_revised_Selected_descriptors.tif).
- The predicted responses masking the waterbodies (masked.stack.all.diversity.tiff).

en: Elevation and waterbodies

en: Zip file containing two rasters: the digital elevation model of Switzerland and the waterbodies of Switzerland.

en: Wild bee trait data

en: Text file containing the scores of the 8 used traits. The data originates from the European trait database (compiler: Stuart Roberts; pollinator loss module of the EU- FP6 ALARM project).

en: Protected areas

en: Protected areas considered, divided in three groups and rasters: protected areas sensu stricto, protected areas sensu lato and all protected areas

en: Deprecated-Predictors

en:
The compress file includes the rster files of the predictors used in the analyses, including the climate, resource availability, land-use and beekeeping intensity metrics.

The raster on the climate contains the PCA axes on the CHELSA variables.
The raster on resource availability contains the PCA axes on plant communities
The raster on land-use contains the land-use composition at 2500 m.
The raster on beekeeping intensity contains thedensity of hives within 2500 m

en:
Raw data supporting the paper "Countrywide wild bee taxonomic and functional diversity reveal a spatial mismatch between alpha and beta-diversity facets across multiple ecological gradients". It contains taxonomic and functional metrics in 3343 community-plots distributed across Switzerland. The calculated metrics are:
- Alpha taxonomic community metrics: species richness and Shannon diversity
- Alpha functional community metrics: Functional richness (using the Trait Onion Peeling index, TOP), functional eveness (using the Trait Even Distribution index, TED) and the functional dispersion.
- Community weighted means of 8 functional traits
- The local community contributions on the functional and taxonomic beta diversity (LCBD).

The dataset also includes the following:
- The used predictors to model the spatial distribution of the community metrics (climate PCA, vegetation PCA, land-use metrics, beekeeping intensity).
-The three types of protected areas, defined according to the protective measures.
- The model evaluation, variable importance and partial dependece data.

anthophila

display_name: anthophila

beta-diversity

display_name: beta-diversity

biodiversity

display_name: biodiversity

climate

display_name: climate

community-indices

display_name: community-indices

elevation-gradient

display_name: elevation-gradient

functional-diversity

display_name: functional-diversity

hymenoptera

display_name: hymenoptera

predictive-modelling

display_name: predictive-modelling

protected-areas

display_name: protected-areas

resource-availability

display_name: resource-availability

switzerland

display_name: switzerland

urbanization

display_name: urbanization

wild-bee

display_name: wild-bee

envidat

en: EnviDat: the Environmental Data Portal

en: Data on wild bee taxonomic and functional diversity in Switzerland


maintainer: Esther R. Frei

en: swiss-alps climate-change picea-abies treeline stillberg g-tree forest larix-decidua seedling-recruitment

en: climate data G-TREE experiment, Davos, Switzerland

en: GTREE Website

en:
 Background information
Climate change-induced range expansion of treeline populations depends on their successful recruitment, which requires dispersal of viable seeds followed by successful establishment of individual propagules. The Global Treeline Range Expansion Experiment (G-TREE) is a global initiative involving researchers from Europe, North America, Australia and New Zealand (Brown et al., 2013). At 15 alpine and Arctic treeline sites worldwide the mechanisms determining the elevational and latitudinal distribution of tree populations are studied using a standardized experimental approach. In summer 2013, a multifactorial seedling recruitment experiment has been established at the Stillberg ecological treeline research site. The aim of this experiment, is to quantify the effect of multiple abiotic and biotic drivers on emergence, survival, and growth of *Larix decidua* and *Picea abies* seedlings in replicated plots along an elevation gradient with three sites below (1930 m a.s.l.), at (2090 m a.s.l.), and above treeline (2410 m a.s.l.; Frei et al., 2018). All plots have been surveyed annually to count seedlings and to measure their total height. Additional environmental factors, such as soil temperature, have been recorded.

 Experimental design
The Stillberg research area is located in the Eastern Swiss Alps near Davos, Switzerland. The site has been used for several long-term monitoring as well as experimental studies for the last four decades. Our G-TREE experiment consists of a lowest site located in a subalpine Larch-Spruce forest (*Larici-Picetum*) dominated by *Larix decidua* and *Picea abies* (1930 m a.s.l.), a transition zone site dominated by alpine shrubs (2100 m a.s.l.), and an uppermost site in an alpine meadow with some dwarf shrubs (2390 m a.s.l.). The three experimental sites were set up following the standard protocol of the global G-TREE initiative (Brown et al., 2013). In a split-plot design, 20 plots (224 cm × 45 cm) were established at each site, which were randomly assigned to the 2 × 2 treatment combinations of the main factors seeding and scarification (i.e. seeding and scarification, seeding only, scarification only, and full control), resulting in five replications per main treatment combination. Each plot was divided into 16 split-plots (22.5 cm × 28 cm), to which treatment combinations of four additional two-level factors species (larch and spruce), provenance (low- and high-elevation), herbivore exclosure (with and without exclosure), and seeding year (2013, 2014) were randomly assigned, which resulted in a total of 960 split-plots (Details see Frei et al. 2018).

 Data description
All plots have been surveyed annually to count seedlings and to measure their total height. Seedling height was assessed with a hand ruler as the total length from the original emerging point to the apical meristem (Details see Frei et al. 2018). Additionally, soil temperature at each site, has been continuously recorded since 2013. Here, we present data from eight years (2013–2021).

climate-change

display_name: climate-change

forest

display_name: forest

g-tree

display_name: g-tree

larix-decidua

display_name: larix-decidua

picea-abies

display_name: picea-abies

seedling-recruitment

display_name: seedling-recruitment

stillberg

display_name: stillberg

swiss-alps

display_name: swiss-alps

treeline

display_name: treeline

educ

en: Education, culture and sport

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: G-TREE: Global Treeline Range Expansion Experiment Davos, Switzerland


en: snow-height snow-onset-and-disappearance-dates parallel-measurements climate-indicators snow-depth

en: Sdur

en: Potential sunshine duration during MA and mean snow disappearance dates for a selection of station pairs.

en: snow climate indicators

en: Snow climate indicators derived from parallel manual snow measurements

en: ReadMe

en: File containing information about variables used in this data set

en: Data set consisting of snow climate indicators derived from parallel manual snow measurements in Switzerland.

climate-indicators

display_name: climate-indicators

parallel-measurements

display_name: parallel-measurements

snow-depth

display_name: snow-depth

snow-height

display_name: snow-height

snow-onset-and-disappearance-dates

display_name: snow-onset-and-disappearance-dates


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

en: Link to flux and meteo data

en: Near real-time flux and meteo data uploaded daily to the ICOS carbon portal

en: LWF plot Davos. Photo: Peter Waldner, 2018

en:
![alt text](https://www.envidat.ch/dataset/801cdd7e-f5b0-4998-bb8a-bd6d2ae8baa2/resource/f2ef5505-e0ea-493d-8b86-4f27dd556da8/download/lwf_davos.jpg "LWF Davos")

This research site is located on the Seehorn mountain near Davos within a managed subalpine coniferous forest in the Swiss Alps. Seehronwald Davos site is dedicated to forest ecosystem research with current projects focusing on topics of climate change, ecosystem carbon balance, ecophysiology, vegetation and soil sciences. The site belongs to one of the best equipped long-term forest ecology research sites of the world. Time series of climate variables, ecosystem gas exchange (eddy covariance), tree physiology records (sap flow, stem radius changes), and air pollution data cover the history of this site over more than 20 years. Records of local climate variables started in 1876. Since 2013 the site is part of [ICOS](https://www.icos-cp.eu), which awarded the infrastructure the CLASS 1 label on 21 November 2019. The site is part of the following national and international networks and encourages further synergistic collaborations with scientists from all over the world:

* National Air Pollution Monitoring Network ([NABEL](https://www.empa.ch/web/s503/nabel))
* ICOS Switzerland ([ICOS-CH](https://www.icos-switzerland.ch/davos))
* [TreeNet](https://treenet.info/switzerland/davos): The biological drought and growth indicator network
* Long-term Forest Ecosystem Research ([LWF](https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/davos.html))
* [Swiss FluxNet](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav)
* Ecosystem Research ([ExpeER](http://www.expeeronline.eu/43-expeer-ta-sites/131-davos-seehornwald-switzerland.html))
* Long Term Ecological Research ([LTER](https://www.lter-europe.net))
* [ICP Forests](http://icp-forests.net): the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests

The site measurements are jointly run by the Swiss Federal Laboratories for Materials Science and Technology ([EMPA](https://www.empa.ch)), the Swiss Federal Institute of Technology Zurich ([ETHZ](https://www.gl.ethz.ch)), and the Swiss Federal Research Institute ([WSL](https://www.wsl.ch)) in Birmensdorf and Davos. The infrastructure is provided by the Federal Office of Environment ([FOEN](https://www.bafu.admin.ch/bafu/en/home/topics/air/state/data/national-air-pollution-monitoring-network--nabel-.html)). All partners are grateful to forest owners and to the forestry service of the community of Davos for their continuous support.

LWF Plot Davos

- Community: Davos / canton GR
- Date of installation: 15.06.2006
- Size of the plot: 0.6 ha
- Altitude: : 1635-1665
- Geology (in German): Untergrund: - Oberfläche:
- Provisional soil type (WSL):
- Woodland association after EK72: 58: Larici-Piceetum
- Main tree species: Picea abies
- Management system: high forest
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 47.0 cm
- Number of trees BHD >= 12 cm (2006): 498
- Maximum tree age: Picea abies: 200 - 390 yr

flux

display_name: flux

icos

display_name: icos

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-fluxnet

display_name: swiss-fluxnet

swiss-forest-lab

display_name: swiss-forest-lab

envidat

en: EnviDat: the Environmental Data Portal

en: LWF Seehornwald Davos long-term research site


en: photovoltaic biomethane gis complementarity bioenergy

en: Additional references

en: Additional references to an article

en: Manure monthly resolution

en: This gives details on how calculation were made to estimate the monthly distribution of manure produced in Switzerland.

en: Supplementary Information

en: Supplementary Information with regards to how the solar and biomass resources were calculated for the paper. Electricity yields of PV depending on various variables

en:
Base data for quantitites of manure and agricultural residues available in Switzerland aggregated per municipalities and provided yearly and monthly.

Method:
The biomass considered in this study consists of agricultural residues arising according to current practice, i.e. mainly manure and some crop by-products. Availability was calculated following the method of (Burg et al., 2018)   based on the updated national agricultural inventory (FSO, 2018). The animal manure potential considered here represents the total annually collected amount, i.e. generated amount after subtracting losses from grazing. With regard to crop by-products, only a small percentage of the theoretical potential was considered to be available since most crop by-products cannot be readily collected today (Burg et al., 2018). The potential biogas production used in the present study was calculated analogously as in Burg et al. (2018) based on a specific biogas yield from fresh biomass. The additional potential of co-substrates from industrial or municipal organic wastes was not considered here.
Bsed on G1G22 (Municipalities in 2022) according to BFS

References:
Burg, V., Bowman, G., Erni, M., Lemm, R., Thees, O., 2018a. Analyzing the potential of domestic biomass resources for the energy transition in Switzerland. Biomass and Bioenergy 111, 60-69. https://doi.org/https://doi.org/10.1016/j.biombioe.2018.02.007
FSO, 2018. Individual data from the agricultural structure survey STRU. Federal statistical office FSO, Bern.

en:
Additional references to the article:
Linking solar and biomass resources to generate renewable en-ergy: can we find local complementarities in the agricultural setting?
Gillianne Bowman, Thierry Huber, Vanessa Burg
Energies, https://www.mdpi.com/1996-1073/16/3/1486

Today, the energy transition is underway to tackle the problems of climate change and energy sufficiency. For this transition to succeed, it is essential to use all available re-newable energy resources most efficiently. However, renewable energies often bring high volatility that needs to be balanced. One solution is to combine the use of different renewable sources to increase the overall energy output or reduce its environmental impact. Here, we estimate the agricultural solar and biomass resources at the local level in Switzerland, considering their spatial and temporal variability using Geographic In-formation Systems. We then identify the technologies that could allow synergies or complementarities. Overall, the technical agricultural resources potential is ~15 PJ/annus biogas yield from residual biomass and ~10 TWh/a electricity from solar installed on roofs (equivalent to ~36 PJ/a). Anaerobic digestion, combined heat & power plant, Raw manure separation, Biomethane upgrading, Power to X, Electrolysis, Chill generation and Pho-tovoltaic on biogas facilities could foster complementarity in the system if resources are pooled within the agricultural setting. Temporal complementarity at the farm scale can only lead to partial autarchy. The possible benefits from these complementarities should be better identified, particulary in looking looking at the economic viability of such systems.

bioenergy

display_name: bioenergy

biomethane

display_name: biomethane

complementarity

display_name: complementarity

gis

display_name: gis

photovoltaic

display_name: photovoltaic

educ

en: Education, culture and sport

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: Linking solar and biomass resources to generate renewable energy: can we find local complementarities in the agricultural setting?


rorcc

maintainer: Tobias Wechsler

title_for_slug: rorcc

en: run-of-river-power-plants environmental-flow hydropower design-discharge climate-change

en: RoRCC

en:
The dataset “RoRCC” consists of simulation-based results on climate change impacts on Alpine RoR power production; it is based on 21 Swiss RoR power plants, with a total production of 5.9 TWh a-1. The dataset contains the following information:
-	metadata on the RoR power plants under consideration
-	annual and seasonal production scenarios differentiated into three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020–2049, T2: 2045–2074, T3: 2070–2099)
-	annual and seasonal production loss due to environmental flow requirements
-	annual and seasonal the technical increase potential (via design discharge optimisation)
-	annual and seasonal changes in the hydrological cycle for the three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020–2049, T2: 2045–2074, T3: 2070–2099)

format: XLS

en:
The dataset "RoRCC" consists of simulation-based results on climate change impacts on Alpine RoR power production; it is based on 21 Swiss RoR power plants, with a total production of 5.9 TWh a-1. The dataset contains the following information:
1) metadata on the RoR power plants under consideration, 2) annual and seasonal production potential scenarios under into three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020–2049, T2: 2045–2074, T3: 2070–2099), 3) annual and seasonal streamflow scenarios, 4) annual and seasonal production loss due to environmental flow requirements, 5) annual and seasonal the technical increase potential (via design discharge optimisation) and 6) annual and seasonal changes in the hydrological cycle.

climate-change

display_name: climate-change

design-discharge

display_name: design-discharge

environmental-flow

display_name: environmental-flow

hydropower

display_name: hydropower

run-of-river-power-plants

display_name: run-of-river-power-plants


causal-effect-of-mfoz

title_for_slug: causal-effect-of-mfoz

en: propensity-score-matching built-up-land-change the-major-function-oriented-zone fujian-province selection-bias causality

en: Data and Stata code for repeating the results

en:
Data.xlsx is the variables of 952 townships
Table 1-2 NN(1-1)_noreplace_caliper(0.01).do is STATA code to produce the results in Table 1 and 2
Figure 5 Distribution of the propensity scores.R is R code rto produce Figure 5
Figure 5 Standard mean difference.R is R code rto produce Figure 5

en:
Title: Closer to causality: How effective is spatial planning in governing built-up land expansion in Fujian Province, China?

Research objective: The Major Function Oriented Zone (MFOZ), the first strategic spatial plan in China, is developed to achieve a coordinated regional development, through spatial regulation and zoning of development. The MFOZ he MFOZ divided land into four major function-oriented zones: The development-optimized zone, the development-prioritised zone, the development-restricted zone, and the development-prohibited zone. We used propensity score marching to evaluate the effect of the MFOZ on built-up land expansion in Fujian Province over three time intervals (2013–2015, 2013–2018 and 2013–2020).

Data: Data.xlsx contains the variables of 954 towns in Fujian Province.
Town_ID is the town unique ID;
County_ID is the county unique ID;
City_ID is the city unique ID;
MFOZ is the the development-prioritised zone and the development-restricted zone (The development-optimized zone and the development-prohibited zone are excluded);
Builtup_13_15 is the built-up land expansion from 2013 to 2015;
Builtup_13_18 is the built-up land expansion from 2013 to 2018;
Builtup_13_20 is the built-up land expansion from 2013 to 2020;
Dis2water is the Euclidean distance from the town to the nearest waterbody;
Slope is the the average slope within the town;
GDP is the average GDP in 2010 within the town;
Pop is the average population in 2010 within the town;
Road is the average population in 2010 within the town;
Dis2city is the Euclidean distance from the town to the nearest prefectural city centre;
Nei_Arable, Nei_Forest, and Nei_Built.up are the area of arable land, forest land, and built-up land neighbouring town i in 2010.

Method: we used the propensity score matching to compare the changes in the amount of built-up land in the towns of the development-prioritised zone with the matched towns of the development-restricted zone. Additionally, we used three evaluation intervals (2013–2015, 2013–2018 and 2013–2020) to evaluate temporal variation in the causal effect of the MFOZ on built-up land expansion.

built-up-land-change

display_name: built-up-land-change

causality

display_name: causality

fujian-province

display_name: fujian-province

propensity-score-matching

display_name: propensity-score-matching

selection-bias

display_name: selection-bias

the-major-function-oriented-zone

display_name: the-major-function-oriented-zone

envidat

en: EnviDat: the Environmental Data Portal

en: Causal effect of MFOZ


maintainer: Matthias Haeni

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

en: Field station data (VSF)

en: Data file.

en: Data plot of the field station (VSF)

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

mimetype: application/pdf

en: Data plot of the forest station (VSB)

en: Site photo of the field station (VSF)

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

mimetype: image/jpeg

en: Site photo of the forest station (VSB)

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

en: Forest station data (VSB)

en: Site aerial photo (Visp)

en: Site aerial photo from both Visp stations. Source: Google. 2016. Site aerial photo from both Visp 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 Visp in Switzerland where one station is located within a natural mixed forest stand (VSB) with Scots pine (_Pinus sylvestris_; 40-80 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VSF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Visp is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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


en: impact-pressure modelling hazard-mapping avalanche release ramms

en: Scenario 30 years with forest

en:
PRA_.shp = avalanche release areas shape file with the attributes in the attribute table
_maxHEIGHT.tif = maximum flow height [m]
_maxPRESSURE.tif = maximum impact pressure [kPa]
_maxVELOCITY.tif = maximum flow velocity [m/s]
_outlines = outline shape file of every avalanche simulation linked to the PRA shape file by ID

en: Extent of the canton of Grisons

media_type: application/json

format: JSON

en: Scenario 300 years without forest

en: Scenario 100 years without forest

en: Scenario 10 years without forest

en: Scenario 10years with Forest

en: Scenario 300 years with forest

en: Scenario 30 years without forest

en: Scenario 100 years with forest

en:
We developed a workflow to generate Large Scale Hazard Simulations for avalanches based on digital elevation models and information on the protective function of the forest. This datasets contains the potential avalanche release areas (PRA) as polygons, the simulation outputs (maximum pressure, maximum flow velocity and maximum flow height) as .tif rasters and the outlines of the simulated avalanches (polygon) for the entire area of the canton of Grisons (7105 km2). The simulations are performed for the scenarios wit return periods of 10, 30, 100 and 300 years, once with (FOR) and once without (NoFor) taking the effect of the forest into account.

The details can be found in this publication:

Bühler, Y., Bebi, P., Christen, M., Margreth, S., Stoffel, L., Stoffel, A., Marty, C., Schmucki, G., Caviezel, A., Kühne, R., Wohlwend, S., and Bartelt, P.: Automated avalanche hazard indication mapping on state wide scale, Nat. Hazards Earth Syst. Sci. Discuss., 2022, 1-22, 10.5194/nhess-2022-11, 2022.

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

impact-pressure

display_name: impact-pressure

modelling

display_name: modelling

ramms

display_name: ramms

release

display_name: release

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: Large Scale Hazard Indication Simulations for avalanches, canton of Grisons


title_for_slug: total-stem-number-by-type-of-damage

en:
Number of all living and dead trees and shrubs starting at 12 cm dbh where a particular type of damage (including no damage, dead or lying) was observed. One tree may have more than one type of damage, which means it may contribute to the total number of stems for several different types of damage.

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

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

damage-of-tree

display_name: damage-of-tree

economic-region

display_name: economic-region

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

total-stem-number

display_name: total-stem-number

total-stem-number-by-type-of-damage

display_name: total-stem-number-by-type-of-damage

en: Total stem number by type of damage


maintainer: Alexandre Buttler

en: transplantation-experiment silvopastoral-systems soil climate-change biogeochemisty

en: Dataset

en: Zip archive of all datasets detailed in study.

en:
Silvopastoral systems are highly productive and combine long-term wood production with annual provision of forage for livestock. In the Swiss Jura Mountains these systems are a key component of the landscape. As in other cold biomes, climate change can potentially accelerate landscape change within these historically sustainable systems.

In order to anticipate the evolution of subalpine wooded pasture ecosystems under future climate and land-use changes, this project focused on the interplay between soil, vegetation and climate. It was aimed at providing experimental evidence for chief ecosystem processes, with emphasis on the quality of the ecosystem services provided. The main interest was placed on vegetation turf resistance to climate change along an unwooded – sparsely wooded - densely wooded pasture gradient (land-use intensity), where plant productivity, diversity and succession along with rates of carbon cycling and microbial activity provided measures of ecosystem functioning at both plot and landscape level.

Experimental transplantation of monolith soil turfs to lower altitudes allowed to simulate soil warming and reduced annual precipitation. In order to simulate a year-round warmer and drier climate the natural climate variation along an altitudinal gradient was used as a proxy. The aim was to simulate realistic climate change scenarios for the second half of the 21st century predicted by the IPCC report and downscaled for Switzerland providing regionalized interpolated projections integrating therein trends for temperature increase and precipitation decrease. By using permanent meteorological stations within the network of the Federal Office of Meteorology and Climatology (MeteoSwiss), we obtained high resolution regional data on the variation of mean annual temperature (MAT) and mean annual precipitation (MAP) in relation to altitude in the Swiss Jura Mountains. We observed a general increase of +0.5 K in MAT and a decrease of -20 % MAP for each 100 m decrease in altitude along the SE slope of the Swiss Jura Mountains. These relationships served for the selection of the transplantation sites such that in comparison to a control site at 1350 m a.s.l. (Combe des Amburnex, N 46°54’, E 6°23’) a +2 K MAT and -20 % MAP was achieved at 1010 m a.s.l. (Saint-George, N 46°52’, E 6°26’), a +4 K MAT and -40 % MAP at 570 m a.s.l., (Arboretum d’Aubonne, N 46°51’, E 6°37’), and a +5 K MAT and -50 % MAP at 395 m a.s.l. (Les Bois Chamblard, N 46°47’, E 6°41’). The two stations at 1010 m a.s.l. and 570 m a.s.l. corresponded to the IPCC scenario A1B for a moderate increase in greenhouse gas emissions and to scenario A2 for a high increase in greenhouse gas emissions, respectively. The station at 395 m a.s.l. was chosen to represent an extreme scenario with climate variables lying at the positive tail distribution of model predictions under the A2 scenario.

Soil cores were assembled into rectangular PVC boxes of 60  80 cm2 size and of 35 cm height. All mesocosms were dug down to surface level into previously prepared trenches in the ground thus preventing lateral heat exchange with the atmosphere. Since at each site the mesocosms were placed in a common garden with no light interception, mesocosms with turfs from the two wooded pastures were shaded from direct sun light to simulate the natural light conditions in the corresponding habitats. Each mesocosm was equipped with a drainage system and was connected to a water tank thus representing a zero potential lysimeter collecting soil solution and precipitation/snowmelt runoff. ECH2O EC-TM sensor probes coupled to Em50 data-loggers (Decagon Devices, Inc., USA) recorded soil temperature and volumetric water content in each mesocosm at the top-soil (0 to -3 cm) every minute and data were averaged over one hour intervals. Climate parameters at each transplantation site were monitored continuously throughout the experiment by means of automated weather stations (Sensor Scope Sàrl, Switzerland), measuring rain precipitation (non-heated tipping bucket gauges) and air temperature and humidity 2 m above the ground surface at one minute intervals.

A list of above- and belowground variables were measured to assess the resilience of biogeochemical processes, plant productivity, tree regeneration, and carbon sequestration for each respective land-use practice. Furthermore, the experimental data were used to improve on (parameterization) the existing spatially explicit, dynamic model WoodPaM and refine the modelʼs climatic and land-use variables so that different scenarios of climate change and land use change could be simulated. Natural and management induced disturbance patterns were incorporated into the model.
The data have been made available within the project CCES Mounted. The climate stations Sensorscope are still in use within the project CLIMARBRE (Wald und Klimawandel, WSL/BAFU).

#References

1. Puissant, J., Cécillon, L., Mills, R.T.E., Robroek, B.J.M. Gavazov, K., De Danieli, S., Spiegelberger, T., Buttler, A., Brun, J.J. 2015. Seasonal influence of climate manipulation on microbial community structure and function in mountain soils. Soil Biology and Biochemistry 80: 296–305.
2. Mills, R., K. Gavazov, T. Spiegelberger, D. Johnson and A. Buttler 2014. Diminished soil functions occur under simulated climate change in a sup-alpine pasture, but heterotrophic temperature sensitivity indicates microbial resilience. Science of the Total Environment, vol. 473–474(0): 465-472.
3. Gavazov, K., Spiegelberger, T. and Buttler, A. 2014. Transplantation of subalpine wood-pasture turfs along a natural climatic gradient reveals lower resistance of unwooded pastures to climate change compared to wooded ones. Oecologia (174) : 1425-1435.
4. Peringer A., Siehoff S., Chételat J., Spiegelberger T., Buttler A. & Gillet F. 2013. Past and future landscape dynamics in pasture-woodlands of the Swiss Jura Mountains under climate change. Ecology and Society, 18, 3: 11. DOI: 10.5751/ES-05600-180311. [online] URL: http://www.ecologyandsociety.org/vol18/iss3/art11/
5. Gavazov, K. S., A. Peringer, A. Buttler, F. Gillet and T. Spiegelberger. 2013. Dynamics of Forage Production in Pasture-woodlands of the Swiss Jura Mountains under Projected Climate Change Scenarios. Ecology and Society 18 (1): 38. [online] URL: http://www.ecologyandsociety.org/vol18/iss1/art38/

biogeochemisty

display_name: biogeochemisty

climate-change

display_name: climate-change

silvopastoral-systems

display_name: silvopastoral-systems

soil

display_name: soil

transplantation-experiment

display_name: transplantation-experiment

en: Biogeochemical data from a transplantation experiment of monolith soil turfs along an altitudinal gradient to simulate climate change scenarios


maintainer: Leonie Schönbeck

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

en: Pfynwald gas exchange

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

en: Methods survey

en: Parameters and methods for point measurements / survey measurements

en: Instrument details

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

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

en: Methods light response

en: Methods and parameters for light response curves

en: Variable names and units

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

en: Methods diurnal measurements

en:
Parameters and methodology for diurnal measurements

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

en: Methods ACi curves

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

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

en: Methods fluorescence

en:
Parameters and methodology for Fluorescence measurements

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

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

acclimation

display_name: acclimation

drought

display_name: drought

fluorescence

display_name: fluorescence

gas-exchange

display_name: gas-exchange

photosynthesis

display_name: photosynthesis

pinus-sylvestris

display_name: pinus-sylvestris

vpd

display_name: vpd

en: 2013-2020 gas exchange at Pfynwald


en: climate precipitation aridity potential-evapotranspiration weather

en: 1980-2011 average potential evapotranspiration (in mm per year) at 500m resolution over Switzerland

media_type: image/tiff

en: Long-term (1980-2011) average potential evapotranspiration (in mm per year) at 500m resolution over Switzerland. GeoTIFF raster file can be read in R using the command raster(“pet_ch_longterm_yr_avg.tif”), after installing packages "raster" and "rgdal".

format: TIFF

en: 1980-2011 average precipitation over Switzerland at 500m resolution

en: Long-term (1980-2011) average precipitation (in mm per year) at 500m resolution over Switzerland. GeoTIFF raster file can be read in R using the command raster(“pet_ch_longterm_yr_avg.tif”), after installing packages "raster" and "rgdal".

en: Long-term (1980-2011) average annual precipitation (pcp_ch_longterm_yr_avg.tif) and potential evapotranspiration (pet_ch_longterm_yr_avg.tif) at 500m resolution. Units are mm per year. Files are GeoTIFF rasters, and can be read in R using the command raster("pcp_ch_longterm_yr_avg.tif), after installing packages "raster" and "rgdal".

aridity

display_name: aridity

climate

display_name: climate

potential-evapotranspiration

display_name: potential-evapotranspiration

precipitation

display_name: precipitation

weather

display_name: weather

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: Average precipitation and PET over Switzerland at 500m resolution