snow-depth-mapping

en:
The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData).
For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the
state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground - 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths - 15m and smaller than - -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth.
The work is published in:
Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015

en: Snow Depth Mapping

en: snow-water-equivalent avalanche aerial-imagery snow-hydrology dsm snow-depth image-matching

title_for_slug: snow-depth-mapping

en:
Resource Title: Snow depth map 20160420
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/bc035833-3193-4394-af92-f8d195d5e034/download/snowmap_20160420_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

en:
Resource Title: Snow depth map 20160309
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/ceae68a6-28f9-43f4-8ff5-9715588ade57/download/snowmap_20160309_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

en:
Resource Title: Snow depth map 20120320
    Size: 412272526 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/aff41055-8e6e-4287-b52d-2c2fb61633c2/download/snowmap_2012_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en:
Resource Title: Snow depth mapping in high-alpine catchments using digital photogrammetry
    Size: 8122296 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/3b711e29-cea1-48e8-8de6-a008850c4f94/download/snow-depth-mapping-in-high-alpine-catchments.pdf
    Description:

en:
Resource Title: Snow depth map 20100416
    Size: 412324171 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/4ba96052-9e3e-4b7a-9968-2dcc58fc96c0/download/snowmap_2010_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en:
Resource Title: Snow depth map 20140417
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/5cf5d3a5-555d-429e-9819-6c93ccce6dfc/download/snowmap_2014_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.18 m

en:
Resource Title: Snow depth map 20160126
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/9229984f-b417-45e5-8dbe-031f295d0406/download/snowmap_20160126_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m
Because of very diffuse light conditions at time of image acquisition the quality of the resulting snow depth values are in some regions quite poor.

en:
Resource Title: Snow depth map 20130415
    Size: 412272526 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/da3ca703-c629-45c0-8dac-e78645b0fa5e/download/snowmap_2013_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en:
Resource Title: Snow depth map 20150415
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/578e95ce-ffa0-4fc6-8109-d9653b2c622a/download/snowmap_2015_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

aerial-imagery

display_name: aerial-imagery

avalanche

display_name: avalanche

dsm

display_name: dsm

image-matching

display_name: image-matching

snow-depth

display_name: snow-depth

snow-hydrology

display_name: snow-hydrology

snow-water-equivalent

display_name: snow-water-equivalent


$oid: OBSOLETE

en:
The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)).
 Manuscript Abstract:
Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.

en: Energy- and momentum-conserving model of splash entrainment in sand and snow saltation

en: erosion model snow mars sediment-transport

envidat

approval_status: approved

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.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

en:
Resource Title:
    Size: 3626 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/6ee21796-08ad-4069-a4cb-da6c35ffede7/download/figure4a.txt
    Description: Datasets of Figure 4a

format: TXT

media_type: text/plain

en:
Resource Title:
    Size: 3587 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/1f30b883-fc62-4bbf-a8f5-88f716008097/download/figure2.txt
    Description: Datasets of Figure 2

en:
Resource Title:
    Size: 2904 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/709f4330-9a35-4685-aac8-347067a5e483/download/figure4b.txt
    Description: Datasets of Figure 4b

en:
Resource Title:
    Size: 2190 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/82479779-1b20-486f-b4bc-414c6024c25d/download/figure3b.txt
    Description: Datasets of Figure 3b

en:
Resource Title:
    Size: 1434 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/8ab9bafb-62f5-4420-80da-a2a96d22be74/download/figure3a.txt
    Description: Datasets of Figure 3a

erosion

display_name: erosion

mars

display_name: mars

model

display_name: model

sediment-transport

display_name: sediment-transport

snow

display_name: snow


seilaplan

en:
Cable-based technologies have been a backbone for harvesting on steep slopes. The layout of a single cable road is challenging because one must identify intermediate support locations and heights that guarantee structural safety and operational efficiency while minimizing set-up and dismantling costs. Seilaplan optimizes the layout of a cable road by
Seilaplan stands for Cable Road Layout Planner. Seilaplan is able to calculate the optimal rope line layout (position and height of the supports) between defined start and end coordinates on the basis of a digital elevation model (DEM).
The program is designed for Central European conditions and is designed on the basis of a fixed suspension rope anchored at both ends. For the calculation of the properties of the load path curve an iterative method is used, which was described by Zweifel (1960) and was developed especially for standing skylines. When testing the feasibility of the cable line, care is taken that 1) the maximum permissible stresses in the skyline are not exceeded, 2) there is a minimum distance between the load path and the ground and 3) when using a gravitational system, there is a minimum inclination in the load path. The solution is selected which has a minimum number of supports in the first priority and minimizes the support height in the second priority.
The newly developed method calculates the load path curve and the forces occurring in it more accurately than tools available on the market to date (status 2019) and is able to determine the optimum position and height of the intermediate supports.
The reason for the more accurate results of the new tool is the assumption that the skyline is anchored at both end points. Forest cable yarders used in Europe have a skyline that is fixed at both ends. The behaviour of fixed-anchored suspension ropes is very difficult to describe mathematically and cannot be solved analytically. For this reason, simplified linearized assumptions have so far been used in the forestry sector, which corresponds to the behaviour of a weight-tensioned suspension rope and is known as the Pestal method (1961). Weight-tensioned suspension ropes are used for passenger transport.
For the calculation of the load path curve we use an iterative method, which was described by Zweifel (1960) and developed especially for fixed anchored suspension ropes. This makes mathematics much more demanding, but leads to more accurate and realistic results. Since there are no current models which describe the installation costs with adequate accuracy, the solution sought is the one which has a minimum number of supports in the first priority and minimises the support height in the second priority (Figure 2). The presented method is the first one, which starts from a fixed anchored supporting rope and identifies the mathematically optimal column layout at the same time. In contrast to methods that assume a weight-tensioned suspension rope, this approach achieves more realistic solutions with longer spans and lower support heights, which ultimately leads to lower installation costs. Background information on rope mechanics and calculation methods is documented in Bont and Heinimann (2012).
License: GNU, General Public License, Version 2 or newer.
Literature:
Bont, L., & Heinimann, H. R. (2012). Optimum geometric layout of a single cable road. European journal of forest research, 131(5), 1439-1448.

en: Seilaplan

en: software qgis cable-planning catenary-load-path steep-terrain-harvesting python cable-yarding

title_for_slug: seilaplan

format: ZIP

cable-planning

display_name: cable-planning

cable-yarding

display_name: cable-yarding

catenary-load-path

display_name: catenary-load-path

python

display_name: python

qgis

display_name: qgis

software

display_name: software

steep-terrain-harvesting

display_name: steep-terrain-harvesting


stem-radius-change-data-pfynwald

en:
**Continuously measured stem radius change data (time series) from high-precision point dendrometers (Natkon) mounted on the stems of Pinus sylvestris trees at 1.5 m in the Pfynwald VPDrought experimental area.**
The data originates from the **TreeNet database**. The published data represents the highest quality data following automatic and manual corrections (LM data level).
**The dataset contains the following files:**
- 10-minute resolution time series (.parquet)
- Daily resolution time series (.csv and .parquet)
- Metadata (.csv)
- Readme (.txt)
Main variables are stem radius changes (SRC), tree water deficit (TWD) and radial stem growth (GRO).

**The site area contains the following site_ids (treatments)**
| 86 | Pfynwald-Control
| 89 | Pfynwald-Irr_Stop
| 90 | Pfynwald-Irrigation
| 145 | Pfynwald-Irrigation_VPD
| 146 | Pfynwald-Roof
| 147 | Pfynwald-Roof_VPD
The measurements cover data from 2011 to 2025. All data is in UTC + 1 hour (Central European Time CET, winter time, no DST).

en: Stem Radius Change Data Pfynwald

en: drought-legacy time-series treenet pinus-sylvestris wood-growth bark-growth forest drought-experiment ecophysiology dendrometer alps drought-stress treewaterdeficit automated-monitoring data-publication climate-change

title_for_slug: stem-radius-change-data-pfynwald

en:
Resource Title: Pfynwald_README.txt
    Size: 12082 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/60f3c95c-fe5d-49f0-89af-f325ef864cf0/resource/8c24a11c-ecda-404c-9dd4-078629e89559/download/pfynwald_readme.txt
    Description:

en:
Resource Title: Pfynwald_Dendro_2010-2025.zip
    Size: 446097419 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/60f3c95c-fe5d-49f0-89af-f325ef864cf0/resource/aec4c998-c6dd-4000-bd4e-c9bc79fe7b22/download/pfynwald_dendro_2010-2025.zip
    Description:

media_type: application/zip

alps

display_name: alps

automated-monitoring

display_name: automated-monitoring

bark-growth

display_name: bark-growth

climate-change

display_name: climate-change

data-publication

display_name: data-publication

dendrometer

display_name: dendrometer

drought-experiment

display_name: drought-experiment

drought-legacy

display_name: drought-legacy

drought-stress

display_name: drought-stress

ecophysiology

display_name: ecophysiology

forest

display_name: forest

pinus-sylvestris

display_name: pinus-sylvestris

time-series

display_name: time-series

treenet

display_name: treenet

treewaterdeficit

display_name: treewaterdeficit

wood-growth

display_name: wood-growth


en:
**Continuously measured stem radius change data (time series) from high-precision point dendrometers (Natkon) mounted on the stems of Pinus sylvestris trees at 1.5 m in the Pfynwald VPDrought experimental area.**
The data originates from the TreeNet database. The published data represents the highest quality data following automatic and manual corrections (LM data level).
The dataset contains the following files:
- 10-minute resolution time series (.parquet)
- Daily resolution time series (.parquet)
- Readme (.txt)
Main variables are stem radius changes (SRC), tree water deficit (TWD) and radial stem growth (GRO).
The measurements cover data from 2011 to 2025. All data is in UTC + 1 hour (Central European Time CET, winter time, no DST).

en: Pfynwald VPDrought  experiment stem radius changes (TreeNet)

en: twd vpd pfynwald stem-growth vpdrought drought pinus-sylvestris irrigation point-dendrometer

en:
Resource Title: pfynwald_vpdrought_dendrometer_2024.zip
    Size: 61781663 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/571cf7ad-edda-4e1f-9e9d-45014c39a9e2/resource/425594e2-7210-49a8-82b7-1bdfc4cbaf33/download/pfynwald_vpdrought_dendrometer_2024.zip
    Description:

en:
Resource Title: pfynwald_vpdrought_dendrometer_2025.zip
    Size: 62493309 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/571cf7ad-edda-4e1f-9e9d-45014c39a9e2/resource/cea43ffe-6437-4b0e-8d58-398026cbde41/download/pfynwald_vpdrought_dendrometer_2025.zip
    Description:

drought

display_name: drought

irrigation

display_name: irrigation

pfynwald

display_name: pfynwald

point-dendrometer

display_name: point-dendrometer

stem-growth

display_name: stem-growth

twd

display_name: twd

vpd

display_name: vpd

vpdrought

display_name: vpdrought


en: This dataset includes the diversity of soil prokaryotes, fungi and micro-eukaryotes in unmanaged forest reserves with managed forests across nine locations within Switzerland. Forests have been exploited for resources for centuries, which has caused disturbances, altered community structure, and reduced diversity at different scales. Strict forest reserves, where no forest management is practiced, have been implemented to promote biodiversity, by allowing old trees with microhabitats and high deadwood amounts. However, the effectiveness of forest reserves on soil microbial diversity and functional traits are little known at a national scale in Swiss forests. The associated metadata includes local environmental conditions, such as soil pH, organic C, N, deadwood volume, MAT and MAP, and forest type (coniferous vs deciduous).

en: Management practices shape soil fungal diversity and functional traits in Swiss forests

en: prokaryotes soil-biodiversity microeukaryotes forest-management fungi

envidat

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

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: Environmental data
    Size: 13926 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/04e9459c-7024-46c3-9c33-a50a33da55b2/resource/c27f01d0-217e-4b50-88c0-70421ec0334f/download/forest-management-soilbd-environmental-data-envidat.csv
    Description: Environmental Data such as soil abiotic properties

format: XLS

en:
Resource Title: Microeukaryotes
    Size: 2512555868 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.780%2FForestMan_SoilBD_Microeukaryotes%20%2818S%29%2F
    Description:

en:
Resource Title: Prokaryotes
    Size: 2738041651 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.780%2FForestMan_SoilBD_Prokaryotes%20%2816S%29%2F
    Description:

en:
Resource Title: Fungi
    Size: 2931315180 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.780%2FForestMan_SoilBD_Fungi%20%28ITS%29%2F
    Description:

forest-management

display_name: forest-management

fungi

display_name: fungi

microeukaryotes

display_name: microeukaryotes

prokaryotes

display_name: prokaryotes

soil-biodiversity

display_name: soil-biodiversity


en: This dataset comprises qualitative data for the development of the five Swiss Shared Socioeconomic Pathways (SSP-CH) up to the year 2100. First, it comprises projections of 22 socioeconomic factors, each represented by four to five factor-specific trajectories that form the basis of the qualitative scenario storylines. Second, it includes an input table providing concise qualitative characterizations of 63 socioeconomic factors across the five SSP-CH scenarios.

en: Shared Socioeconomic Pathways for Switzerland (SSP-CH): qualitative data for scenario development

en: socioeconomic-scenarios qualitative-scenarios national-ssps scenario-development ssp-storylines

en:
Resource Title: Input-table_final_e.xlsx
    Size: 45599 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/3e5310e5-4038-4782-b3c9-b64100709e32/resource/9eef7ee5-8a6d-47c2-9952-447cd39018e4/download/input-table_final_e.xlsx
    Description: SSP-CH Input table: qualitative description of 63 socioeconomic factors in the 5 SSP-CH (english). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Programme. The following individuals participated in the compilation of this data: Gubler, L., Tschumi, P., Pütz, M., Seidl, I.

en:
Resource Title: Input-Tabelle_final_d.xlsx
    Size: 198502 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/3e5310e5-4038-4782-b3c9-b64100709e32/resource/3458c684-6a22-437e-99cf-20c9f494d521/download/input-tabelle_final_d.xlsx
    Description: SSP-CH Input table: qualitative description of 63 socioeconomic factors in the 5 SSP-CH (german). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Programme. The following individuals participated in the compilation of this data: Gubler, L., Tschumi, P., Pütz, M., Seidl, I.

en:
Resource Title: SSP-CH_Faktorprojektionen_d.pdf
    Size: 3030385 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/3e5310e5-4038-4782-b3c9-b64100709e32/resource/11d2b1ab-8a77-407e-9732-db22a853e03f/download/faktorprojektionen_beschreibung_d.pdf
    Description: SSP-CH factor projections of 20 socioeconomic factors (german). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Programme. The following individuals participated in the compilation of this data: Gubler, L., Tschumi, P., Pütz, M., Seidl, I.

format: PDF

media_type: application/pdf

national-ssps

display_name: national-ssps

qualitative-scenarios

display_name: qualitative-scenarios

scenario-development

display_name: scenario-development

socioeconomic-scenarios

display_name: socioeconomic-scenarios

ssp-storylines

display_name: ssp-storylines


en:
This dataset contains the results of greenhouse gas emission modeling for the five Swiss Shared Socioeconomic Pathways (SSP-CH) up to the year 2100. Six sector-specific reduced-complexity models were developed specifically for this analysis, enabling the modelling of the SSP-CH storylines. Accordingly, the dataset comprises the outputs of all six models, generated both in the absence of and under the implementation of additional climate policy packages, referred to as Shared Policy Assumptions (SPAs; see related publications).
- Transport model (energy consumption by energy source and transport type/fleet mix)
- Housing model (heating demand and GHG emissions per building category)
- Electricity model (electricity consumption, electricity generation by energy source, installed capacity, district heating)
- Industry model (energy consumption by energy source, subdivided into raw materials/building materials, consumer goods, and processed goods)
- Agriculture model (feed digestion in livestock, manure storage, and nitrogen fertilization)
- Export/Import model (product quantities in import and export, broken down into consumer goods, raw materials/building materials, and processed goods)
- Synthesis model

en: Results of greenhouse gas modelling of the Swiss Shared Socioeconomic Pathways (SSP-CH)

en: greenhousegas-modelling qualitative-scenarios national-ssp emissions-scenarios socioeconomic-scenarios

en:
Resource Title: SSP-CH_GHG_imp_exp_model.xlsx
    Size: 11240735 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/c80f618c-fd87-4c43-920e-77c8081ccd5a/download/ssp-ch_ghg_imp_exp_model.xlsx
    Description: SSP-CH GHG modelling resluts of Export/Import model (product quantities in import and export, broken down into consumer goods, raw materials/building materials, and processed goods). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_agriculture_model.xlsx
    Size: 624865 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/b8d73680-8159-46ac-98f6-9d97131f5c2e/download/ssp-ch_ghg_agriculture_model.xlsx
    Description: SSP-CH GHG modelling results of Agriculture model (feed digestion in livestock, manure storage, and nitrogen fertilization). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_housing_model.xlsx
    Size: 3051356 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/913c1ae0-418b-4f31-8b51-9ef1e364b3fd/download/ssp-ch_ghg_housing_model.xlsx
    Description: SSP-CH GHG modelling results of Housing model (heating demand and GHG emissions per building category). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_Synthesis_model.xlsx
    Size: 386540 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/a1e54990-11c5-43cf-b922-d839925b4078/download/ssp-ch_ghg_synthesis_model.xlsx
    Description: SSP-CH GHG modelling results of Synthesis model. This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_transport_model.xlsx
    Size: 9070182 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/c147c40f-3431-4847-a954-5a80b6a38cf1/download/ssp-ch_ghg_transport_model.xlsx
    Description: This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_industry_model.xlsx
    Size: 465818 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/3e81b975-33bf-4327-bf18-3ae32f36b21d/download/ssp-ch_ghg_industry_model.xlsx
    Description: SSP-CH GHG modelling results of Industry model (energy consumption by energy source, subdivided into raw materials/building materials, consumer goods, and processed goods). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_electricity_model.xlsx
    Size: 1195377 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/b543e472-b1c0-488c-8a71-ff09be0d140d/download/ssp-ch_ghg_electricity_model.xlsx
    Description: SSP-CH GHG modelling results of Electricity model (electricity consumption, electricity generation by energy source, installed capacity, district heating). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_transport_model.xlsx
    Size: 9070182 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/be82872b-5fd7-4498-a4b9-2141dd5eb00f/download/ssp-ch_ghg_transport_model.xlsx
    Description: SSP-CH GHG modelling results of Transport model (energy consumption by energy source and transport type/fleet mix). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

emissions-scenarios

display_name: emissions-scenarios

greenhousegas-modelling

display_name: greenhousegas-modelling

national-ssp

display_name: national-ssp


en:
Starter kit for the TreeMig framework that allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions.
The starter kit consists of a set of climate, land-use, topography and soil property input data for Switzerland, a script for installing the framework and running the GUI of the framework with the link to the input data, a tutorial covering a range of simulation examples for Switzerland and a transect through Europe, and an associated example simulation environment containing the model executables and other model inputs.

en: Starter Kit for the TreeMig framework v1 for easy simulation of spatio-temporal forest dynamics

en: species-migration bioclimatic-variables gui treemig species-interaction forest-model spatial-modelling forest r-package land-use forest-dynamics

envidat

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

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: TreeMig Simulation Environment and Input Data
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/5eef16d1-80af-442b-9da2-f64b4fa94671/download/tmenvandinputdata.zip
    Description: The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data.  For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders.  For the first example simulation of the tutorial it contains the
   - the TreeMig executables for MacOS, Linux and Windows
   - simulation control files that define the setup and input files of the simulation (in folder C)
   - species parameters (in folder P)
   - bioclimatic data  (in folder E)
   - folder R for results
   - folder Plots for plots of the simulation results.
   - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

en:
Resource Title: TreeMig_Tutorial_v2024_12_04.pdf
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/d4c15251-917c-4102-b172-256fecd39161/download/treemig_tutorial_v2024_12_04.pdf
    Description:

en:
Resource Title: TreeMig Tutorial
    Size: 2046576 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/8e6ac324-ab2b-40bc-a12f-70960c8f4bd1/download/treemig_tutorial.pdf
    Description: Tutorial for using the TreeMig framework

en:
Resource Title: Description input data
    Size: 263110 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/3db8ca57-2838-4b26-82a9-9770b75b7096/download/readme_descriptioninputdata.pdf
    Description: Details where the input data come from and how they have been processed

en:
Resource Title: TreeMig_Tutorial_v2024_12.pdf
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/5188a2e1-470c-4ed7-83c6-26dc72bd18ab/download/treemig_tutorial_v2024_12.pdf
    Description: Tutorial for using the TreeMig framework; updated

en:
Resource Title: TreeMig_Tutorial_2025-05-02.pdf
    Size: 2107648 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/a1dec65a-30ba-4688-90ac-50df5db73d4c/download/treemig_tutorial_2025-05-02.pdf
    Description: Tutorial for using the TreeMig framework; updated

en:
Resource Title: TreeMig Simulation Environment and Input Data
    Size: 1602386904 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/c2ae78fd-418a-4cdb-a511-0914eb46224f/download/tmenvandinputdata.zip
    Description: The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data.  For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders.  For the first example simulation of the tutorial it contains the
   - the TreeMig executables for MacOS, Linux and Windows
   - simulation control files that define the setup and input files of the simulation (in folder C)
   - species parameters (in folder P)
   - bioclimatic data  (in folder E)
   - folder R for results
   - folder Plots for plots of the simulation results.
   - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

bioclimatic-variables

display_name: bioclimatic-variables

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-model

display_name: forest-model

gui

display_name: gui

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-interaction

display_name: species-interaction

species-migration

display_name: species-migration

treemig

display_name: treemig


en:
Spatio-temporal vegetation models are essential for simulating dynamics and shifts
in species distributions over large areas, particularly under global change. One such
model is the dynamic forest landscape model TreeMig. We have developed a frame-
work that makes the model more user-friendly, improves its functionality, and expands
its possibilities. The framework consists of the improved TreeMig model, the R pack-
age ‘TreeMig-R’ for pre-processing input data, model execution and visualization and a
graphical user interface (GUI) for ease of use. The code of the TreeMig Package and the GUI is stored here.

en: Code of TreeMig Package version 1

en: dynamic-modelling r-package gui landscape forest-model

dynamic-modelling

display_name: dynamic-modelling

landscape

display_name: landscape


en:
We used 1463 observations of early leaf discoloration presence or absence in Switzerland's European beech (*Fagus sylvatica L.*) forests between 2018 and 2023 and interpolated Sentinel-2 vegetation index time series from Koch et al. (2024) to train a support vector classifier of early leaf discoloration. We applied this classifier to the European beech dominated forest area of Switzerland over the same time period to estimate a probability of early leaf discoloration between 0 and 1 (high probability of early leaf discoloration). The maps intentionally exclude legacy effects of drought like crown dieback or mortality which occurred in prior years.
Early leaf discoloration maps are available at the end of every summer month (June, July, August, and September) 2017 to 2023. Additional maps are available every 5-days during the summer months of 2018. Probability of early leaf discoloration at each date is provided as a GeoTIFF file with a scaled value range of 0 to 10,000 such that a probability of 0.9 is represented by a value of 9000 (a high probability of early leaf discoloration). An optimized probability threshold of 0.4 (4000) is recommended for classification into discolored and not discolored pixels. More details on the methods and results can be found in the publication of Bloom et al. (2026).
***These maps represent an interpreted remote sensing product that approximates ground observations of early leaf discoloration across Switzerland's European beech dominated forests.***
Given ongoing efforts to operationalize an early discoloration pipeline, all data is currently available upon reasonable request. An example of the 2018 5-day time series in the Hardwald (Canton Basel-Land) is available for immediate download.

en: Early leaf discoloration maps for Switzerland's European beech forests

en: forest drought-2018 drought european-beech sentinel-2 early-warning early-defoliation switzerland

drought

display_name: drought

early-defoliation

display_name: early-defoliation

early-warning

display_name: early-warning

european-beech

display_name: european-beech

switzerland

display_name: switzerland


en:
The TreeMig framework allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions.
Here, case study examples in Switzerland are given that simulate the dynamics and spatial spread of competing tree species in a region in Switzerland, the invasion of a hypothetical invasive tree species, and the control of this species via a management model coupled to TreeMig.  The datasets consist of the installation- and GUI starting-script,  example environmental input data for Switzerland, the simulation environment, and further R-scripts for running the simulations and plotting the simulation results directly from R and for coupling TreeMig to the forest management model.

en: Case study applications demonstrating the use and potential of the TreeMig framework v1

en: treemig forest-management spatial-modelling forest r-package land-use example-applications forest-dynamics climate-change species-migration bioclimatic-variables

en:
Resource Title: installAndRunTreeMigWrapperAndGUI_std.R
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    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/916c8a50-1d57-4e28-97c8-f50b8a9f7bf7/download/installandruntreemigwrapperandgui_std.r
    Description: R-script to install the TreeMig framework and start the graphical user interface

en:
Resource Title: InstructionsForRunningTheSimulations.pdf
    Size: 68995 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/830a45db-7985-42f6-8864-4961fe5ef5a2/download/instructionsforrunningthesimulations.pdf
    Description: A condensed instruction for running the presented simulations.

en:
Resource Title: readme_descriptioninputdata.pdf
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/5abbe67b-ab73-413b-bff8-5cccf5347b1c/download/readme_descriptioninputdata.pdf
    Description:

en:
Resource Title: ExampleApplicationsTreeMig.zip
    Size: 1676301558 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/609e85c8-98d0-4043-be91-48bd32c162ea/download/exampleapplicationstreemig.zip
    Description: 1) Simulation environment TMEnvironmentExampleApplications for the simulation of the case study applications. It contains a) simulation settings (folder C),  b) species parameters (folder P), c) folders S, R and P for the state files and results and plots to be produced, and the binaries of the TreeMig model.
2) Input data for Switzerland in TMInputData (Climate, soil, land cover, DEM)
3) Several R scripts (folder Scripts) to run and plot the simulations directly from R without having to use the graphical user interface.
For learning how to use TreeMig we recommend the starter kit https://www.doi.org/10.16904/envidat.533.

en:
Resource Title: InstructionsForRunningTheSimulations.pdf
    Size: 127302 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/32c74611-c61e-48be-998b-6722bbb697c8/download/instructionsforrunningthesimulations.pdf
    Description: A condensed instruction for running the presented simulations.

en:
Resource Title: ExampleApplicationsTreeMig.zip
    Size: 1686005000 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/0f59997f-3e90-4b25-9e5b-37b641d9769b/download/exampleapplicationstreemig.zip
    Description: 1) Simulation environment TMEnvironmentExampleApplications for the simulation of the case study applications. It contains a) simulation settings (folder C),  b) species parameters (folder P), c) folders S, R and P for the state files and results and plots to be produced, and the binaries of the TreeMig model.
2) Input data for Switzerland in TMInputData (Climate, soil, land cover, DEM)
3) Several R scripts (folder Scripts) to run and plot the simulations directly from R without having to use the graphical user interface.
For learning how to use TreeMig we recommend the starter kit https://www.doi.org/10.16904/envidat.533.

bioclimatic-variables

display_name: bioclimatic-variables

climate-change

display_name: climate-change

example-applications

display_name: example-applications

forest

display_name: forest

forest-management

display_name: forest-management


en:
Regional disturbance predisposition maps for windthrow, bark beetle and snow breakage. The maps are created using spatially explicit data of classified forest structure and site factor parameters. The classified input parameters were weighted according to their effect on windthrow, bark beetle and snow breakage predisposition using an expert-based model. The predisposition raster maps (10 x 10 m) represent the sum of expert-weighted effects on the predisposition and are provided as raw values (sum of the effects on predisposition) and as classified layers.
Classified predisposition maps are encoded as follows:
1: low predisposition (-50% quantile based on raw predisposition values)
2: increased predisposition (50–75% quantile)
3: high predisposition (-75–95% quantile)
4: extreme predisposition (-95% quantile)
Also included are the derived forest structure maps for development stage, dominant stand height, number of stems and canopy cover used for the predisposition mapping, as well as the R scripts and example data to re-create these parameters. Forest structure parameters are based on a data set of individual trees detected using a high-resolution vegetation height model. Further included are ArcPy scripts and example data to re-create the predisposition maps. Running the ArcPy scripts requires a valid ArcGis license including the extensions "SpatialAnalyst", "ImageAnalyst" and "3D".
The example data provided here is assembled from different sources; please check the related publication Bührle et al. (in review) for data sources and further information about the processing, the expert-based model, the forest structure derivation and the predisposition mapping.
Also consider reviewing the related datasets and publications Bast et al. (2025) for more information about canopy layering derivation and individual tree detection (ITD).

en: Regional bark beetle windthrow and snow breakage disturbance predisposition maps

en: development-stage canopy-cover itd als bark-beetle tree-detection stand-height predisposition stem-number predisposition-mapping forest-structure windthrow snow-breakage

envidat

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

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: 01_predispostion_maps.zip
    Size: 19881001 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/59df8818-2e56-4794-9152-3652c20fe6fc/download/01_predispostion_maps.zip
    Description: Classified and raw value predisposition maps for bark beetle, windthrow and snow breakage

en:
Resource Title: 02_python_scripts_predisposition_mapping.zip
    Size: 438323 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/e165fc12-ca86-423f-807a-d7cf1c8535a5/download/02_python_scripts_predisposition_mapping.zip
    Description: ArcPy-Python scripts and demo data to create the bark beetle, windthrow and snow breakage predisposition maps.

en:
Resource Title: 03_rcode_forest_structure.zip
    Size: 103693681 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/42199442-7c55-48ed-a3a7-04aaf9967da0/download/03_rcode_forest_structure.zip
    Description: R-codes and demo data to derive the forest structure parameters: development stage, number of stems, dominant stand height, and canopy coverage based on single trees detected from a vegetation height model. These parameters are necessary input parameters for predisposition mapping.

en:
Resource Title: 04_forest_structure_maps.zip
    Size: 34068234 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/cd162993-bc30-46c1-816b-0722c3d83a50/download/04_forest_structure_map.zip
    Description: Maps of development stage, number of stems, dominant stand height and canopy coverage based on single trees detected from a vegetation height model.

als

display_name: als

bark-beetle

display_name: bark-beetle

canopy-cover

display_name: canopy-cover

development-stage

display_name: development-stage

forest-structure

display_name: forest-structure

itd

display_name: itd

predisposition

display_name: predisposition

predisposition-mapping

display_name: predisposition-mapping

snow-breakage

display_name: snow-breakage

stand-height

display_name: stand-height

stem-number

display_name: stem-number

tree-detection

display_name: tree-detection

windthrow

display_name: windthrow


en:
Phenology, growth, NSC and Amax data used for the following paper:
Early-season warming advances autumn senescence in temperate tree saplings in association with early growth dynamics  published in Plant Cell & Environment 2026.

en: Data for the paper: Early-season warming advances autumn senescence in temperate tree saplings in association with early growth dynamics

en: warming-experiment growth leaf-senescence tree-phenology nsc

en:
Resource Title: ReadMe_Data information.docx
    Size: 17668 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/90ccc75b-bc5e-4968-92a2-38d3452341a8/resource/28146b13-81fd-4625-bc7a-6f55ee39796d/download/readme_data-information.docx
    Description:

en:
Resource Title: Data.zip
    Size: 42598 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/90ccc75b-bc5e-4968-92a2-38d3452341a8/resource/6a336540-7ba5-471e-b452-db20bc540b6a/download/data.zip
    Description: Data used in the paper published in Plant Cell & Environment Vitasse et al. 2026

growth

display_name: growth

leaf-senescence

display_name: leaf-senescence

nsc

display_name: nsc

tree-phenology

display_name: tree-phenology

warming-experiment

display_name: warming-experiment


en: This dataset contains R scripts and pre-processed files to reconstruct the analyses made in the technical note submitted to Ecology Letters as a response to Li & He (2026, https://doi.org/10.1111/ele.70326). In short, I show that the claims made by LH26 (variation in tree growth has increased due to global warming, and that the power exponent between growth variance and mean is b=1.4) are questionable. I show that the reported variance increase in ring widths is an artefact caused by division-based detrending. The reported exponent of 1.4 reflects systematic underestimation, not biological reality of a steep variance-mean scaling. The ecological interpretations built on these foundations cannot be considered reliable.

en: No increased tree growth variability due to global warming

en: tree-rings global-warming estimation-methods climate-change variability

en:
Resource Title: preprocessed data.zip
    Size: 3199750636 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b1e75fa6-f232-45af-a524-d2088ff2757b/resource/3ada643d-214e-4573-a4d6-3c49736f5e3f/download/preprocessed-data.zip
    Description: pre-processed data produced in the accompanying R scripts.

en:
Resource Title: Diagnostic plots.zip
    Size: 279812506 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b1e75fa6-f232-45af-a524-d2088ff2757b/resource/e51b22cb-ff0f-40cb-b302-bfc24e2a3a48/download/diagnostic-plots.zip
    Description: Diagnostic plots presented in SM1.

Outlier_plots_negexp contains the outlier screening where the arithmetic mean at the population levels creates much larger values than the robust mean.

varmax_diagnostic_pages contains the outlier screening for running 5-year variance >5.

en:
Resource Title: R scripts.zip
    Size: 24412 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b1e75fa6-f232-45af-a524-d2088ff2757b/resource/15fb0bd5-ad52-4cf3-ad52-3573063232d2/download/r-scripts.zip
    Description: R scripts to reconstruct analyses made in technical note.

mean_variance_ITRDB_SM1: SM1, calculation of running variances, calculation of outliers, diagnostic plots
power_estimate_clean_simulation: Simulation for SM5
fair_power_comparison_persample: power estimation per 50-year period per sample, SM3
fair_power_comparison: population power estimation per 50-year period, SM3
powertest3.1: Simulation to estimate power given the shape of the ITRDB raw ring-width data, SM4
different power exponent estimations: SM4

climate-change

display_name: climate-change

estimation-methods

display_name: estimation-methods

global-warming

display_name: global-warming

tree-rings

display_name: tree-rings

variability

display_name: variability


en:
Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.
Data are provided on request to contact person against bilateral agreement.

en: Ecological properties of urban ecosystems. Biodiversity dataset of Zurich

en: snails arthropods sdms birds spiders diversity species-richness-models urbanization species-abundance-distributions insects urban-ecology

envidat

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

en: EnviDat: the Environmental Data Portal

en:
Resource Title: Dataset_5_Species_richness_all.txt
    Size: 2453668 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/f3abc8d4-c92e-4a57-aea4-39ba8c93158d/download/dataset_5_species_richness_all.txt
    Description: contains the raw data to perform the species richness models of all species

en:
Resource Title: Dataset_4_Species_richness_rare.txt
    Size: 2275410 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/3bbbc78a-92c3-4832-b51b-61d65346460e/download/dataset_4_species_richness_rare.txt
    Description: contains the raw data to perform the species richness models of the rare species

en:
Resource Title: Dataset_1_SAD_SOD.txt
    Size: 73482 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/a7a96a85-dc46-4b5f-bcf4-4d91485e3d03/download/dataset_1_sad_sod.txt
    Description: abundance and occurrence data of the 1446 species studied, used to investigate species-abundance distributions and species-occurrence distributions

en:
Resource Title: Dataset_3_Species_richness_hyper.txt
    Size: 1373635 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/0ef3b508-b8e7-4404-a7b9-edc51c7f2375/download/dataset_3_species_richness_hyper.txt
    Description: contains the raw data to perform the species richness models of the hypercommon species

en:
Resource Title: Dataset_2_SDM_data.txt
    Size: 65682801 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/0e2e7fb0-88ba-4aa4-92ef-079191b4a668/download/dataset_2_sdm_data.txt
    Description: contains the raw data to perform species distribution models, only for common species

arthropods

display_name: arthropods

birds

display_name: birds

diversity

display_name: diversity

insects

display_name: insects

sdms

display_name: sdms

snails

display_name: snails

species-abundance-distributions

display_name: species-abundance-distributions

species-richness-models

display_name: species-richness-models

spiders

display_name: spiders

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization


en: This repository contains plant community data collected at 180 sites across five types of urban green spaces (allotments, gardens, green areas in residential estates, parks, and ruderal sites) in three Swiss cities (Lugano, Geneva, Zurich). There are 12 sites per UGS type and city (i.e., 60 sites per city). Plant communities were sampled in 100 m2 plots, and coverage was recorded using the extended Braun-Blanquet scale. The data is part of the SNF Synergia project PAPPUS.

en: Data on the plant composition in 5 urban green space types and 3 Swiss Cities (project PAPPUS)

en: urban-plants origin-status human-preferences urban-greenspaces ubran-biodiversity

envi

en: Environment

en:
Resource Title: Non-native plant community
    Size: 183641 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb645df-e8d4-4887-93c4-d91a24afdea5/resource/b0270e86-5580-4b99-8f94-a903178db451/download/community_matrix_nonnative_f.csv
    Description: The dataset contains the non-native plant communities  sampled in the 180 study sites. Rows are sites and columns are plant species. Cells contain the numerical values of the extended Braun-Blanquet coverage classes (e.g., https://doi.org/10.1658/1100-9233(2007)18[413:HRITMO]2.0.CO;2). Native species are considered as those whose natural distribution ranges are outside continental Europe, following (https://doi.org/10.1016/j.dib.2021.107243). They include invasive species and exotics.

Sites IDs are composed by City_UGStype + Values orthogonal gradient + replicate number:

    City: 3 levels, GE = Geneva, ZH= Zurich, LG = Lugano
    UGStype: 5 levels, Al = Allotments, Ga = private gardens, Es = green areas in residential estates, Pa = parks, Ru = ruderal sites.
    Orthogonal gradient: Landscape urbanization class + Local structural diversity class. 4 combinations: HH, HL, LH, LL. H =high, L = Low
    Replicate, numeric. 1-3

format: CSV

media_type: text/csv

en:
Resource Title: Full plant community
    Size: 449744 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb645df-e8d4-4887-93c4-d91a24afdea5/resource/70681117-86df-43b9-abf9-870d027eb8af/download/community_matrix.csv
    Description: The dataset contains the full plant communities (including both native and non-native species) sampled in the 180 study sites. Rows are sites and columns are plant species. Cells contain the numerical values of the extended Braun-Blanquet coverage classes (e.g., https://doi.org/10.1658/1100-9233(2007)18[413:HRITMO]2.0.CO;2).

Sites IDs are composed by City_UGStype + Values orthogonal gradient + replicate number:
- City: 3 levels, GE = Geneva, ZH= Zurich, LG = Lugano
- UGStype: 5 levels, Al = Allotments, Ga = private gardens, Es = green areas in residential estates, Pa = parks, Ru = ruderal sites.
- Orthogonal gradient: Landscape urbanization class + Local structural diversity class.  4 combinations: HH, HL, LH, LL.  H =high, L = Low
- Replicate, numeric. 1-3

en:
Resource Title: Native plant community
    Size: 262862 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb645df-e8d4-4887-93c4-d91a24afdea5/resource/ca98f7fd-5e48-4ffc-b5cd-b8a9b6fd4b15/download/community_matrix_native_f.csv
    Description: The dataset contains the native plant communities  sampled in the 180 study sites. Rows are sites and columns are plant species. Cells contain the numerical values of the extended Braun-Blanquet coverage classes (e.g., https://doi.org/10.1658/1100-9233(2007)18[413:HRITMO]2.0.CO;2). Native species are considered as thosw whose natural distribution ranges are within continental Europe, following (https://doi.org/10.1016/j.dib.2021.107243)

Sites IDs are composed by City_UGStype + Values orthogonal gradient + replicate number:

    City: 3 levels, GE = Geneva, ZH= Zurich, LG = Lugano
    UGStype: 5 levels, Al = Allotments, Ga = private gardens, Es = green areas in residential estates, Pa = parks, Ru = ruderal sites.
    Orthogonal gradient: Landscape urbanization class + Local structural diversity class. 4 combinations: HH, HL, LH, LL. H =high, L = Low
    Replicate, numeric. 1-3

human-preferences

display_name: human-preferences

origin-status

display_name: origin-status

ubran-biodiversity

display_name: ubran-biodiversity

urban-greenspaces

display_name: urban-greenspaces

urban-plants

display_name: urban-plants


en:
Jikradia olitoria Say is a Nearctic leafhopper that was recently identified in several European countries. Given its role as the vector of phytoplasmas associated with Grapevine Yellows diseases (GYD) in its native range, its presence in European vineyard agroecosystems could represent an important phytosanitary risk.
Systematic monitoring conducted over two consecutive years (i.e., 2023 and 2024) using chromotropic traps in seven sites allowed to confirm its widespread presence in the southern Alps, both inside vineyards as well as in the nearby forest edge compartment. Moreover, a subset of the collected specimens was analyzed to test potential infection by phytoplasmas associated with Flavescence dorée.

en: First detailed record of Jikradia olitoria (Hemiptera: Cicadellidae) in the Swiss southern Alps and evaluation of its current potential as a vector of 16SrV-phytoplasmas

en: nearctic-leafhopper neobiota epidemiology flavescence-doree grapevine-yellows

en:
Resource Title: Table2 - Jikradia olitoria hatching events
    Size: 15278 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/7bf9a0ee-2111-4144-8f11-f4f35784b74c/download/table2.docx
    Description: Jikradia olitoria hatching events divided by year and wood type. For the hatching experiment, wood was collected from the Giornico site due to the large J. olitoria adult population observed in 2023.

en:
Resource Title: Supplementary Figure 1
    Size: 13233029 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/0b456ac4-2091-4836-9b36-065b5fa39f55/download/figure_s1.tiff
    Description: Jikradia olitoria captures on yellow sticky traps placed inside the vineyard canopy in the years 2023 and 2024.

format: TIFF

media_type: image/tiff

en:
Resource Title: Supplementary_table3 - Hatching events
    Size: 15852 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/588d922d-1acd-4eab-9693-c76d4cf7d9a6/download/supplementary_table3.xlsx
    Description: Hatching events in 2024 and 2025.

en:
Resource Title: Table 3 - Insect vectors of 'Candidatus Phytoplasma vitis' associated to grapevine’s Flavescence dorée in Europe
    Size: 21135 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/7b134d20-6f86-41d1-bca1-3610fdf02b07/download/table-3.docx
    Description: Insect vectors of 'Candidatus Phytoplasma vitis' associated to grapevine’s Flavescence dorée in Europe. Insect species categorized according to their vector competence, i.e., main vector: primary epidemic grapevine-to-grapevine vector; alternative vector: FD phytoplasma acquisition and transmission confirmed in laboratory and supported by data collected in the field and usually involving other plant species; putative vector: only FDp and/or FDp-like genotypes acquisition confirmed, no indication of ability to transmit FDp to receipient plant. Associated plant species involved in alternative FDp cycle do not represent the complete and known range of host plant species for the listed insect species. Only major and epidemic map-type genotypes are listed. For further details on map-type gene and grouping (i.e., FD1, FD2, and FD3) and vectotype related gene VmpA, refer to Malembic-Maher et al. (2020).

en:
Resource Title: Supplementary_table2 - Trap captures 2024
    Size: 165550 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/bba02f83-22d2-48d3-8d60-39e03be1cfb6/download/supplementary_table2.xlsx
    Description: Trap captures in 2024.

en:
Resource Title: Supplementary_table1 - Trap captures 2023
    Size: 144783 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/8c89de75-a018-44c1-b913-346b1dbc5356/download/supplementary_table1.xlsx
    Description: Trap captures in 2023.

en:
Resource Title: Table1 - Jikradia olitoria total captures
    Size: 21647 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/05806ade-fd08-4bfa-b2bb-a0991dec3377/download/table1.docx
    Description: Jikradia olitoria total captures divided by plot and trap type, mean captures per trap and ratio between the two sampling years. YST/plot = yellow sticky traps installed in each plot, L = landscape YST, V = vineyard YST, N YST = overall number of processed YST throughout the experiment.

en:
Resource Title: Supplementary Figure 2
    Size: 13233029 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/41f7fb05-1fac-4ef1-adc6-7362059d972d/download/figure_s2.tiff
    Description: Jikradia olitoria captures on yellow sticky traps placed in the landscape around the vineyards in the years 2023 and 2024.

epidemiology

display_name: epidemiology

flavescence-doree

display_name: flavescence-doree

grapevine-yellows

display_name: grapevine-yellows

nearctic-leafhopper

display_name: nearctic-leafhopper

neobiota

display_name: neobiota


en:
This dataset supports the analysis presented in "From point clouds to forest management: Quantifying the sensitivity of a decision support framework to initialization data using close-range remote sensing" (https://doi.org/10.1016/j.ecoinf.2026.103896). The repository covers two parts of the analysis: (1) data preparation from LiDAR-based forest inventories, and (2) sensitivity analysis of decision support system (DSS) outputs. The intermediate forest simulation (ForClim v4.1) and multi-criteria decision analysis (MCDA) were carried out using an external pipeline described in Blattert et al. (2024), using the R package rforclim (v1.01.001) by Hiltner & Marano (2025). The corresponding model outputs are included here as inputs to the sensitivity analysis, making all analyses in this repository fully reproducible without requiring access to the simulation pipeline. The ForClim model (v4.1) (Marano et al., 2025) is openly available from the ETH Zurich Institute of Terrestrial Ecosystems (https://ites-fe.ethz.ch/openaccess/products/forclim). Climate data were sourced from the publicly available Swiss CH2018 dataset (CH2018 Project Team, 2018). Soil data were sourced from Baltensweiler et al. (2024). For a detailed description of the repository structure, scripts, and data files, see the README file included in this dataset.
 References
Blattert C, Mutterer S, Thrippleton T, Diaci J, Fidej G, Bont LG, Schweier J (2024). Managing European Alpine forests with close-to-nature forestry to improve climate change mitigation and multifunctionality. Ecological Indicators, 165, 112154. https://doi.org/10.1016/j.ecolind.2024.112154
Baltensweiler A, Walthert L, Hanewinkel M, Zimmermann S, Nussbaum M (2024). Soil property maps for the Swiss forest. EnviDat. https://doi.org/10.16904/envidat.484
CH2018 Project Team (2018). CH2018 - Climate Scenarios for Switzerland. National Centre for Climate Services, Zurich. https://www.nccs.admin.ch/nccs/en/home/climate-change-and-impacts/swiss-climate-change-scenarios/ch2018---climate-scenarios-for-switzerland.html
Marano, G., Hiltner, U., Meusburger, K., Hands, T., & Bugmann, H. (2025). Predicting drought-induced tree mortality in Swiss beech forests hinges upon predisposing and inciting factors (p. 2025.12.15.694299). bioRxiv. https://doi.org/10.64898/2025.12.15.694299
Hiltner U, Marano G (2025). rforclim: Collection of useful functions to parameterize and run the forest model ForClim. R package version 1.01.001. Available on request from the ETH Zurich Institute of Terrestrial Ecosystems.

en: lidar uncertainty forest-model forest-inventory sensitivity-analysis ecosystem-services decision-support

envidat

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

en: EnviDat: the Environmental Data Portal

en:
Resource Title: README.md
    Size: 4149 bytes
    Format: text/markdown
    URL: https://www.envidat.ch/dataset/6b3838ed-8433-492d-8424-2a19ae134b0a/resource/eaa7ed36-52f8-47fc-827c-a54a71292a4a/download/readme.md
    Description:

format: markdown

en:
Resource Title: dss_sensitivity_analysis_envidat.zip
    Size: 20615843021 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.784%2F
    Description:

en:
Resource Title: README.html
    Size: 23631 bytes
    Format: text/html
    URL: https://www.envidat.ch/dataset/6b3838ed-8433-492d-8424-2a19ae134b0a/resource/b331ae91-9d28-4262-9edd-9efeb9f2fc12/download/readme.html
    Description:

format: HTML

media_type: text/html

decision-support

display_name: decision-support

ecosystem-services

display_name: ecosystem-services

forest-inventory

display_name: forest-inventory

forest-model

display_name: forest-model

lidar

display_name: lidar

sensitivity-analysis

display_name: sensitivity-analysis

uncertainty

display_name: uncertainty


en:
Rising temperatures are thawing mountain permafrost, putting infrastructure like buildings, cable cars, and tunnels at risk. While warming air is the main cause, rainfall at high altitudes also plays an important role.
We built a small, portable system to test how active cooling powered by solar panels, can help keep the ground frozen. Experiments showed that rain can sometimes deepen thawing, especially in winter, but in summer under the impact of liquid precipitation it can also briefly refreeze parts of the ground or lead to ground movement.
Outdoor tests over six months showed that a solar-powered cooling system can successfully maintain a stable frozen layer. The results suggest that such systems could help protect infrastructure in mountain regions, though managing rainfall effects is crucial.
The results are described in the publication: Preserving frozen ground with active cooling under seasonal forcing - an experimental study.

en: precipitation thermal-stabilisation climate-change permafrost swiss-alps solar-energy prototype

envi

en: Environment

climate-change

display_name: climate-change

permafrost

display_name: permafrost

precipitation

display_name: precipitation

prototype

display_name: prototype

solar-energy

display_name: solar-energy

swiss-alps

display_name: swiss-alps

thermal-stabilisation

display_name: thermal-stabilisation


en: This dataset contains the responses of an online expert survey on municipal land-use planning regulations governing buildings and open and green spaces across 30 European countries. Using purposive sampling, 73 planning experts – practitioners and academics – rated the priority they assign to thirteen groups of regulations (seven for buildings, six for open and green spaces) and assessed to which extent does each regulation group pursues the objectives of efficiency equity. The survey was administered online between August and December 2023. The dataset is the quantitative companion to the expert-interview dataset on discretion in land-use planning (doi:10.16904/envidat.785).

en: European survey of planning experts on land-use planning regulations

en: development-priorities efficiency green-spaces building-regulation equity

en:
Resource Title: regulations_survey_data.xlsx
    Size: 27351 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/3676fed8-bc78-46c4-96c7-cea0b89c5630/resource/6a0473de-2866-406a-b233-ece90429c965/download/regulations_survey_data.xlsx
    Description: The survey data in Excel format. 73 respondents from 30 European countries, 45 columns: respondent background, priority ratings of regulation groups and their efficiency and equity ratings. Identical content to the csv file.

en:
Resource Title: regulations_survey_data.csv
    Size: 14643 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3676fed8-bc78-46c4-96c7-cea0b89c5630/resource/95cb0a11-6693-4e5b-b1aa-9065b0912c36/download/regulations_survey_data.csv
    Description: The survey data in plain CSV format. 73 respondents from 30 European countries, 45 columns. Identical content to the xlsx file.

en:
Resource Title: Data_Documentation.pdf
    Size: 212081 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/3676fed8-bc78-46c4-96c7-cea0b89c5630/resource/e75c2dc0-7329-4fcb-9b9a-820b496370d0/download/data_documentation.pdf
    Description: Full documentation of the dataset. Describes the survey design, sampling, the 45 variables, regulation group definitions, license and recommended citation. Read this first.

en:
Resource Title: regulations_survey_questionnaire.pdf
    Size: 171694 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/3676fed8-bc78-46c4-96c7-cea0b89c5630/resource/3c99f535-70e6-4586-ae43-84fcd3ad934e/download/regulations_survey_questionnaire.pdf
    Description: The survey questionnaire as administered. Only the questions whose answers appear in the published data file are included.

building-regulation

display_name: building-regulation

development-priorities

display_name: development-priorities

efficiency

display_name: efficiency

equity

display_name: equity

green-spaces

display_name: green-spaces


en: This dataset comprises the interview guides and the corresponding verbatim transcripts of 16 in-depth, semi-structured expert interviews examining how discretion is understood and exercised in land-use planning, across both plan-making and development control. The three jurisdictions — England (5 interviews), Ireland (5) and Switzerland (6) — were selected for their contrasting approaches to pursuing efficiency and equity through planning regulation, as identified in the companion survey of planning experts (doi:10.16904/envidat.735). The interviews were conducted with planning practitioners and academics across two rounds, structured by a six-question first guide and an eight-question second guide. All interviews were digitally recorded, transcribed verbatim, and anonymised prior to deposit.

en: Interviews with experts in Switzerland, England and Ireland on planners' discretion and the pursuit of efficiency and equity objectives

en: land-use-planning planning-culture plan-making land-use-regulations development-control

en:
Resource Title: Transcript_ENG1.docx
    Size: 38543 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/4084679c-7b68-498f-b37b-e1adab1c33bf/download/transcript_eng1.docx
    Description: Anonymised transcript of expert interview ENG1 (England, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_CHE6.docx
    Size: 37868 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/544f2aae-45e9-4614-89ec-3b9be1641c94/download/transcript_che6.docx
    Description: Anonymised transcript of expert interview CHE6 (Switzerland, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_ENG5.docx
    Size: 41923 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/4149ed2d-63be-4f83-a7be-49a61bcc1ffd/download/transcript_eng5.docx
    Description: Anonymised transcript of expert interview ENG5 (England, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_CHE5.docx
    Size: 47698 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/d804f9ea-74c3-49d4-890d-a22d35eba18e/download/transcript_che5.docx
    Description: Anonymised transcript of expert interview CHE5 (Switzerland, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Interview_guide.pdf
    Size: 119081 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/6e8bea59-8dc5-4348-8b63-0267942ab8ae/download/interview_guide.pdf
    Description: The semi-structured interview guides for both rounds. Round 1 used questions Q1-6. Round 2 used questions Q1-8. Question numbers match those referenced in the transcripts.

en:
Resource Title: Transcript_CHE4.docx
    Size: 40376 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/2710ea43-aca9-4e13-ad82-6ba2d02c2a57/download/transcript_che4.docx
    Description: Anonymised transcript of expert interview CHE4 (Switzerland, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Data_Documentation.pdf
    Size: 99389 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/40211321-bf12-41e4-9593-34f37ee833fa/download/data_documentation.pdf
    Description: Full documentation of the dataset. Describes the study design, participant recruitment, the two interview rounds, the anonymisation procedure, known limitations, license and recommended citation. Read this first.

en:
Resource Title: Transcript_ENG4.docx
    Size: 50074 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/f2ab12bb-67d6-4bee-91df-083b5d390dd5/download/transcript_eng4.docx
    Description: Anonymised transcript of expert interview ENG4 (England, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

en:
Resource Title: participant_index.csv
    Size: 826 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/3e865c5f-fc90-4d82-ad8c-1226ecd5bac0/download/participant_index.csv
    Description: Index of the 16 interviews. One row per participant with anonymised code, country, interview round, question set used and the matching transcript file name.

en:
Resource Title: Transcript_IRL3.docx
    Size: 45640 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/ac5ba07c-b953-4925-9367-ed05e110b84a/download/transcript_irl3.docx
    Description: Anonymised transcript of expert interview IRL3 (Ireland, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_CHE2.docx
    Size: 41585 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/b574de10-10df-42fc-ae7e-1877e905e368/download/transcript_che2.docx
    Description: Anonymised transcript of expert interview CHE2 (Switzerland, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_IRL5.docx
    Size: 42680 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/4958f672-49fc-4f07-9614-d219a894cf28/download/transcript_irl5.docx
    Description: Anonymised transcript of expert interview IRL5 (Ireland, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_IRL1.docx
    Size: 37571 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/929065d7-1414-4df5-b5e2-4b00c5ad2606/download/transcript_irl1.docx
    Description: Anonymised transcript of expert interview IRL1 (Ireland, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_CHE1.docx
    Size: 40940 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/d18f3bc6-c4a3-49b8-94a1-e5f86bbbff9b/download/transcript_che1.docx
    Description: Anonymised transcript of expert interview CHE1 (Switzerland, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_ENG3.docx
    Size: 41134 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/7b4b111f-6754-482a-a4bd-f0bdc21a3cf3/download/transcript_eng3.docx
    Description: Anonymised transcript of expert interview ENG3 (England, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_IRL4.docx
    Size: 43674 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/93ceb19e-503e-4db1-b97e-9f5afcefb64a/download/transcript_irl4.docx
    Description: Anonymised transcript of expert interview IRL4 (Ireland, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_CHE3.docx
    Size: 38523 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/9a629acc-5185-41c3-be04-6bdeaca79815/download/transcript_che3.docx
    Description: Anonymised transcript of expert interview CHE3 (Switzerland, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_ENG2.docx
    Size: 41820 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/388aff06-33d4-48dc-ae18-6f7001b68ac7/download/transcript_eng2.docx
    Description: Anonymised transcript of expert interview ENG2 (England, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
Resource Title: Transcript_IRL2.docx
    Size: 40776 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/94968b66-d9c7-4bf8-8f06-e0da0073a1a5/download/transcript_irl2.docx
    Description: Anonymised transcript of expert interview IRL2 (Ireland, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

development-control

display_name: development-control

land-use-planning

display_name: land-use-planning

land-use-regulations

display_name: land-use-regulations

plan-making

display_name: plan-making

planning-culture

display_name: planning-culture


en: This dataset contains idealized sensitivity simulations performed with the SNOWPACK model to investigate how the timing of summer heat affects ground thermal conditions in alpine permafrost. The simulations demonstrate that late-summer heatwaves lead to greater heat retention in the ground until the following spring than equally warm early-summer events. The dataset supports analyses of the influence of atmospheric heat timing on active-layer heat storage and permafrost thermal evolution.

en: The importance of hot summers and individual heatwaves for permafrost in the Swiss Alps

en: climate-change hot-summer seasonal-variability subseasonal-variability atmospheric-heatwaves mountain-permafrost

envidat

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

en: EnviDat: the Environmental Data Portal

envi

en: Environment

atmospheric-heatwaves

display_name: atmospheric-heatwaves

climate-change

display_name: climate-change

hot-summer

display_name: hot-summer

mountain-permafrost

display_name: mountain-permafrost

seasonal-variability

display_name: seasonal-variability

subseasonal-variability

display_name: subseasonal-variability


en: This repository presents the basis data of both the Energyscape 1 (2018) and the Energyscape 2 (2022) survey about renewable energy infrastructure related landscape preferences in Switzerland. Both  datasets are based on Swiss-national representatative online panel surveys (BILENDI). Information about the choice design and landscape visualizations can be derived from https://www.sciencedirect.com/science/article/pii/S2352340921003097fig0001

en: Energyscape 2 - Comparing stated preferences of renewable energy landscapes in Switzerland in 2018 and 2022

en: landscape-perception discrete-choice energy-transition renewable-energy survey

en:
Resource Title: Energyscape2 basis data with original Respondent ID
    Size: 70331 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e3451afd-f9d8-41ce-a7dc-d3f9fad95e67/resource/4ad511f8-f29a-4b71-bd2c-8b2e9e541ee6/download/energyscape2_n1006_basis_ce1_swth_sel.csv
    Description: This is solely the Energyscape 2 basis data including the original respondents ID "sys_RespNum" to be merged with the covariated data set.

en:
Resource Title: Energyscape2 Covariates
    Size: 12192 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e3451afd-f9d8-41ce-a7dc-d3f9fad95e67/resource/9d6902f1-784f-4a0a-be6a-aa800b430beb/download/energyscape2_n1006_basis_cov_items_recoded.csv
    Description: This data includes the recoded covariates used in the publication (Cov_1, Cov_2, Cov_3). They can be merged with the column "sys_RespNum" to the basis dataset of the Energyscape 2 survey.

en:
Resource Title: Energyscape1 & Energyscape2 basis data
    Size: 136853 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e3451afd-f9d8-41ce-a7dc-d3f9fad95e67/resource/9081e5b8-6f16-4873-84f3-f8ab6d030cf2/download/energyscape12_basis_ce1_swth_sel.csv
    Description: This is the already cleaned and ready to use basis dataset for the comparison of stated choice preferences across the 2018 (Energyscape 1, n=844) and the 2022 (Energyscape 2, n=1006) surveys. The first four numbers in the first column "sys_RespNum" of the dataset refer to the year of the surveys, followed by the original Respondent ID. The date can be uploaded as is to analysis software (like Sawtooth) and can be filtered by survey with the help of the very last column "surveytime".

discrete-choice

display_name: discrete-choice

energy-transition

display_name: energy-transition

landscape-perception

display_name: landscape-perception

renewable-energy

display_name: renewable-energy

survey

display_name: survey


en:
This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).
The dataset was used for the paper
Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science 30: 1122–1133.
The excel file contains 5 sheets:
1) Plot information
2) Bryophyte data with species specific information, separated per substrate
3) Macrolichen data with species specific information, separated per substrate
4) Bryophyte data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 2 and 4 might therefore differ slightly.
5) Macrolichen data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 3 and 5 might therefore differ slightly.

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

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

biodiversity

display_name: biodiversity

bryophyte

display_name: bryophyte

bryophyte-life-strategy

display_name: bryophyte-life-strategy

climate-change-impact

display_name: climate-change-impact

disturbance

display_name: disturbance

elevational-gradient

display_name: elevational-gradient

endemic

display_name: endemic

endemic-species

display_name: endemic-species

functional-taxonomic-group

display_name: functional-taxonomic-group

functional-taxonomic-groups

display_name: functional-taxonomic-groups

lichen

display_name: lichen

lichen-growth-form

display_name: lichen-growth-form

lichen-growthform

display_name: lichen-growthform

liverwort

display_name: liverwort

madeira-island

display_name: madeira-island

moss

display_name: moss

photobiont

display_name: photobiont


en:
The data presented here corresponds to the publication "Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain" (Gerber et al., 2018a), which investigates the precipitation variability of snow precipitation in the central northern part of the Grisons (CH) and the publication "The importance of near-surface winter precipitation processes in complex alpine terrain" (Gerber et al., 2018b). The dataset contains:
* WRFsimulations: WRF simulation output for simulations with 4x (14x) terrain smoothing with an output timestep of 30 min/5 min and horizontal grid spacings of 1350 m, 450 m, 150 m and 50 m (currently: data available upon request).
* StationData: Meteorological station data of 18 meteorological stations in the central northern part of the Grisons with 30 minute resolution for the period 1 January 2016 till 1 May 2016.
* ADS80data: Photogrammetrically determined snow depth distribution data over the Dischma valley for the 26 January 2016 and 9 March 2016. Snow heights are corrected for buildings, vegetation (- 1m), outliers, and pixles, which are obivously snow-free on the pictures (Bühler et al., 2015). In addition the snow depth differences (snow depth on 9 March 2016 minus snow depth on 26 January 2016) are provided.
For more details about the simulation and observation data, see Gerber et al., 2018 and Gerber and Sharma (2018).
Publications:
Bühler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and Ginzler, C.: Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9, 229–243, doi:10.5194/tc-9-229-2015, 2015.
Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137–3160, doi:10.5194/tc-12-3137-2018, 2018.
Gerber, F., Mott, R. and Lehning, M.: The importance of near-surface winter precipitation processes in complex alpine terrain, Journal of Hydrometeorology, accepted, 2018.
Gerber, F., and Sharma, V.:  Running COSMO-WRF on very-high resolution over complex terrain. Laboratory of Cryospheric Sciences CRYOS, École Polytechnique Fédérale de Lausanne EPFL, Lausanne, Switzerland. doi:10.16904/envidat.35, 2018.

en: DISCHMEX - High-resolution WRF simulations in complex alpine terrain and station measurements

en: orographic-precipitation snow-precipitation precipitation-variability cosmo-wrf complex-terrain

en:
Resource Title: StationData
    Size: 1161085 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/68536bc6-0990-41c0-a06f-21b19c0f2ffe/resource/096c4c10-1d39-428b-9762-e6c769553cbd/download/stationdata.zip
    Description: Meteorological station data of 18 meteorological stations in the central northern part of the Grisons with 30 minute resolution for the period 1 January 2016 till 1 May 2016.

en:
Resource Title: ADS80data
    Size: 88940889 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/68536bc6-0990-41c0-a06f-21b19c0f2ffe/resource/dd0a21fc-e609-4a79-ab10-58e7dd9953d4/download/ads80data.zip
    Description: Snow depth maps over Dischma valley for 26 January 2016 and 9 March 2016 and snow depth differences between the two dates.

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

orographic-precipitation

display_name: orographic-precipitation

precipitation-variability

display_name: precipitation-variability

snow-precipitation

display_name: snow-precipitation