bats-in-beaver-systems

en:
This dataset contains processed bat acoustic data collected to evaluate the ecological impacts of beaver-engineered ponds on bat species richness, activity, and feeding activity. It includes nightly activity and feeding call counts identified to species or species group level, site and treatment information (beaver pond vs. control areas along the same stream), and associated environmental covariates such as deadwood volume, arthropod abundance, and vegetation structure.

Methods: Bats were recorded using passive acoustic detectors placed at paired beaver-influenced and control sites. The resulting data was processed in the software Batscope. Arthropod abundance was estimated using flight-interception traps. The amount of deadwood and vegetation cover was measured in the field. Canopy cover and canopy heterogeneity were modelled from GIS models.

Temporal and Spatial Coverage:
Data were collected from eight sites in Switzerland between June and July 2022, each site sampled for 16 nights.

en: beaver castor chiroptera deadwood ecosystem-engineer flying-arthropods habitat-heterogeneity nature-conservation wetland

en: Bats in beaver systems

title_for_slug: bats-in-beaver-systems

en: README text file providing context for the data. Please do not hesitate to contact us if you have any questions

en:
For the time information about the research data, created (2022-09-23 to 2022-09-23) refers to the GIS data, collected (2022-06-13 to 2022-07-31) to the field sampling. Details can be found in the csv “sampling_dates”.

sampling_dates with columns
"site" (sampling site),
"bats_1" (first period of bat survey in June),
"bats_2" (second period of bat survey in June),
"bats_3" (first period of bat survey in July),
"bats_4" (second period of bat survey in July),
"flying_arthropods_1" (arthropod sampling in June),
"flying_arthropods_2" (arthropod sampling in July),
"deadwood survey" (date of deadwood survey),
"vegetation survey" (date of vegetation survey)

en: sampling dates.xlsx

en:
vegetation_characteristics data on vegetation_cover and cumulative_cover with columns "laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"Vegetation_cover" (estimated vegetation cover over 5x1 m plot in %),
"Cumulative cover" (summed up cover over tree, shrub and herb layer in %),
"Cumulative cover trees" (tree cover level, higher than 3 metres in %),

en:
gewichtungsfaktor with columns
"Project" (sampling site),
"Treatment" (sampling location within site, with levels ctrl for Control and pool for Pool),
"date" (sampling date),
"gew" (weighting factor to standardize night length)

en:
dead_wood for deadwood with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"type" (deadwood type),
"height_length" (the height or lenghth of a piece of deadwood in m),
"DBH_diamlarge" (diameter at breast height or diameter of larger end if lying in m),
"diamsmall (diameter of smaller end if lying in m",
"volume" (calculated volume in m3)

en:
bats_biodiv.day.guilds with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"guild" (which feeding guild information),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en:
bats_biodiv.day with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en:
bats.environment with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"canopy" (modelled tree canopy cover, averaged across radius of 100 m, unitless),
"structure_ruggedness" (mean ruggedness of the total vertical structure > 3 m, averaged across radius of 100 m, unitless), "vegetation_ruggedness" (mean ruggedness of the canopy > 3 m, later called canopy_heterogenneity, averaged across radius of 100 m, unitless),
"pool_size" (maximum width of beaver pool in m),
"area_deadwood_monitoring" (total area of deadwood moonitoring in m3),
"age" (beaver presence in years)

en:
bat_guilds with columns
"SP" (identification level),
"EndGuilde" (information on feeding guild)

en:
arthropods_flying the arthropod abundance with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"month" (sampling month),
"abundance" (number of flying arthropods)

en:
site_info contains columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"location" (sampling location row within site),
"sample_nr" (sampling number within location)

en:
Raw data data_bats with columns:
“Categories” (empty or "feeding", indicating that call sequence contains feeding buzz)
“Sequence” (sequence number)
“Timestamp” (recording time)
“Temperature” (temperature recorded by batlogger in degree celcius)
“Serial” (serial number of bat logger)
“Duration” (duration of call sequence in seconds)
“Call.Count” (call count number, how many calls could be detected)
“Verification.1” (bat sequence verification)
“Notes” (remarks for identification)
“treatment” (treatment ctrl = Control and Pool)
“slot” (slot number, 4 slots, two in June, two in July)
“Laufnummer” (unique identifier per sampling point)

en: R script bats_age to test the beaver pool age effect on bat richness, activity and feeding activity

en: R script bats_data_cleaning to clean the raw data

en: R script bats_data_exploration_Figure_nighly to explore the data and display nightly species richness, activity and feeding activity

en: R script bats_glmm to test the influence of beavers on bat richness, activity and feeding activity

en: R script bats_pool_size to test the beaver pool size on bat richness, activity and feeding activity

en: bats_raw_values_overview to produce raw value summaries and tables

en: R script bats_SEM to test the influence of the beaver on bat richness, activity and feeding activity through feeding and habitat heterogeneity

beaver

display_name: beaver

castor

display_name: castor

chiroptera

display_name: chiroptera

deadwood

display_name: deadwood

ecosystem-engineer

display_name: ecosystem-engineer

flying-arthropods

display_name: flying-arthropods

habitat-heterogeneity

display_name: habitat-heterogeneity

nature-conservation

display_name: nature-conservation

wetland

display_name: wetland


en:
This dataset contains the population evolution of a pest and its biocontrol agent in terms of presence proportion at gall level and absolute number of insects.

The study area extends from the Cuneo region (Piedmont, Italy) to southern Switzerland. In order to provide a complete range of data covering the entire process from the pest arrival to complete biological control by its natural enemy T. sinensis, a space-for-time substitution approach has been adopted so as to create a temporal gradient of the epidemic stages over the whole study area.

The southernmost Swiss sites roughly represent the arrival and establishment of the pest without the presence of the natural enemy, the central ones the early epidemic stage and the epidemic peak, whereas the northern ones the end of the epidemic with the beginning of the biocontrol. The Italian ones represent the beginning of the equilibrium between the two population as well as the situation with stable T. sinensis populations on the long term.

These data are used in the paper entitled: Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions

en: biological-control castanea-sativa dryocosmus-kuriphilus pest-invasion population-evolution southern-switzerland torymus-sinensis

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

type: dataset

key: guid

key: uri

value: Envidat Harvester

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

fr: Éducation, culture et sport

it: Istruzione, cultura e sport

en:
Data has been collected from summer 2011 to 2016.

Main reasons for NA values:

-	D. kuriphilus still not arrived in the site.
-	No galls were found during the field sampling.
-	Site has not been sampled in the correspondent year because of time, weather or money constrains.
-    T. sinensis not found in the galls

Variable description:

Year = sampling year.

Coordinate, elevation, slope, aspect = average site characteristics.

ts_arrival = T. sinensis arrival year.

dk_arrival = D. kuriphilus arrival year.

dk_parasitism_by_ts = D. kuriphilus parasitism rate by T. sinensis. Formula: (no. of T. sinensis/ no. of D. kuriphilus chambers) * 100 at gall level.

dk_infestation = Percentage of attacked buds. Formula: no. of attacked buds / no. of total developed buds*100 (as presented in Kotobuki et al. 1985) at shoot level.

ts_per_bud = number of alive T. sinensis individual calculated at bud level.

dk_per_bud = number of alive D. kuriphilus individual calculated at bud level.

dk_pot_per_bud = overall number (dead and alive) D. kuriphilus individual calculated at bud level.

total_gall_nr_per_bud = average gall number per bud.

chamber_per_galls = average D. kuriphilus chamber number per gall.

For details see the submitted publication with the same name

References
Kotobuki, K., Mori, K. and Sato, Y. (1985) ‘2 methods to estimate the tree damage by chestnut gall wasp Dryocosmus-kuriphilus’, Bulletin of the fruit tree research station A (Yatabe), 2(12), pp. 29–36.

format: CSV

media_type: text/csv

biological-control

display_name: biological-control

castanea-sativa

display_name: castanea-sativa

dryocosmus-kuriphilus

display_name: dryocosmus-kuriphilus

pest-invasion

display_name: pest-invasion

population-evolution

display_name: population-evolution

southern-switzerland

display_name: southern-switzerland

torymus-sinensis

display_name: torymus-sinensis


en:
This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.
Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0° Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels.
As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.

en: climate-change-impacts hydrology snow snowfall water-cycle

en: Snow and the water cycle in a changing climate

en: Synthesis report chapter

format: PDF

media_type: application/pdf

en: DORA Persistent URL

format: HTML

media_type: text/html

climate-change-impacts

display_name: climate-change-impacts

hydrology

display_name: hydrology

snow

display_name: snow

snowfall

display_name: snowfall

water-cycle

display_name: water-cycle


en:
 Important
This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry "Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.." The entry can be found under this link <https://www.envidat.ch/#/metadata/meteo-stillberg> and with this DOI <https://doi.org/10.16904/envidat.396>.

en: atmospheric-conditions precipitation radiation temperature-profiles wind

en: Long-term meteorological and snow station at 2090 m a.s.l., Stillberg, Davos, Switzerland (1975 - present)

en: This data access is outdated and has been replaced with an updated version (see description).

en: DATA ACCESS

format: SERVICE

en: This description is outdated and has been replaced with an updated version (see description).

en: README

format: TXT

media_type: text/plain

en: This overview plot is outdated and has been replaced with an updated version (see description).

en: Overview plot

format: PNG

media_type: image/png

en: This site photo is outdated and has been replaced with an updated version (see description).

en: Site photo 1

en: Site photo 2

en: Site photo 3

en: This dataset is outdated and has been replaced with an updated version (see description).

en: Latest data from Stillberg climate station

en: STB2 CSV

atmospheric-conditions

display_name: atmospheric-conditions

precipitation

display_name: precipitation

radiation

display_name: radiation

temperature-profiles

display_name: temperature-profiles

wind

display_name: wind


en:
This repository contains data required for reproducibility of the results to be published in the associated manuscript.

Apart from reproducibility, the attached datasets also serve as templates for new users to adopt CRYOWRF in their research.

The datasets consist of two folders organized in zip format:

1. REPRODUCIBILITY_SIMULATION:
  Consists of namelists for WPS, WRF and SNOWPACK to reproduce simulations published in the manuscript
  Additional files include datasets (from IMAU-FDM / RACMO, see "credits" below ) as well as helper python scripts to
  produce *.sno files
  which are used as initial conditions for SNOWPACK in CRYOWRF.

2. REPRODUCIBILITY_POSTPROCESSING:
   Includes outputs of CRYOWRF and python scripts used to prepare figures in the manuscript.

Each of the folders have their own readme files for more details.

 Code
citation: Varun Sharma. (2021, July 2). vsharma-next/CRYOWRF: CRYOWRF v1.0 (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.5060165

location: https://gitlabext.wsl.ch/atmospheric-models/CRYOWRF   (stable releases / institutional repo)
        https://github.com/vsharma-next/CRYOWRF            (dev branches / developer repo)

 Publication
**Introducing CRYOWRF v1.0: Multiscale atmospheric flow simulations with advanced snow cover modelling.**
Varun Sharma, Fraziska Gerber and Michael Lehning, Submitted to Geoscientific Model Development

 Acknowledgements
We thank Peter Kuipers Munneke (P.KuipersMunneke@uu.nl) for preparing and sharing outputs of IMAU-FDM and RACMO used for initial conditions for case Ia. The relevant citations for the methods through which these datasets were generated are:

* Kuipers Munneke, P., S. R. M. Ligtenberg, B. P. Y. Noël, I. M. Howat, J. E. Box, E. Mosley-Thompson, J. R. McConnell, K. Steffen, J. T. Harper, S. B. Das and M. R. van den Broeke. 2015. Elevation change of the Greenland ice sheet due to surface mass balance and firn processes, 1960-2014. The Cryosphere, 9, 2009-2025. doi:10.5194/tc-9-2009-2015

 * Ligtenberg, S. R. M., P. Kuipers Munneke, B. P. Y. Noël, and M. R. van den Broeke. 2018. Brief communication: Improved simulation of the present-day Greenland firn layer (1960-2016). The Cryosphere, 12, doi:10.5194/tc-12-1643-2018

en: alps antarctica blowing-snow mesoscale-modelling snow-atmosphere-interaction

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: Reproducibility Dataset for CRYOWRF v1.0

educ

en: Education, culture and sport

en: REPRODUCIBILITY_SIMULATION.zip contains all the extraneous files required to perform the simulations in associated manuscript (see Citations 3 in README.txt). The purpose of sharing these files apart from purposes of reproducibility is to also serve as templates for users to learn the workflow of CRYOWRF and perform simulations for their own purposes.

format: ZIP

media_type: application/zip

en:
REPRODUCIBILITY_POSTPROCESSING.zip contains data and scripts used to produce all figures from for the associated manuscript submitted for peer review ( see section on Citations below )

The folders stand for the three case studies presented in the manuscript. Two case studies (case Ia and case Ib) are on simulations in Antarctica and one case study
case II is for simulations in the Alps.

The associated data in the form of netcdf files or text files were produced using CRYOWRF v1.0

Apart from reproducibility, the intention of sharing these scripts is also to make it easy for new users to adopt CRYOWRF and be efficient, in particular in processing snowpack outputs.

The scripts were tested on multiple machines (different OSes) with the attached YAML file (wrf_analysis.yml) to setup the conda environment. All scripts were run using spyder as the IDE.

alps

display_name: alps

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

mesoscale-modelling

display_name: mesoscale-modelling

snow-atmosphere-interaction

display_name: snow-atmosphere-interaction


en:
This dataset contains the fire events, drivers and auxillery datasets analyzed in the study "Compounding preconditions of wildfires vary in time and space within Europe" of Miller et al. (2025) (DOI: 10.1038/s43247-025-02955-1).
A Jupyter-Notebook explaining the fire event datasets and code to reproduce the figures of Miller et al. (2025) are available on gitlab (see https://gitlab.com/jumi26/wildfire-drivers-in-europe).

en: driver europe event-analysis forest-fire mountains observations

en: Wildfire events and drivers across Europe

en:
01_fire_events_and_drivers: contains the fire event metrics files and in the grid subdirectory drivers and burned area information
02_utest_results: contains the results for different u-test experiments, i. e. fire vs. no-fire for (a) entire region (*both*-files), (b) fire vs. no-fire for mountain regions (*1*-files), (c) fire vs. no-fire for no-mountain regions (*0*-files) and (d) mountain vs. non-mountain on fire days (*mr_nonmr_new*-files)
03_random_forest_results: contains for each region (first two letters) and season (second three letters), the variable importance and accuracy metrics.
04_timelags_datasets: contains seasonal averages for the timelags of fire drivers on fire days and on non-fire days.
figures: contains figures of the publication, which can be derived from the code available in the git-repository https://gitlab.com/jumi26/wildfire-drivers-in-europe.git
mountain_regions_mask: contains file to reproduce mountain regions

driver

display_name: driver

europe

display_name: europe

event-analysis

display_name: event-analysis

forest-fire

display_name: forest-fire

mountains

display_name: mountains

observations

display_name: observations


forest-canopy-layer

en: This dataset provides a LiDAR-based classification of the forest canopy layer for selected Swiss cantons. Combining an area-based and tree-centred approach, forest areas are categorised into four canopy layering classes that differentiate between single-layered and multi-layered canopies.

en: forest-canopy forest-management forest-structure lidar mountain-forest nfi

en: Forest Canopy Layer

title_for_slug: forest-canopy-layer

en: Forest Canopy Layer (class 1: single-layered; class 2: probably single-layered; class 3: probably multi-layered; class 4: multi-layered).

format: TIFF

media_type: image/tiff

en: QGIS QML file with stored layer style definitions for the ForestCanopyLayer.tif.

format: OCTET

en: Validation data from the Independent Forester Assessment (IFA). x- /y-coordinate (CH1903+ LV03) and layer (1: single-layered; 2: multi-layered; -999: NA).

forest-canopy

display_name: forest-canopy

forest-management

display_name: forest-management

forest-structure

display_name: forest-structure

lidar

display_name: lidar

mountain-forest

display_name: mountain-forest

nfi

display_name: nfi


forest-canopy-layer-code

en:
R script for large-scale mapping of forest canopy layering. The approach combines tree-based and area-based methods, using airborne laser scanning point clouds together with individual tree detection (ITD) information (tree height and position) to distinguish between single- and multi-layered forests. The output is a raster layer at 10m x 10m resolution, encoded as follows:
1: single-layered (class 1): Both point cloud and ITD approaches classify the forest as single-layered.
2: probably single-layered (class 2): ITD classifies the forest as single-layered, while the point cloud suggests multi-layered.
3: probably multi-layered (class 3): ITD classifies the forest as multi-layered, while the point cloud suggests single-layered.
4: multi-layered (class 4): Both point cloud and ITD approaches classify the forest as multi-layered.
Next to the R script, we also provide an example data set. The LAZ files in the example dataset are from the nationwide ALS data and are provided by the Federal Office of Topography swisstopo in accordance with its open data policy (swisstopo, 2022a, 2019). The detected individual trees were identified using Dalponte and Coomes' (2016) algorithm on a spike-free vegetation height model (cell size 0.5m). The provided forest mask corresponds to the NFI forest layer definition by Waser et al. (2015). For details and mentioned references, we refer to the related publication by Bast et al. (under revision; JAG).

en: forest forest-management itd lidar mountain-forest protective-forest vhm

en: Forest Canopy Layer Code

title_for_slug: forest-canopy-layer-code

forest

display_name: forest

itd

display_name: itd

protective-forest

display_name: protective-forest

vhm

display_name: vhm


distributed-acoustic-sensing-brienz

en:
This dataset contains the Distributed Acoustic Sensing (DAS), radar detection data used for training and result analysis in the GRL paper titled `Automatic Monitoring of Rock-Slope Failures Using Distributed Acoustic Sensing and Semi-Supervised Learning`.

The DAS dataset (both waveform and cross-spectral density matrices), extracted features, labeled dataset, two trained models (feature extraction model and xgboost classification model), scripts to reproduce the whole training and classification processes, and a notebook to replicate the result analysis part are provided under the MIT license. To provide a reasonable data size, we chunked the raw data to a few hundred channels which we used in our project.

Abstract:
Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi-supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million cubic meters on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground-truth labeling. The proposed algorithm is capable of distinguishing between rock-slope failures and background noise, including road and train traffic, with a detection precision of over 95%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal-to-noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

en: early-warning landslide machine-learning natural-hazard rockfall seismic

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: Distributed Acoustic Sensing Brienz

title_for_slug: distributed-acoustic-sensing-brienz

en: Data access

early-warning

display_name: early-warning

landslide

display_name: landslide

machine-learning

display_name: machine-learning

natural-hazard

display_name: natural-hazard

rockfall

display_name: rockfall

seismic

display_name: seismic


en:
The cold habitats of the Swiss Alps and the Arctic are undergoing change and are at risk of disappearing completely or partially in the future. Along with them, a poorly known diversity of microorganisms that have adapted to life in these supposedly hostile places. By cryopreserving microorganisms from these habitats, some of today's biodiversity can be preserved in biobanks.
The collection contains cryopreserved bacteria and fungi isolated from permafrost and active layer of Muot da Barba Peider and Val Lavirun, from the glacier forefield and glacier toe of the Damma glacier, from active layer in northern Greenland and from plastic waste in Svalbard, as well as isolates from plastic incubations in Muot da Barba Peider, northern Greenland and arctic Russian soils.

Project overview: https://www.wsl.ch/en/projects/life-at-chilly-temperatures-a-collection-of-microorganisms-from-extreme-habitats/

en: active-layer arctic bacteria biobank biodiversity collections conservation extreme-habitat fungi glaciers permafrost plastisphere swiss-alps

en: Life at chilly temperatures - A collection of microorganisms from extreme habitats

educ

en: Education, culture and sport

en: Complete collection of bacterial and fungal isolates from Permafrost, active layer and glaciers in Switzerland, Svalbard, northern Greenland and Russia.

format: XLS

active-layer

display_name: active-layer

arctic

display_name: arctic

bacteria

display_name: bacteria

biobank

display_name: biobank

biodiversity

display_name: biodiversity

collections

display_name: collections

conservation

display_name: conservation

extreme-habitat

display_name: extreme-habitat

fungi

display_name: fungi

glaciers

display_name: glaciers

permafrost

display_name: permafrost

plastisphere

display_name: plastisphere

swiss-alps

display_name: swiss-alps


en: This dataset consists of data related to capillary rise experiments performed with neutron radiography. There are 4 videos of capillary rise experiments as well as the files used to perform the inverse fitting with Hydrus. The videos show the upward flow of water in glass columns filled with sand and snow or sand, gravel, and snow. The videos show the 2D evolution of the unitless optical density with time. The Hydrus files were used to fit the parameter values of the Mualem-van Genuchten model. The experiments were performed at the Paul Scherrer Institute (PSI) in Villigen, Switzerland.

en: measurements pore-water snow soil transport water

en: Capillary rise rise experiments in snow using neutron radiography

en: Videos of the capillary rise experiments

en: Description of the videos

en: The Hydrus files used for the inverse fitting of the hydraulic conductivity.

en: Descriptions of the nomenclature, Hydrus files, and videos.

measurements

display_name: measurements

pore-water

display_name: pore-water

snow

display_name: snow

soil

display_name: soil

transport

display_name: transport

water

display_name: water


en: The dataset contains measured values of soil liquid water content, matric potential, and soil texture of 40 soil samples in Davos, Switzerland. The measurements were used for determining van Genuchten parameter values through fitting of water retention curves, pedotransfer functions, and inverse fitting with Hydrus-1D.

en: alpine glide-snow-avalanche lwc matric-potential measurements soil soil-classification soil-texture

en: Soil measurements of Seewer Berg and Davos

en: Detailed information of each file in the dataset

en: Coordinates of each soil location, location of the snow profiles, and location of the snow liquid water content measurements

en: Hydrus files used for the inverse fitting

en: Daily averaged values used for the direct fitting

en: Texture data of the soil locations

en: Complete list of the van Genuchten parameter values for each soil and method

alpine

display_name: alpine

glide-snow-avalanche

display_name: glide-snow-avalanche

lwc

display_name: lwc

matric-potential

display_name: matric-potential

soil-classification

display_name: soil-classification

soil-texture

display_name: soil-texture


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

envidat

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

en: EnviDat: the Environmental Data Portal

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

educ

en: Education, culture and sport

en: A condensed instruction for running the presented simulations.

en:
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: Where the environmental and land use input data come from and how they were processed

en: R-script to install the TreeMig framework and start the graphical user interface

bioclimatic-variables

display_name: bioclimatic-variables

climate-change

display_name: climate-change

example-applications

display_name: example-applications

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-management

display_name: forest-management

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-migration

display_name: species-migration

treemig

display_name: treemig


c-n-p-release-treeline

en:
1. Chemical characteristics of litter layer sampled along elevation gradient across treeline ecotones in the Khibiny mountains (birch) and South Urals (spruce)
2. Release of C-N-P during decomposition at 5°C and 15° studied in an incubation experiment with microlysimeters
3. Data on microbial biomass C-N-P, potential activity of C-N-P requiring enzymes in the litter layer
4. Mineralization of 13C labeled glucose-6 phosphate, microbial 13C uptake, and net P immobilization of released P to identify microbial processing of labile organic P

en: biogeochemical-cycling carbon-cycle climate-change enzymatic-activities litter-decomposition microbial-biomass nutrient-fluxes treeline-ecosystems

en: C-N-P-Release Treeline

title_for_slug: c-n-p-release-treeline

en: Litter layer characteristics, C-N-P-release during decomposition, microbial C-N-P, enzyme activity, mineralization of 13C-labeled glucose-6-phosphate from samples collected across two treelines

biogeochemical-cycling

display_name: biogeochemical-cycling

carbon-cycle

display_name: carbon-cycle

enzymatic-activities

display_name: enzymatic-activities

litter-decomposition

display_name: litter-decomposition

microbial-biomass

display_name: microbial-biomass

nutrient-fluxes

display_name: nutrient-fluxes

treeline-ecosystems

display_name: treeline-ecosystems


en:
The data supports the paper "Local and landscape factors shape alpha and beta trophic interaction diversity in urban gardens". It provides:
(1) the host-natural enemy interaction networks and network metrics between cavity-nesting bees and wasps and their natural enemies,
 (2) the explanatory variables reflecting urban intensity, temperature, habitat amount and management intensity.
(3) the questionnaires for assessing management intensity.
(4) a thesaurus for the variable names.
(5) a readme file.
(6) the R script used for the analyses.
The data is part of the BetterGardens project. It was sampled in 85 allotment and home gardens in the ctiy of Zurich in 2015 using standardized trapnests.

en: bees cavity-nesting food-webs host-enemy-interactions hymenoptera management-intensity trap-nest urban-ecology urban-ecosystems urbanization wasps zurich

en: Data on interaction diversity in urban gardens

en: Not used: data of the study

en: Variable names and their description.

en: Thesaurus

en: Host-enemy interaction data, used to compute the interaction diversity metrics

en: Raw data nests

en: Read me file

en: Not used: readme

en: Cleaned script to reproduce the analyses

en: Script for the analyses v1.0

en: Not used: raw questionnaires

en: Results from the management intensity questionnaires anonymised. IDs are randomized and don't match those in the study data csv

en: Questionnaires management intensity anonymised

en: The csv. file contains the response variable "interaction diversity" (idiv), and the values of the predictors used in the paper. The "id" column indicates the garden ID, each row in the dataset correspond to a garden and each column to the response (idiv), an explanatory variable or a descriptor (name, Lat, Long). See the thesaurus and the readme file for extended information on the variables.

en: Data of the study

en: Script for the analyses v2.0

bees

display_name: bees

cavity-nesting

display_name: cavity-nesting

food-webs

display_name: food-webs

host-enemy-interactions

display_name: host-enemy-interactions

hymenoptera

display_name: hymenoptera

management-intensity

display_name: management-intensity

trap-nest

display_name: trap-nest

urban-ecology

display_name: urban-ecology

urban-ecosystems

display_name: urban-ecosystems

urbanization

display_name: urbanization

wasps

display_name: wasps


chelsacerra-daily

en:
CHELSA is a mechanistic downscaling model that links large-scale atmospheric conditions with local topographic factors to produce very high-resolution climate data. It includes commonly used climate variables for impact modeling, such as air temperature, precipitation, humidity, solar radiation, wind speed, and derived variables.
CHELSAcerra daily is a high resolution climate dataset for air-temperatures generated with the CHELSA downscaling model using the Copernicus regional reanalysis for Europe (CERRA).
Daily summaries are provided for: Daily Mean Near-Surface Air Temperature

en: air-temperature climate europe maximum-temperature minimum-temperature reanalysis

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: CHELSAcerra-daily

title_for_slug: chelsacerra-daily

educ

en: Education, culture and sport

en: Daily Mean Near-Surface Air Temperature

en: Daily Mean Near-Surface Air Temperature 1985

en: Daily Mean Near-Surface Air Temperature 1986

en: Daily Mean Near-Surface Air Temperature 1987

en: Daily Mean Near-Surface Air Temperature 1988

en: Daily Mean Near-Surface Air Temperature 1989

en: Daily Mean Near-Surface Air Temperature 1990

en: Daily Mean Near-Surface Air Temperature 1991

en: Daily Mean Near-Surface Air Temperature 1992

en: Daily Mean Near-Surface Air Temperature 1993

en: Daily Mean Near-Surface Air Temperature 1994

en: Daily Mean Near-Surface Air Temperature 1995

en: Daily Mean Near-Surface Air Temperature 1996

en: Daily Mean Near-Surface Air Temperature 1997

en: Daily Mean Near-Surface Air Temperature 1998

en: Daily Mean Near-Surface Air Temperature 1999

en: Daily Mean Near-Surface Air Temperature 2000

en: Daily Mean Near-Surface Air Temperature 2001

en: Daily Mean Near-Surface Air Temperature 2002

en: Daily Mean Near-Surface Air Temperature 2003

en: Daily Mean Near-Surface Air Temperature 2004

en: Daily Mean Near-Surface Air Temperature 2005

en: Daily Mean Near-Surface Air Temperature 2006

en: Daily Mean Near-Surface Air Temperature 2007

en: Daily Mean Near-Surface Air Temperature 2008

en: Daily Mean Near-Surface Air Temperature 2009

en: Daily Mean Near-Surface Air Temperature 2010

en: Daily Mean Near-Surface Air Temperature 2011

en: Daily Mean Near-Surface Air Temperature 2012

en: Daily Mean Near-Surface Air Temperature 2013

en: Daily Mean Near-Surface Air Temperature 2014

en: Daily Mean Near-Surface Air Temperature 2015


en: This research data is used to re includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions. This research data includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions.

en: habitat-heterogeneity hengduan-mountains hypervolume model-residuals phytodiversity

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: Phytodiversity is associated with habitat heterogeneity from Eurasia to Hengduan

educ

en: Education, culture and sport

en: This is the plant range map at the Hengduan mountain scale based on species distribution model, elevation, and county level distribution information

en: Plant range map at Hengduan scale

en: This is the family level plant richness map in the Hengduan mountain scale at 1km resoution

en: Hengduan plant family richness map

en: This is the plant family level richness map at the Eurasia scale calculated from https://www.nature.com/articles/s41597-021-00997-6

en: Eurasia plant family richness map

en: This table is used for the Eurasia scale analyses, including both family level species richness map and different heterogeneity predictors

en: Eurasia SR and heteorgeneity

en: The Heterogeneity matrics in the Hengduan mountain scale

en: HDM heterogeneity scales

habitat-heterogeneity

display_name: habitat-heterogeneity

hengduan-mountains

display_name: hengduan-mountains

hypervolume

display_name: hypervolume

model-residuals

display_name: model-residuals

phytodiversity

display_name: phytodiversity


en:
List of key debris flow variables from the WSL Illgraben monitoring station (2023) such as occurrence date and time, peak flow depth, peak flow velocity, total volume and bulk density.
This table contains values based on our current analysis methods. The list will be updated annually after each debris flow season, and as our methods continue to improve, individual values may change slightly in the future.

en: debris-flow force-plate illgraben magnitude monitoring-station

en: Illgraben debris-flow characteristics 2023

debris-flow

display_name: debris-flow

force-plate

display_name: force-plate

illgraben

display_name: illgraben

magnitude

display_name: magnitude

monitoring-station

display_name: monitoring-station


climarctic-seasons

en: This dataset includes measurements of potential microbial functions (potential extracellular enzyme activities and microbial functional genes abundance) and soil physico-chemical properties (pH, nutrients, texture, etc) in biocrusts and underlying mineral soils in High-Arctic tundra near Ny-Ålesund (Svalbard) during each season along a toposequence.

en: arctic greenhouse-gases microbial-biodiversity microbial-functions soil

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: Climarctic seasons

title_for_slug: climarctic-seasons

en:
Samples description
•	sample number: sample list number (S1 to S126)
•	season: season during which the samples were collected
•	location: one of the two location where the samples were collected in the vicinity of Ny Alesund on Svalbard
•	slope site: sites where the samples were collected along the toposequence (upslope = top of the slope, midslope = middle of the slope, downslope = bottom of the slope)
•	soil layer: depth layer where the sample were collected (biocrust = 0-2 cm and mineral soil = 5-10 cm)
•	replicate: replicated sampled (3 replicates per site)
Environmental variables
•	pH = pH value of the soil
•	sand = proportion of sand of the soil [%]
•	silt = proportion of silt of the soil [%]
•	clay = proportion of clay of the soil [%]
•	GWC = Gravimetric Water content in the soil [%]
•	OM = Organic Matter content in the soil [g]
•	N = Total Nitrogen content in soil [mg/g]
•	C = Total Carbon content in soil [mg/g]
•	C/N = ration of C content over N content [ratio]
•	NO3 = Nitrate content in soil [mg/kg]
•	NO2 = Nitrite content in soil [mg/kg]
•	NH4 = Ammonium content in soil [mg/kg]
•	Ptot = Total Phosphorus content in soil [mg/kg]
•	Pavail = Available Phosphorus content in soil [mg/kg]
•	PO4 = Phsphate content in soil [mg/kg]
•	CO2 = CO2 fluxes from soil [mmol CO2 m-2 d-1]
•	CH4 = CH4 fluxes from soil [umol CH4 m-2 d-1]
•	N2O = N2O fluxes from soil [umol CH4 m-2 d-1]
•	GPP = Gross Primary Production [mmol CO2 m-2 d-1]
•	ER = Ecosystem respiration [mmol CO2 m-2 d-1]
•	NEE = Net Ecosystem Exchange [mmol CO2 m-2 d-1]
•	glucoOM = glucosidase enzyme activity [mmol h-1 gOM-1]
•	23 xyloOM = xylosidase enzyme activity [mmol h-1 gOM-1]
•	chitOM = N-acetyl-glucosaminidase (chitinase) enzyme activity [mmol h-1 gOM-1]
•	phosOM = phosphatase enzyme activity [mmol h-1 gOM-1]
•	leuOM = leucine-aminopeptidase enzyme activity [mmol h-1 gOM-1]
•	PPOM = Polyphenol Peroxidase enzyme activity [mmol h-1 gOM-1]
•	POOM = Polyphenol Oxidase enzyme activity [mmol h-1 gOM-1]
•	30. Bac16SOM = Bacterial 16S gene abundance [copies gOM-1]
•	31. BacamoAOM = Bacterial amoA gene abundance [copies gOM-1]
•	32. ArchamoAOM = Archaeal 16S gene abundance [copies gOM-1]
•	33. nifHOM = nifH gene abundance [copies gOM-1]
•	34. nirSOM = nirS gene abundance [copies gOM-1]
•	35. nosZOM = nosZ gene abundance [copies gOM-1]
•	36. nxrBOM = nxrB gene abundance [copies gOM-1]
•	37. mcrAOM = mcrA gene abundance [copies gOM-1]
•	38. pmoAOM = pmoA gene abundance [copies gOM-1]

en: Climarctic seasons dataset Envidat.xlsx

arctic

display_name: arctic

greenhouse-gases

display_name: greenhouse-gases

microbial-biodiversity

display_name: microbial-biodiversity

microbial-functions

display_name: microbial-functions

soil

display_name: soil


en:
The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.

Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height > 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0.
The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.html#links). High vegetation is also derived from the classification and the calculated object height from the point cloud.
Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.

An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.

The work is published in:

en: avalanches davos dischma photogrammetry snow-depth-distribution snow-hydrology

envidat

en: EnviDat: the Environmental Data Portal

en: Snow depth mapping by airplane photogrammetry (2017 -  ongoing)

educ

en: Education, culture and sport

en: Snowdepth maps

format: GeoTIFF

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology


en: LiDAR and PD radar data as well as camera videos of the 30 June 2022 debris flow at the Illgraben (Switzerland).

en: 3d-lidar analysis debris-flow illgraben pulse-doppler radar

en: Dataset for "Sorting and Surging 3D LiDAR and Pulse-Doppler Radar Analysis of a Natural Debris Flow"

analysis

display_name: analysis

pulse-doppler

display_name: pulse-doppler

radar

display_name: radar


en:
This repository hosts the experimental data accompanying our publication, “On the compressive strength of weak snow layers of depth hoar,” featured in the Journal of Glaciology.

-Compressive Strength Data:
Measurements of the compressive strength for 92 artificially grown weak snow samples.
-Microstructural CT Data:
CT-derived microstructural information for each sample, including:
Density
Specific surface area
Connectivity density
Correlation lengths
Anisotropy
-Additional CT Data:
Parent Sample Variability: CT data used to assess the variability of the parent samples.
Temporal Evolution: CT data capturing the evolution within the artificial weak layers.
Reference Data: Information on the reference CT data sourced from the RHOSSA and MOSAiC campaigns.

en: depth-hoar microstructure slab-avalanche snow snow-mechanics strength

en:
CT Reference Data: Contains sample names, positions, evaluated regions and microstructural parameters based on data from the RHOSSA and MOSAiC campaigns.
MOSAiC:
https://mosaic-portal.org/data/open-mcs/vessel/polarstern/sn_microct/sn_microct/exdata/
https://doi.org/10.1594/PANGAEA.952794
RHOSSA:
https://doi.org/10.16904/envidat.151

en: Experimental Data: Contains results of the compression experiments and microstructural parameters.

en: Parent sample variability: Contains the CT data used to estimate the parent sample variability.

en: Time series data: Contains the CT data of upper slab, weak layer and lower slab used to evaluate the temporal evolution within the artificial samples.

depth-hoar

display_name: depth-hoar

microstructure

display_name: microstructure

slab-avalanche

display_name: slab-avalanche

snow

display_name: snow

snow-mechanics

display_name: snow-mechanics

strength

display_name: strength


en: These datasets and code support research on the community assemblage of aquatic macroinvertebrates in ephemeral microhabitats, specifically water-filled tree holes (WTH), in three biogeographically and climatically distinct old-growth forests: Temperate-Mediterranean (France), Neotropical (Brazil) and Palaeotropical (India). By integrating standardised biological sampling, detailed environmental measurements and spatial analysis, the dataset allows for an in-depth examination of how local habitat conditions and spatial configuration influence taxonomic and functional beta-diversity patterns. The data capture the variation in species composition and ecological traits in 35 WTH per forest site and are particularly important for understanding how environmental filtering and spatial processes shape beta-diversity in natural forest ecosystems.

en: beta-diversity community-assembly environmental-factors functional-diversity metacommunity spatial-factors taxonomic-diversity water-filled-tree-holes

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: Spatial factors shape taxonomic and functional beta-diversity in water-filled tree holes in different biogeographical regions

en: This Excel file contains metadata that provides detailed information about all other files included here. It describes the ten accompanying datasets, outlining the type of data each contains, the variables used, their descriptions, and the units of measurement (where applicable).

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the Brazilian forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the French forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the Indian forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This dataset contains values of taxonomic and functional β-diversity for each study site, along with their respective components: species richness difference vs. turnover (taxonomic), and functional richness difference vs. functional turnover (functional). Used to generate Fig. 1.

en: This dataset includes physical and environmental characteristics of water-filled tree holes (WTHs), such as water depth, height, opening size, hole origin and orientation, circumference at breast height, WTH type, and water volume. It also contains physico-chemical parameters, nutrient concentrations (ammonium, nitrogen, phosphate, sulfate, chloride), CDOM, Chlorophyll-A, turbidity, and dry organic matter (FPOM, CPOM, total).

en: This dataset describes the animal community composition sampled from 35 water-filled tree holes (WTHs) per site. It includes abundance and taxonomic identification (to morphospecies level) of aquatic macroinvertebrates and Chordata.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the Brazilian forest plot.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the French forest plot.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the Indian forest plot.

en: This dataset includes results from variation partitioning analysis used to assess the relative contributions of environmental and spatial factors to taxonomic and functional β-diversity and their components across biogeographic regions. It contains site-level values for pure and shared effects of predictors. Used to generate Fig_2.

en: Code to do the analysis and figures of the paper.

beta-diversity

display_name: beta-diversity

community-assembly

display_name: community-assembly

environmental-factors

display_name: environmental-factors

functional-diversity

display_name: functional-diversity

metacommunity

display_name: metacommunity

spatial-factors

display_name: spatial-factors

taxonomic-diversity

display_name: taxonomic-diversity

water-filled-tree-holes

display_name: water-filled-tree-holes


individual-detected-trees

en: This dataset provides individual detected trees for selected Swiss Cantons (Appenzell Outer-Rhodes - AR, Grisons - GR, Nidwald - NW, Obwald - OW and St. Gall - SG), including the derived diameter at breast height and the derived NFI development stage. Data show the Individual detected trees (ITD) with the x- and y-positions of each detected tree based on a spike-free vegetation height model based on nationwide LiDAR data. The shapefile contains the following attributes: tree height [m] (Height), estimated diameter at breast height [cm] (BHD), and the NFI development stage (Ent; 1: young growth/thicket (< 12 cm); 2: pole timber (12-30 cm); 3: young timber (31-40 cm); 4: medium timber (41-50 cm); 5: old timber (> 50 cm).

en: development-stage forest-management forest-structure itd mountain-forest nfi

envidat

en: EnviDat: the Environmental Data Portal

en: Individual Detected Trees

title_for_slug: individual-detected-trees

educ

en: Education, culture and sport

development-stage

display_name: development-stage

forest-management

display_name: forest-management

forest-structure

display_name: forest-structure

itd

display_name: itd

mountain-forest

display_name: mountain-forest

nfi

display_name: nfi


en:
This repository hosts the experimental data accompanying our publication "The influence of snow microstructure on the compressive mechanical properties of weak snowpack layers" feautured in Acta Materialia.
- Experimental Data and microstructural descriptors:
Full dataset used for the main analysis of the paper. Contains the measured mechanical properties, µCT parameters, further analysis, etc. The column "ID" (e.g. 363) links this data with the 3D µCT dataset (e.g. e0000363).
- 3D Data of the segmented µCT scans:
Full dataset containing the raw segmented µCT scans in the MetaIO format. They can be opened using the SimpleITK package (`pip install SimpleITK`).
Note: the Files can be idenfied by their scan number in the filename, (e.g, e0000363)
 `import SimpleITK as sitk `
 `img = sitk.ReadImage("e0000363_crop_seg.mhd") `
 `array = sitk.GetArrayFromImage(img) ` shape: [z, y, x]
`array_xyz = np.transpose(array, (2, 1, 0))` (Optional) for array axes in [x, y, z]
 `spacing = img.GetSpacing() `         voxel size (x,y,z)

en: lab-experiment snow snow-avalanche snow-mechanics snow-microstructure

envidat

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

en: Dataset containing both the experimental results and microstructural parameters.

lab-experiment

display_name: lab-experiment

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-microstructure

display_name: snow-microstructure


en: Countrywide vegetation height models (VHM) were generated for Armenia based on Copernicus Sentinel-2 imagery within the framework of the FORACCA project. “The Forest Restoration and Climate Change in Armenia” (FORACCA) project is funded by the Swiss Agency for Development and Cooperation (SDC). The project is implemented by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL) in collaboration with Zoï Environment Network as well as the Forest Alliance of Armenia. A Convolutional Neural Network (CNN) model was trained in Switzerland to estimate the maximum vegetation height at the spatial resolution of the Sentinel-2 pixel of 10 m. Vegetation heights from the spatially higher-resolved VHM Lidar NFI were used as reference data for the CNN training. Then, the model was spatially transferred and VHMs for Armenia were generated annually based on available Sentinel-2 imagery from May – September of the respective year. Further details on the dataset and model to create the Sentinel-2 VHMs can be found in the paper Jiang et al. (2023, https://doi.org/10.1016/j.srs.2023.100099). Contains modified Copernicus Sentinel data.

en: armenia cnn foracca forest remote-sensing sentinel-2 vegetation-height

en: Sentinel-2 Vegetation Height Model Armenia

armenia

display_name: armenia

cnn

display_name: cnn

foracca

display_name: foracca

forest

display_name: forest

remote-sensing

display_name: remote-sensing

vegetation-height

display_name: vegetation-height


en:
This dataset contains several useful components for **Swiss Electricity system modeling**:
1. **High-voltage Swiss grid**: 2019 snapshot of the high-voltage (>150kV) grid composed of :
- Nodes: names, voltage, position.
- Lines: ends nodes, resistance, power capacity.
- Transformers: ends nodes, power capacity, resistance, tap ratio.

2. **Hydropower topology** : Representative description of Swiss reservoirs and plants (2019) composed of:
- Plants: Lower reservoir, Upper reservoir, pump/turbine, debit, power capacity.
- Reservoirs: Volume, altitude, position, canton.

3. **Inflows** into all the reservoirs: hourly time series post-processed from PREVAH model outputs from 2009 to 2019.

4. **Wind production per node**: COSMO-1 data processed and scaled to match SFOE annual production.

5. **Solar irradiance maps projected onto tilted surfaces**: hourly time series for 35° and 65° tilts, azimuth 180°. (same method as [Frischholz, Y. (2025)](https://www.doi.org/10.16904/envidat.632))

6. **Processed ERA5 (historical), ECMWF SEAS5 (seasonal forecast) and ECMWF TIGGE (short-term forecast)**:
- ERA5 Land: For inflow forecast model: historical runoff, snow depth, temperature and total precipitation per catchment (hourly, 2009-2019). For residual demand forecast model: historical temperature, u-v wind, ssrd per country (CH, AT, DE, FR, IT, hourly 2015-2019)
- ECMWF SEAS5: For inflow forecast model: same variables as ERA5 (yearly forecast starting from every 1st day of every month in 2019). For residual demand forecast model: same variables as ERA5 (yearly forecast starting from every 1st day of every month in 2019).
- ECMWF TIGGE: For inflow forecast model: same variables as ERA5 (two weeks forecast starting from every 1st day of every week in 2019). For residual demand forecast model: same variables as ERA5 (two weeks forecast starting from every 1st day of every week in 2019).

\
More details can be found on *README.txt*.

en: electricity-grid forecast-models hydropower meteorological-data renewable-energy reservoir-inflow solar-irradiance

en: Swiss electricity system modeling -- HV grid - hydropower topology and inflows - renewable productions - past and forecast meteorological data

en: Plants and reservoirs description, with associated hourly inflows (2009 - 2019).

en: Solar irradiance maps projected onto tilted surfaces: hourly time series for 35° and 65° tilts, azimuth 180°. (same method as [Frischholz, Y. (2025)](https://www.doi.org/10.16904/envidat.632))

en: Description of Swissgrid high-voltage (>150kV) grid, including lines, transformers and nodes (snapshot of the grid in 2019) .

en: ERA5 Land, Seasonal forecast and short-term forecast meteorological data to train inflow and residual demand forecast models.

en: Data structure and description.

electricity-grid

display_name: electricity-grid

forecast-models

display_name: forecast-models

hydropower

display_name: hydropower

meteorological-data

display_name: meteorological-data

renewable-energy

display_name: renewable-energy

reservoir-inflow

display_name: reservoir-inflow

solar-irradiance

display_name: solar-irradiance


en:
This repository contains data related to an interdisciplinary study on playgrounds, looking at both playground vegetation and the well-being of pre-school children using those playgrounds. This project was funded by and initiated at the Institute for Landscape Planning and Ecology (ILPÖ) of the University of Stuttgart, Germany. Data was collected by ILPÖ. The project was finalised in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). The aim of the study was to (1) describe playground vegetation (species richness and diversity at the ground, shrub and tree layers; habitat structure); (2) describe preschool children’s well-being and nature-connectedness; and (3) investigate whether there is a relationship between plant diversity and well-being while exploring potential other predictors of well-being, including children’s and parents’ or carer’s nature-connectedness, greenspace exposure, seasonality, and socio-demographic background.
We sampled ground (<1m height), shrub (1-2m height) and tree (>2m hieght) vegetation of 29 playgrounds of Stuttgart's inner city. Sampling took place between May 25 and July 01, 2021. Ground layer vegetation was sampled in ten one-by-one m2 quadrats per playground, within which we recorded the cover of each species based on the Braun-Blanquet scale. Woody vegetation was exhaustively sampled within the playgrounds and in a surrounding two meters buffer, recording each individual plant. Vegetation cover was visually estimated as a percentage for each vegetation layer, namely the ground, shrub and tree (canopy) layers.
In parallel, we distributed questionnaires to parents and carers to assess the well-being and connectedness-to-nature of preschool children (aged 6 or younger) across the seasons. The questionnaire covered children's (1) use of greenspaces, (2) nature-connectedness, (3) well-being, and (4) socio-demographic variables. Questionnaires were distributed accross all seasons: sampling seasons included autumn (Oct-Nov 2021), winter (Jan-Feb 2022), spring (April-May 2022) and summer (June-July 2022). Ethical approval was granted by the University of Stuttgart Commission on Responsibility in Research (Az. 21-033).

en: biodiversity green-spaces seasons urban well-being

envidat

en: EnviDat: the Environmental Data Portal

en: Playground biodiversity and preschool children well-being

en:
This file contains the playground and plant diversity data for the manuscript "Playground tree diversity: a driver of well-being in preschool children".
The file is separated in five tabs containing:
(1) the overview of the playgrounds,
(2) the overview of all plant species, with their life-history traits and the diversity indexes per layer,
(3) the tree (canoy) layer abundance of each plant species, measured exhaustively in each playground,
(4) the shrub layer abundance of each plant species, measured exhaustively in each playground, and
(5) the ground layer abundance of each plant species, estimated with the Braun-Blanquet scale in 10 quadrats per playground.

en: Plant and playground data.xlsx

en: This file contains the anonymised well-being and nature-connectedness data for the manuscript "Playground tree diversity: a driver of well-being in preschool children".

en: This document includes the English version of the questionnaire administered to carers of pre-school children. This was the original questionnaire, later translated in the 10 most spoken languages in Baden-Württemberg (DE).

en: Spielplatz Fragebogen_Englisch.pdf

en: This document includes the German version of the questionnaire administered to carers of pre-school children.

en: Spielplatz Fragebogen_Deutsch.pdf

en: This document includes the French version of the questionnaire administered to carers of pre-school children.

en: Spielplatz Fragebogen_Französisch.pdf

en: This document includes the Italian version of the questionnaire administered to carers of pre-school children.

en: Spielplatz Fragebogen_Italienisch.pdf

en: This document includes the Polish version of the questionnaire administered to carers of pre-school children.

en: Spielplatz Fragebogen_Polnisch.pdf

en: This document includes the Arabic version of the questionnaire administered to carers of pre-school children.

en: Spielplatz Fragebogen_Arabisch.pdf

en: This document includes the Romanian version of the questionnaire administered to carers of pre-school children.

en: Spielplatz Fragebogen_Rumänisch.pdf

en: This document includes the Russian version of the questionnaire administered to carers of pre-school children.

en: Spielplatz Fragebogen_Russisch.pdf

en: This document includes the Spanish of the questionnaire administered to carers of pre-school children.

en: Spielplatz Fragebogen_Spanisch.pdf

en: This document includes the Turkish version of the questionnaire administered to carers of pre-school children.

en: Spielplatz Fragebogen_Türkisch.pdf

en: This document includes the Ukrainian version of the questionnaire administered to carers of pre-school children.

en: Spielplatz Fragebogen_Ukrainian.pdf

biodiversity

display_name: biodiversity

green-spaces

display_name: green-spaces


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

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

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

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

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

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

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

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

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

educ

en: Education, culture and sport

en: Natural hazard fatalities per year in Switzerland since 1946

debris-flow-fatalities

display_name: debris-flow-fatalities

earthquake-fatalities

display_name: earthquake-fatalities

flood-fatalities

display_name: flood-fatalities

ice-avalanche-fatalities

display_name: ice-avalanche-fatalities

landslide-fatalities

display_name: landslide-fatalities

lightning-fatalities

display_name: lightning-fatalities

natural-hazard-fatalities

display_name: natural-hazard-fatalities

rockfall-fatalities

display_name: rockfall-fatalities

windstorm-fatalities

display_name: windstorm-fatalities


en: This data supplement contains the data associated with the manuscript: "Hyper-resolution large-scale hydrological modelling benefits from improved process representation in mountain regions" (Janzing et al., 2025). It contains the data to create the figures in the paper and consists of model output from **PCRaster Global Water Balance Model (PCR-GLOBWB) 2.0** model over the larger Alpine region at 30 arcsec resolution. As the full model output takes up very high memory space, we here mostly provide time series for the different measurement stations used for evaluation and some additional maps needed to recreate the figures in the manuscript.

en: discharge european-alps hydrological-simulations hydrology large-scale

en: Data supplement to Hyper-resolution large-scale hydrological modelling benefits from improved process representation in mountain regions

discharge

display_name: discharge

european-alps

display_name: european-alps

hydrological-simulations

display_name: hydrological-simulations

hydrology

display_name: hydrology

large-scale

display_name: large-scale


wasserschloss-europas

en:
*english description below
Die Schweiz als „Wasserschloss Europas“ ist eine etablierte Bezeichnung im öffentlichen Diskurs, die sich im Laufe der Zeit zu einer identitätsstiftenden Metapher entwickelt hat. Die Zeitungsanalyse der letzten 100 Jahre zeigt, wie sich die Verwendung dieser Metapher unter dem Einfluss äusserer Faktoren gewandelt hat. Das Jahr mit den meisten Zeitungsartikeln, in denen die Metapher verwendet wurde, war der Hitzesommer 2003. Weitere Jahre mit häufigem Vorkommen der Metapher fallen mit internationalen Ausstellungen (Expos) zusammen, bei denen sich die Schweiz als „Wasserschloss Europas“ präsentierte oder entsprechende Bezüge herstellte. Dabei diente die Metapher einerseits als Symbol für den Wasserreichtum des Landes, andererseits unterstrich sie die Rolle der Schweiz im Umweltschutz, deren Verantwortung gegenüber den Unterliegern; zudem diente sie der touristischen Vermarktung der Naturlandschaft. Lange Zeit galt der Wasserreichtum in der Schweiz als selbstverständlich, angesichts der rasanten Klimaerwärmung und des damit einhergehenden steigenden Nutzungsdrucks auf die Ressource Wasser und die aquatischen Lebensräume erscheint die Verwendung dieser beschaulichen Metapher als überholt.
Switzerland, as the “Wasserschloss Europas”, is an established term in public discourse that has evolved over time into an identity-shaping metaphor. A newspaper analysis spanning the last 100 years shows how the use of this metaphor has changed in response to external factors. The year with the highest number of newspaper articles employing the metaphor was the heatwave summer of 2003. Other years with high numbers of occurrences coincide with international exhibitions (Expos), where Switzerland presented itself as the “Wasserschloss Europas” or made related references. The metaphor served as a symbol of the country’s abundance of water and, highlighted Switzerland’s role in environmental protection and its responsibility towards downstream countries; it also supported the tourist marketing of its natural landscapes. For a long time, Switzerland’s water wealth was taken for granted. In view of rapid climate warming and the resulting increasing pressure on water resources and aquatic habitats, the use of this idyllic metaphor now seems outdated.

en: metapher wasserressourcen wasserschloss wasserwirtschaft zeitungsartikelanalyse

en: Wasserschloss Europas

title_for_slug: wasserschloss-europas

en: Sammlung der 226 verwendeten Zeitungsartikel.

en: Kodierungsschema für die Zeitungsartikelanalyse.

metapher

display_name: metapher

wasserressourcen

display_name: wasserressourcen

wasserschloss

display_name: wasserschloss

wasserwirtschaft

display_name: wasserwirtschaft

zeitungsartikelanalyse

display_name: zeitungsartikelanalyse


en:
This repository contains data related to the field experiment of the WP3 (social impacts) of the ALANeX project. This project is a collaboration between the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and Elektrizitätswerk des Kantons Zürich (EKZ). It has received funding from the WSL programme EXtremes. The overall objective of the ALANeX project was to assess the ecological and social impact of anthropogenic light at night.

We conducted a randomized field experiment to compare the well-being benefits of exposure to streetlights of different correlated colour temperatures (CCT) during a 20-min stay in the centre of Richterswil, Swizerland, to explore:
 - the impact of exposure to streetlamps of different CCT on affect, self-reported stress and cortisol levels
- the differences in impression of artificial light and of the feeling of safety provided by streetlamps based on its CCT.

en: artificial-lighting field-data stress urban-landscape well-being

en: This file includes the information sheet provided to participants before they consented to take part in the study.

en: 01_Information sheet.pdf

en: This file contains the informed consent document that participants signed to participate in the study.

en: 02_Particiants consent form.pdf

en: This document includes the questionnaire administered at the first measurement time point (before exposure).

en: This document includes the questionnaire administered at the second measurement time point (after exposure).

en: This file contains data for the manuscript "Randomised trial reveals a mismatch between preferences for and hormonal responses to anthropogenic light colour temperatures".

en: 05_Anonymised dataset.xlsx

artificial-lighting

display_name: artificial-lighting

field-data

display_name: field-data

stress

display_name: stress

urban-landscape

display_name: urban-landscape

well-being

display_name: well-being


en:
The data show locations of the most recent outdoor activity (referred to as restorative location, RL) of Swiss residents, and descriptive characteristics of these places. The data was collected via an online participatory mapping survey targeting residents of urban, peri-urban, and rural areas across Switzerland. Respondents were selected using a stratified random sampling method, considering the greenness and noise exposure of their home locations, and demographic factors such as age, gender, and language region.
The survey was conducted using Maptionnaire software (https://maptionnaire.com). Participants were asked to map the location of their most recent outdoor activity (referred to as restorative location, RL) and provide additional information on their experience in these places. The responses were integrated with the quantitative spatial information (biophysical landscape characteristics, referred to as metrics) on the visited locations, obtained from spatial and remote sensing data.

en: gis-analysis quiet-areas restorative-environments sensory-perception soundscape traffic-noise

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: Restorative locations from participatory mapping by Swiss residents

educ

en: Education, culture and sport

en: The data contains a shapefile of mapped restorative locations with a comprehensive attribute list including geodata and survey results.

gis-analysis

display_name: gis-analysis

quiet-areas

display_name: quiet-areas

restorative-environments

display_name: restorative-environments

sensory-perception

display_name: sensory-perception

soundscape

display_name: soundscape

traffic-noise

display_name: traffic-noise


en:
The SwissRad10 dataset contains light availability maps at 10 m and hourly resolution across an entire annual cycle with wall-to-wall coverage across the whole of Switzerland.
Areas below and within 25 m of forests include light availability calculated for both leaf-on and leaf-off canopy structure, and terrain only data is available across the full domain.
The variables sky-view factor and direct-beam transmissivity are both included, allowing estimation of both direct and diffuse light at the ground surface.
The dataset was calculated using the detailed synthetic hemispheric image-based ray-tracing Canopy Radiation Model (CanRad) with the latest airborne lidar and terrain surface data from the swissSURFACE3D dataset.

en: forests lidar light-availability radiation-transfer shortwave-radiation sky-view-fraction solar-energy solar-irradiance terrain transmissivity

en: SwissRad10 Hourly light availability maps at 10 m resolution over Switzerland

en: Data Download

forests

display_name: forests

lidar

display_name: lidar

light-availability

display_name: light-availability

radiation-transfer

display_name: radiation-transfer

shortwave-radiation

display_name: shortwave-radiation

sky-view-fraction

display_name: sky-view-fraction

solar-energy

display_name: solar-energy

solar-irradiance

display_name: solar-irradiance

terrain

display_name: terrain

transmissivity

display_name: transmissivity


en:
Raw data of paper: A recent ash dieback infection neither affects emerald ash borer
performance nor triggers a substantial systemic phytochemical
defense response in European ash (https://doi.org/10.1007/s10340-025-01981-4)
Raw data on emerald ash borer (EAB) weight gain, mortality, development and longevity. Raw data of ash phloem and leaf chemistry. Raw data of ash dieback (ADB) lesion length.

en: agrilus-palnipennis ash-dieback fraxinus-excelsior metabolomics pest-invasion

en: Raw data-A recent ash dieback infection neither affects emerald ash borer performance nor triggers a substantial systemic phytochemical defense response in European ash

en: Open access raw data Eisenring et al 2025.xlsx

agrilus-palnipennis

display_name: agrilus-palnipennis

ash-dieback

display_name: ash-dieback

fraxinus-excelsior

display_name: fraxinus-excelsior

metabolomics

display_name: metabolomics

pest-invasion

display_name: pest-invasion


restwasser-datenbank

en:
(in English below)
Der erweiterte Ausbau der Wasserkraftproduktion steht im Spannungsfeld mit der Sicherstellung ökologischer Funktionen von Fliessgewässern. Die neu entwickelte Restwasser-Datenbank bietet eine öffentlich zugängliche Grundlage zu den Restwasserbestimmungen der Schweizer Wasserkraftanlagen (WKA). Sie umfasst 252 WKA mit einer installierten Leistung von mindestens 3 MW, darunter 160 Laufwasserkraftwerke, 75 Speicherkraftwerke, 16 Pumpspeicherkraftwerke und ein Umwälzkraftwerk. Die erwartete jährliche Stromproduktion dieser Anlagen (ohne Umwälzbetrieb) beträgt 31’540 GWh, was rund 76 % der gesamten schweizerischen Wasserkraftproduktion entspricht. Die Datenbank umfasst rechtliche, hydrologische und technische Attribute und erlaubt dank ihrer kraftwerksspezifischen Struktur eine einfache Verknüpfung mit der schweizerischen Wasserkraftstatistik (WASTA). Dadurch wird die effiziente Bearbeitung hydro-energetischer Fragestellungen ermöglicht.
Eine öffentlich zugängliche Datengrundlage ist insbesondere vor dem Hintergrund des Klimawandels relevant, da dieser die hydrologischen Verhältnisse verändert und Nutzungskonflikte um Wasserressourcen verschärft. Gleichzeitig steigt der Druck auf aquatische Lebensräume, unter anderem infolge steigender Wassertemperaturen. Die Restwasser-Thematik betrifft nicht nur das Spannungsfeld zwischen Energieproduktion und Gewässerökologie, sondern auch Aspekte der Wasserqualität sowie weitere Nutzungen wie Trinkwasserversorgung, landwirtschaftliche Bewässerung, Kühlung und den Betrieb von Abwasserreinigungsanlagen (ARAs). Für eine nachhaltige Wasserwirtschaft ist die Etablierung öffentlicher und belastbarer Datengrundlagen unerlässlich, da sie eine Quantifizierung und Abwägung divergierender Interessen ermöglicht. Nur auf dieser Basis lässt sich die bislang emotional geführte Debatte versachlichen und können Entscheidungsprozesse evidenzbasiert unterstützt werden, die eine zukunftsfähige und nachhaltige Wasserwirtschaft fördern.
The planned expansion of hydropower in Switzerland conflicts with the need to preserve the ecological functions of rivers. To support informed decision-making, a publicly accessible database on environmental flows has been developed. It documents the environmental flow requirements for 252 hydropower plants with an installed capacity of ≥ 3 MW, including 160 run-of-river plants, 75 storage plants, 16 pumped-storage plants, and one closed-loop pumped-storage plant. Together, these plants have an expected annual electricity production of 31’540 GWh—around 76% of Switzerland’s total hydropower generation.
The database contains legal, hydrological, and technical attributes and can be easily linked to the national hydropower statistics (WASTA), enabling efficient analysis of hydroelectric issues. In the context of climate change, such an open database is crucial, as shifting hydrological conditions intensify competing water uses and increase pressure on aquatic ecosystems. Environmental flow requirements involve not only the trade-off between energy production and river ecology but also water quality and other uses such as drinking water supply, irrigation, cooling, and wastewater treatment. Robust, publicly available data are essential for quantifying and balancing these competing interests and for supporting evidence-based decisions in sustainable water resources management.

en: dotierwasser konzessionen restwasser wasserkraft wasserwirtschaft

en: Restwasser-Datenbank

title_for_slug: restwasser-datenbank

en: Restwasser-Datenbank mit rechtlichen, hydrologischen und technischen Attributen

en: Hydrologische Abflusszeitreihen von 1991-2020 für Anwendungen mit der Restwasser-Datenbank

en: Hydrologische Abflusszeitreihen - Restwasser-Datenbank 2025.xlsx

en: Shapefile mit Restwasserstrecken - ein Produkt das in der Erarbeitung der Restwasser-Datenbank mitentstanden ist.

en: Restwasserstrecken - Restwasser-Datenbank 2025.zip

en: Einzugsgebiete - Restwasser-Datenbank 2025.zip

dotierwasser

display_name: dotierwasser

konzessionen

display_name: konzessionen

restwasser

display_name: restwasser

wasserkraft

display_name: wasserkraft

wasserwirtschaft

display_name: wasserwirtschaft


en:
Plant ecophysiological and associated environmental data for two tree species (*Fagus sylvatica* and *Quercus pubescens*) grown for five years in a factorial open-top climate chamber experiment (Model Ecosystem Facility, Birmensdorf, Switzerland) to test the long-term effects of heat (+5 °C) and soil drought (irrigation reduced by 50%). n = 4 trees per species and treatment, n = 48 trees total.
 Included data sheets:
- Environmental_Data.csv: Chamber-level environmental recorded every 10 minutes
- Tleaf.csv: Leaf temperatures recorded every 10 minutes
- Gas_exchange.csv: Leaf-level gas exchange data taken diurnally on four days during the growing season
- Leaf_traits.csv: Leaf functional traits
- TSMs_HSMs.csv: Photosynthetic thermal tolerance and leaf water potential data, summarized also in terms of leaf thermal safety margins and hydraulic safety margins, collected 3-4 times during the growing season
- Scorch.csv: Leaf-level leaf scorching data, including leaf thermal safety margins and scorching occurrence and presence, summarized by date

en: fagus-sylvatica functional-traits gas-exchange hot-drought hydraulic-safety-margins leaf-scorching quercus-pubescens thermal-safety-margins thermoregulation

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: Hydraulic stress limits thermal acclimation in trees under chronic drought

educ

en: Education, culture and sport

en: README file with more information about the dataset.

en:
Chamber-level abiotic data recorded every 10 minutes.
Variable List:
	- Time (MM/DD/YYYY HH:MM)
	- Chamber: open-top-chamber ID
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
	- Tair: Air temperature (degrees C)
	- Tair_sd: Standard deviation of air temperature
	- RH: Relative humidity
	- RH_sd: Standard deviation of relative humidity
	- VPD: Vapor pressure deficit (kPa)
	- Tsoil: Soil temperature (degrees C)
	- Soil_m: Volumetric soil water content (m^3/m^3)
	- PPFD: Photosynthetic photon flux density (micromoles m^-2 s^-1) *collected from a weather station next to the open-top-chambers (50% intensity inside chambers)
	- windspeed: (m/s) *collected from a weather station next to the open-top-chambers

en:
Tree-level leaf scorching occurrence and severity data summarized by date with daily leaf thermal safety margins.
Variable List:
	- TreeID: Unique identifier for individual trees
	- Date (MM/DD/YYYY)
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Species: Tree species
	- Tleaf: Leaf temperature (degrees C) *measured by an infrared sensor integrated into the Cavicam
	- Tcrit: Critical thermal threshold of Photosystem II (degrees C) *daily entries inherit the Tcrit measured onthe next measurement date and applied to the interval preceding it (or are forward-filled when Tcrit could not be estimated in August or September)
	- TSM: Leaf thermal safety margin (difference between Tcrit and Tleaf, in degrees C)
	- Scorch_binary: Binary indicating whether scorching occurred on that date (0 = no, 1 = yes)
	- Percent_affected: Percent of the camera window affected by scorching during a scorching event

en:
Tree-level photosynthetic thermal tolerance, leaf water potentials, thermal safety margins, and hydraulic safety margins measured 3-4 times per tree during the growing season.
Variable List:
	- TreeID: Unique identifier for individual trees
 	- Species: Tree species
	- Campaign: Month of the growing season in which data was collected
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Waterpotential: midday leaf waterpotential (MPa)
	- WPtlp: Waterpotential at leaf turgor-loss-point (MPa) *Measured by Mas, E., Cochard, H., Deluigi, J., Didion-Gency, M., Martin-StPaul, N., Morcillo, L., Valladares, F., Vilagrosa, A., & Grossiord, C. (2024). Interactions between beech and oak seedlings can modify the effects of hotter droughts and the onset of hydraulic failure. New Phytologist, 241(3), 1021-1034. https://doi.org/https://doi.org/10.1111/nph.19358
	- HSM: Hydraulic safety margin (difference between WPtlp and Waterpotential, in MPa)
	- Tcrit: Critical thermal threshold of Photosystem II (degrees C)
	- T50: High leaf temperature at which Photosystem II function declines by 50% (degrees C)
	- Tleaf.max: Maximum leaf temperature during the week preceding thermal tolerance measurement
	- TSM.min: Minimum leaf thermal safety margin during the week preceeding thermal tolerance measurement (difference between Tcrit and Tleaf.max, in degrees C)

en:
Tree-level leaf functional trait data.
Variable List:
	- TreeID: Unique identifier for individual trees
	- Chamber: open-top-chamber ID
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
 	- Species: Tree species
	- Leaf_width (cm)
	- Leaf_area (cm^2)
	- Stomatal_density (count per cm^2)
	- Pore_length: Stomatal pore length (micrometers)
	- Guard_cell_width: Stomatal guard cell width (micrometers)
	- Leaf_thickness (mm)
	- Epidermal_thickness (mm)
	- Parenchyma_thickness (mm)
	- Parenchyma:Epidermis: Ratio between parenchyma and epidermal thickness
	- Reflectance: Reflectance of photosynthetically active radiation (400-700), fraction
	- Transmittance: Transmittance of photosynthetically active radiation (400-700), fraction
	- Absorptance: Absorptance of photosynthetically active radiation (400-700), fraction
	- Absorbance: Optical absorbance of photosynthetically active radiation (400-700 nm), unitless
	- g1: Medlyn-style stomatal slope calculated with Licor LI6800 data

en:
Leaf-level gas exchange collected diurnally for each tree on 4 days of the growing season and summarized to growing-season mean.
Variable List:
	- TreeID: Unique identifier for individual trees
	- Chamber: open-top-chamber ID
	- Species: Tree species
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
	- gsw.mean: Mean stomatal conductance to water vapor (mol m^-2 s^-1)
	- E.mean: Mean transpiration rate (mol m^-2 s^-2)
	- Tdiff.mean: Mean difference between leaf and air temperature on the day of Licor measurements
	- lnE.mean: Mean of the natural log of the transpiration rate
	- lnE.mean~Tdiff.mean_slope: The slope of the linear least-squares relationship between the natural log of lnE.mean and Tdiff.mean

en:
Tree-level leaf temperature data recorded every 10 minutes.
Variable List:
	- Timestamp (MM/DD/YYYY HH:MM)
	- TreeID: Unique identifier for individual trees
	- Chamber: open-top-chamber ID
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
 	- Species: Tree species
	- Tair: Air temperature (degrees C)
	- Tleaf: Leaf temperature (degrees C)
	- Tdiff: Leaf-to-air temperature differential (degrees C)

fagus-sylvatica

display_name: fagus-sylvatica

functional-traits

display_name: functional-traits

gas-exchange

display_name: gas-exchange

hot-drought

display_name: hot-drought

hydraulic-safety-margins

display_name: hydraulic-safety-margins

leaf-scorching

display_name: leaf-scorching

quercus-pubescens

display_name: quercus-pubescens

thermal-safety-margins

display_name: thermal-safety-margins

thermoregulation

display_name: thermoregulation


functional-invertebrate-groups-dataset

en: Collected Invertebrate data using pitfall traps at streetlights with different characteristics at three different sites in Switzerland (Birmensdorf, Lägern & Alpthal). LED characteristics are LED color temperature, light level and luminaire shape (K, L_level, Diff). Additionally, we had two pitfall trap positions: Center (C; 100% light intensity)) and Periphery (P; 10% light intensity). The traps were emptied weekly over a period of two summers (2021 & 2022). The samples were sorted into 40 different taxonomic invertebrate groups. We included weeks where we turned of the light for a different experiement, these weeks were excluded from this dataset.

en: alan artificial-lighting community-composition functional-groups invertebrates led light-pollution

en: Functional Invertebrate Groups - Dataset

title_for_slug: functional-invertebrate-groups-dataset

en: Raw data of invertebrate sampling at different streetlight characteristics (light temperature (K), light level (L_level), luminaire shape (Diff)) over two summers (2021 & 2022). Included are two pitfall trap positions (Center C (100% light intensity) and Periphery P (10% light intensity).

alan

display_name: alan

artificial-lighting

display_name: artificial-lighting

community-composition

display_name: community-composition

functional-groups

display_name: functional-groups

invertebrates

display_name: invertebrates

led

display_name: led

light-pollution

display_name: light-pollution