maintainer: Camille Albouy

en: teleo-primers 12s actinopterygii atlantic-ocean barcoding environmental-dna mitochondrial-dna

en: This file contains the Teleo Barcode for the Atlantic fishes collected during a French scientific survey (Evhoe) for stock assessment.

en:
Mitochondrial tree based on the 12s sequences obtained for altantic fish.
This data represent the maximum likelihood phylogenetic tree allowing to assess 1) the clustering of the different sequences generated in
this study and their closest sequences recovered through a MegaBLAST on GenBank, 2) the robustness of the clades and 3) the
phylogenetic dissimilarities through the branch length of the tree among the different sequences. This tree was built with IQtree2
using the best substitution model found by modelFinder. The robustness of the node was evaluated using 1000 ultra-fast bootstrap
(node information indicated on the right of the /) and SH-aLRT procedures (node information indicated on the left of the /), using a hillclimbing nearest neighbor interchange (NNI) search to reduce the risk of overestimating branch supports with ultra-fast Bootstrap
caused by too severe model violations.

en: This file contains the metadata associated with each scientific survey

en: This file contains the 12s mitochondrial sequences for the Atlantic fishes collected on a French scientific survey (Evhoe) for stock assessment.

en: This file contains the metadata associated with each 12 s mitochondrial sequence.

en:
The global biodiversity crisis driven by anthropogenic pressures significantly threatens marine ecosystems functioning. The rate of climate change and the impacts of anthropogenic pressures outpacing the capabilities of our observation tools, stresses the need to develop new methods to assess these rapid modifications. Environmental DNA (eDNA; DNA traces released by organisms) metabarcoding has emerged as a non-invasive method that has been widely developed over the last decade. Thanks to a large spatio-temporal coverage, high detection of rare species and its time and cost effectiveness, eDNA metabarcoding represents a promising biomonitoring tool. However, capturing fish diversity using eDNA requires a high-quality genetic reference database, which we are currently still lacking. For the South European Atlantic shelf area, we estimated that only 41% of the fish species present were recorded in the available eDNA reference databases. Improving reference databases can notably rely on opportunistic sampling enabling the reporting of sequences for new species. Therefore, the data provided here consists of barcoding 95 species of ray-finned and cartilaginous fishes over the 12S mitochondrial DNA gene. We generated 168 12S barcodes from fishes that were sampled in the Bay of Biscay (Northeast Atlantic, France) between 2017 and 2019. We also provided the “Teleo” barcode associated with a specific 12S region for each individual. In addition to the sequences, we provided for each individual the taxonomy, the details associated with the barcode (Genbank accession number, chromatograms), a photograph, as well as 5 ecomorphological measures and 11 life-history traits. These traits document several functions such as dispersion, diet, habitat use, and position in the food web. Furthermore, we provided the metadata of each sampling site (date, station, sampling hour, gear, latitude, longitude, depth) and environmental variables measured in situ (conductivity, salinity, water temperature, water density, air temperature).
This data set is highly valuable to improve the Northeast Atlantic eDNA genetic database, thus helping to better understand the effects of environmental forcing in the Bay of Biscay, a transition zone housing mixed assemblages of boreal, temperate and subtropical fish species susceptible to display variability in functional traits to adapt to changing conditions.

actinopterygii

display_name: actinopterygii

atlantic-ocean

display_name: atlantic-ocean

barcoding

display_name: barcoding

environmental-dna

display_name: environmental-dna

mitochondrial-dna

display_name: mitochondrial-dna

teleo-primers

display_name: teleo-primers

en: ATLFISHREF A 12S mitochondrial reference dataset for metabarcoding Atlantic Fishes frequently caught during scientific surveys in the Bay of Biscay


maintainer: EnviDat Support

en: cosmo-wrf grayzone numerical-simulation wrf complex-terrain

state: active

type: dataset

media_type: application/zip

format: ZIP

en:
This dataset contains simulation data for *"Influence of Air Flow Features on Alpine Wind Energy Potential" (Kristianti et al., 2024).*
•	The WRF_dia2702 and WRF_dia1103 folders contain the namelist.wps, namelist.input, and output files from the simulation in the Diablerets region on 27/02/2021 and 11/03/2021, respectively.
•	The WRF_luk2310 and WRF_luk0912 folders contain the namelist.wps, namelist.input, and output files from the simulation in the Lukmanier region on 23/10/2020 and 09/12/2020, respectively.
•	The postprocessing file contains Python codes used to create figures in Kristianti et al. (2024).
**Notes:**
•	Topography input is provided by Gerber et al. (2021).
•	Simulations are run using the COSMO-WRF model developed by Gerber et al. (2018).
•	Visualization by Python code uses wrf-python provided by Ladwig et al. (2017).
*This dataset was produced by the EDGE consortium sponsored by the Swiss Federal Office of Energy's SWEET programme, Swiss National Science Foundation (SNSF): Grant 179130, and the Swiss National Supercomputing Centre (CSCS) projects s938, s1115 and s1242.*

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

grayzone

display_name: grayzone

numerical-simulation

display_name: numerical-simulation

wrf

display_name: wrf

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: COSMO-WRF Dataset for Swiss Alps Simulations in Gray-Zone Resolution


en: landscape-change visualization policy-action climate-change scenario

en: Seeland subtitles FR

en:
Scenarios of landscape change under climate change in the Swiss lowlands.
Trailer in French with subtitles.

format: SERVICE

en: Entremon subtitles FR

en: Scenarios of landscape change under climate change in the Swiss mountains. Trailer in French with subtitles.

en: Entremont subtitles DE

en:
Scenarios of landscape change under climate change in the Swiss mountains.
Trailer in German with written subtitles.

en: Seeland subtitles DE

en:
Scenarios of landscape change under climate change in the Swiss lowlands.
Trailer in German with subtitles.

en:
Short videos showing scenarios of potential landscape development in a 4°C warmer climate for a Swiss mountain region and a region on the Swiss Plateau. The videos are in German or French with subtitles.
The videos' key message is: Climate change will have an impact on the Swiss landscapes. However, according to the strategy chosen to adapt to climate change, society and politics will also have a strong influence on the future landscapes and the services they will provide. If no action is taken to adapt to climate change today, then society will have to react in a hurry at a later point in time. This can often result in resource-intensive technical solutions. Conversely, if society takes action in an anticipatory way already today, nature-based solutions can be implemented more successfully and humans can benefit more from the landscape services..
These short videos are teasers that should make the viewers curious to visit the panorama pictures (https://viergrad.envidat.ch/).

climate-change

display_name: climate-change

landscape-change

display_name: landscape-change

policy-action

display_name: policy-action

scenario

display_name: scenario

visualization

display_name: visualization

en: Teaser videos Swiss landscapes under climate change


distributed-acoustic-sensing-brienz

title_for_slug: distributed-acoustic-sensing-brienz

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

en: Data access

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.

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: Distributed Acoustic Sensing Brienz


maintainer: Rebecca Mott

en: dischmex turbulent-sensible-heat-fluxes snow-patch patchy-snow-covers eddy-covariance-measurements

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.6 m.

mimetype: application/zip

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 0.7 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.5 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 3.2 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (DA-600, Kaijo-Denki) in 0.3 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.3 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.2 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 1.7 m on a daily basis.

en: Output explanation

format: XLS

en: Gletschboden setup

media_type: image/png

en: Background map based on data © 2014/2016 swisstopo (JD100042)

format: PNG

mimetype: image/png

en:
This dataset contains eddy-covariance measurements in the ablation period of 2014-2016. Measurements were taken from two turbulence towers over a long-lasting snow patch, which are 5 m apart from each other (2014 and 2015). The turbulence towers were equipped with two YOUNG ultrasonic anemometers mounted 0.7 m (in 2014) and 3.3 m (in 2015) above snow-free ground, two ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) mounted at 2.6 m (in 2014) and 2.2 m (in 2015) above snow-free ground and one anemometer (DA-600, Kaijo Denki) mounted at 0.3 m above snow surface.

The measurement setup changed in 2016 and includes a measurement above the snow-free ground in upwind direction (Swiss coordinates: 790191/176689). The measurement tower is equipped with one ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 3.3 m above the snow-free ground. Additionally, one measurement tower is installed above the long-Lasting snow patch and equipped with the same setup as 2015.

Turbulence data were sampled at a frequency of 20 Hz. The processing of the data to quality controlled fluxes has been done with the Biomicrometeorology flux software (Thomas et al., 2009). The program applies plausibility tests and a despiking test after Vickers and Mahrt (1997) on the measured data. The routine further applies a time-lag correction and considers the deployment (e.g. the sonic azimuth). A frequency response correction (Moore, 1986) is done and a three-dimensional rotation is performed. Finally, quality assurance/quality control (QA/QC) flags after Foken et al., (2004) are issued and fast Fourier transform power and co-spectra are calculated. The change in snow height is considered in the post-processing for every measurement day. The turbulence data were averaged to 30 minute intervals.

dischmex

display_name: dischmex

eddy-covariance-measurements

display_name: eddy-covariance-measurements

patchy-snow-covers

display_name: patchy-snow-covers

snow-patch

display_name: snow-patch

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: DISCHMEX - Impact of extreme land-surface heterogeneity on micrometeorology over spring snow-cover


en: place-attachment electrodermal-activity sense-of-place landscape urban-ecology

en: 04. Scripts

en: 02. Questionnaire Data

en: 05. Visuals

en: 03. Publication Ready Data

en: 01. EDA Processed Data

en: 00. EDA Raw Data

en: This repository contains data on EDA measurements of visitors with different cultural backgrounds in virtual urban park settings. The parks are a Persian garden (Shiraz, Iran) and a historical park in Zurich, Switzerland. The cultural background of the visitors is Persian and Central European. The repository contains raw data from EDA, processed time series and statistical procedures.

electrodermal-activity

display_name: electrodermal-activity

landscape

display_name: landscape

place-attachment

display_name: place-attachment

sense-of-place

display_name: sense-of-place

urban-ecology

display_name: urban-ecology

en: Electrodermal Activity (EDA) of Bi-cultural Visitors In Virtual Park Settings


carabid-beetles-in-forests

maintainer: Martin K. Obrist

title_for_slug: carabid-beetles-in-forests

en: trapping arthropoda carabidae coleoptera running-beetles insects forest

media_type: text/plain

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

format: TXT

en: Tab-delimited data of the according sql query.

en: SQL query used to extract the data from fauna database.

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

en:
Carabidae data from all historic up to the recent projects (21.10.2019) of WSL, collected with various methods in forests of different types.

Version 2 ('FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae') contains additional data field PROJ_FALLENBEZEICHNUNG.

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

arthropoda

display_name: arthropoda

carabidae

display_name: carabidae

coleoptera

display_name: coleoptera

forest

display_name: forest

insects

display_name: insects

running-beetles

display_name: running-beetles

trapping

display_name: trapping

en: Carabid beetles in forests


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

en: Data plot of the field station (NOF)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Forest station data (NOB)

media_type: text/csv

en: Data file.

format: CSV

mimetype: text/csv

en: Site aerial photo (Novaggio)

en: Site aerial photo from both Novaggio stations. Source: Google. 2016. Site aerial photo from both Novaggio stations. Retrieved November 15, 2016, from https://www.google.ch/maps

mimetype: image/jpeg

en: Site photo of the forest station (NOB)

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

en: Field station data (NOF)

en: Data plot of the forest station (NOB)

en: Site photo of the field station (NOF)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Novaggio in Switzerland where one station is located within a natural deciduous forest stand (NOB) with Turkey oak (_Quercus cerris_; 70 yrs), sweet chestnut (_Castanea sativa_; 90 yrs) and silver birch (_Betula pendula_; 70 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Novaggio is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


snow-depth-mapping

maintainer: Mauro Marty

title_for_slug: snow-depth-mapping

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

en: Snow depth map 20140417

media_type: image/tiff

en: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.18 m

format: TIFF

mimetype: image/tiff

en: Snow depth map 20130415

en: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en: Snow depth map 20150415

en: Snow depth map 20160420

en: Snow depth map 20100416

en: Snow depth map 20160126

en:
Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m
Because of very diffuse light conditions at time of image acquisition the quality of the resulting snow depth values are in some regions quite poor.

en: Snow depth map 20160309

en: Snow depth map 20120320

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

The work is published in:

Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015

aerial-imagery

display_name: aerial-imagery

avalanche

display_name: avalanche

dsm

display_name: dsm

image-matching

display_name: image-matching

snow-depth

display_name: snow-depth

snow-hydrology

display_name: snow-hydrology

snow-water-equivalent

display_name: snow-water-equivalent


maintainer: Dominique Weber

en: mowing use-intensity management grassland remote-sensing landsat sentinel-2 land-use

en: Grassland-use intensity map 2020

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2020. See readme file for more details.

format: GeoTIFF

en: Preview time of 1st grassland management event 2020

en: Spatial distribution of the timing (day of year; DOY) of the first management event for Switzerland and for three zoomed-in regions (a, b, c). The three zoomed-in regions show different grassland-use intensities, which are reflected in the timing of the first event of the year (Weber et al., 2023; IN REVIEW)

en: Webcam-based reference data

en: Webcam-based reference data from 2020 and 2021 (see readme).

en: Readme

en: Metadata and description of the maps and webcam-based reference data.

en: Grassland-use intensity map 2018

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2018. See readme file for more details.

en: Grassland-use intensity map 2021

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2021. See readme file for more details.

en: Grassland-use intensity map 2019

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2019. See readme file for more details.

en: Preview number of grassland management events 2020

en: Spatial distribution of the number of management events for Switzerland and for three zoomed-in regions (a, b, c). The three zoomed-in regions show different grassland-use intensities, which are reflected in the distribution of management frequency (Weber et al., 2023; IN REVIEW)

en:
A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018–2021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework.

The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m × 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons.

We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see [Weber et al., 2023](https://doi.org/10.1002/rse2.372)).

The webcam-based reference data used for verification was subsequently added on 14.02.2024.

grassland

display_name: grassland

land-use

display_name: land-use

landsat

display_name: landsat

management

display_name: management

mowing

display_name: mowing

remote-sensing

display_name: remote-sensing

use-intensity

display_name: use-intensity

educ

en: Education, culture and sport

en: Grassland-use intensity maps for Switzerland


en: soil-moisture avalanche-activity glide-snow-avalanche avalanche snow-profile

en: This file provides an overview of the dataset, the detailed measurement setup, and points out limitations.

en: This file contains the coordinates of the reference location and the sensors in the Seewer Berg slope as a shapefile.

en: This file contains all sensor recordings in the Seewer Berg slope (time interval: 15 minutes).

en: This file contains SNOWPACK simulations (snow height, air temperature, rain) at a virtual station in the reference location. The virtual station was interpolated between the Weissfluhjoch (WFJ2) and Klosters Madrisa (KLO2) IMIS Stations.

en: This file contains the aggregated data from manually recorded snow profiles in the reference location, 100m northwest of the Seewer Berg slope and in release areas of glide-snow avalanches in the Seewer Berg slope.

en: This file contains the release areas (polygon) of glide-snow avalanches as a shapefile.

en: This file contains the daily avalanche activity on Dorfberg as extracted from time-lapse photographs.

en: This file contains all soil measruements (liquid water content and matric potential) recorded at the reference location.

en: This file explains all provided datasets, variables and their units.

en:
This dataset consists of spatio-temporal soil measurements and local snow measurements which were recorded to investigate glide-snow avalanche release in the Seewer Berg slope (Davos, Switzerland) from Nov 2021 to June 2024. Investigations based on this dataset were published in the research article:
Fees A., Lombardo M., van Herwijnen A., Lehmann P., Schweizer J. (2025). The source, quantity, and spatial distribution of interfacial water during glide-snow avalanche release: experimental evidence from field monitoring. The Cryosphere
The dataset consists of:
- Soil measurements (temperature and liquid water content) at soil depths of -5cm, -10cm, and -20cm
- Matric potential at depths of -5cm, -10cm, and -20cm
- Manual snow profiles (Snow height, temperature, liquid water content, bulk density)
- Metadata and manual snow profiles on glide-snow avalanches that released in the Seewer Berg slope
- Snow height, air temperature and rain simulated with SNOWPACK.

avalanche-activity

display_name: avalanche-activity

glide-snow-avalanche

display_name: glide-snow-avalanche

snow-profile

display_name: snow-profile

soil-moisture

display_name: soil-moisture

envidat

en: EnviDat: the Environmental Data Portal

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

en: Spatio-temporal soil and local snow monitoring in a glide-snow avalanche prone slope above Davos Switzerland


en: tracers emma eemma mixing-models end-member-mixing-analysis stable-isotopes

en: vignette data

en: A simple data set for the vignette script

mimetype: text/plain

en: eemma user's guide

en: A brief user's guide to EEMMA, with illustrations using a simple data set

en: An R script that performs ensemble end-member mixing analysis

en: eemma vignette script

en: An R script for the vignettes (simple data analysis demonstrations) shown in the user's guide

format: OCTET

mimetype: application/octet-stream

en:
The R script eemma.R, which implements Ensemble End-Member Mixing Analysis (EEMMA) to estimate source fractions in mixtures, exploiting information contained in time-series correlations among tracer time series.

A brief user's guide, a demonstration script, and a demonstration data set are also provided, to accompany Kirchner, J.W., Mixing models with multiple, overlapping, or incomplete end-members, quantified using time series of a single tracer, Geophysical Research Letters, 2023.

The user's guide is available for public use under Creative Commons CC-BY-SA. Public use of the scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

eemma

display_name: eemma

emma

display_name: emma

end-member-mixing-analysis

display_name: end-member-mixing-analysis

mixing-models

display_name: mixing-models

stable-isotopes

display_name: stable-isotopes

tracers

display_name: tracers

en: eemma.R, an R script for Ensemble End-Member Mixing Analysis


maintainer: Matthias Haeni

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

en: Site photo of the forest station (VSB)

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

en: Field station data (VSF)

en: Data file.

en: Site photo of the field station (VSF)

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

en: Forest station data (VSB)

en: Site aerial photo (Visp)

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

en: Data plot of the forest station (VSB)

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

en: Data plot of the field station (VSF)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Visp in Switzerland where one station is located within a natural mixed forest stand (VSB) with Scots pine (_Pinus sylvestris_; 40-80 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VSF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Visp is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

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


en: alptal switzerland studibach time-series groundwater

en:
Groundwater time series of the monitoring network in the Studibach (C7) catchment, Alptal, Switzerland published in
Rinderer M., van Meerveld I, McGlynn B. (2019): From points to patterns – Assessing runoff source area dynamics and hydrological connectivity using time series clustering. Water Resources Research, doi: 2018WR023886R

en: Groundwater time series between 2010 and 2014 of the distributed monitoring system in the Studibach (C7), Alptal, Switzerland. Data published in Rinderer M., van Meerveld I, McGlynn B. (2019): From points to patterns – Assessing runoff source area dynamics and hydrological connectivity using time series clustering. Water Resources Research, doi: 2018WR023886R

alptal

display_name: alptal

groundwater

display_name: groundwater

studibach

display_name: studibach

switzerland

display_name: switzerland

time-series

display_name: time-series

envidat

en: EnviDat: the Environmental Data Portal

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

en: Groundwater time series Studibach (Rinderer et al., 2019, WRR)


en: alpine-pv sweet-edge plane-of-array rooftop-pv agri-pv speed2zero solar-irradiance

en: This document provides an in-depth description of the CH-POA300 dataset. It includes methodology and validation details.

en:
Contains seasonal and annual statistics (mean, sum) of the CH-POA300 dataset.
- "yyyy_agg_period_hourly_stat.nc": statistic <stat> for the period <agg_period> of the year <yyyy>
- "all_agg_period_stat_mean.nc": statistic <stat> for the period <agg_period> reduced on the entire dataset (all years).
\
yyyy: [2009, 2019]
agg_period: [winter, summer, annual]
stat: [mean, sum]

en:
CH-POA300 is a set of maps of hourly solar irradiance projected onto south oriented tilted surfaces, similar to monofacial solar panels, at a spatial resolution of 300m over Switzerland.
Plane-of-array irradiance for tilts of 0° (horizontal), 30°, 70° and 90° (vertical) are provided. The current temporal extent is: 2009-2019.
Pre-computed seasonal and annual statistics of these maps are also provided in the dataset.
More details are provided in the document of specifications (CH-POA300-specs.pdf).

agri-pv

display_name: agri-pv

alpine-pv

display_name: alpine-pv

plane-of-array

display_name: plane-of-array

rooftop-pv

display_name: rooftop-pv

solar-irradiance

display_name: solar-irradiance

sweet-edge

display_name: sweet-edge

en: CH-POA300 -- Hourly Plane-of-Array Solar Irradiance at High Resolution for Switzerland


maintainer: Lorenz Walthert

en: water-potential water-use-strategy anisohydric stomatal-stringency root-distribution

en: The data file contains the data shown in the Figures 2-7 in Walthert et al. (2024).

en:
This dataset represents the data shown in the Figures 2 - 7 of Walthert et al. (2024): Coordination between degree of isohydricity and depth of root water uptake in temperate tree species. Science of the Total Environment (https://doi.org/10.1016/j.scitotenv.2024.174346). A detailed methodical description of the data can be found in the Methods section of the paper.

Abstract
In an increasingly dry environment, it is crucial to understand how tree species use soil water and cope with drought. However, there is still a knowledge gap regarding the relationships between species-specific stomatal behaviour, spatial root distribution, and root water uptake (RWU) dynamics.
Our study aimed to investigate above- and below-ground aspects of water use during soil drying periods in four temperate tree species that differ in stomatal behaviour: two isohydric tracheid-bearing conifers, Scots pine and Norway spruce, and two more anisohydric deciduous species, the diffuse-porous European beech, and the ring-porous Downy oak. From 2015 to 2020, soil-tree-atmosphere-continuum parameters were measured for each species in monospecific forests where trees had no access to groundwater. The hourly time series included data on air temperature, vapour pressure deficit, soil water potential, soil hydraulic conductivity, and RWU to a depth of 2 m. Analysis of drought responses included data on stem radius, leaf water potential, estimated osmotically active compounds, and drought damage.
Our study reveals an inherent coordination between stomatal regulation, fine root distribution and water uptake. Compared to conifers, the more anisohydric water use of oak and beech was associated with less strict stomatal closure, greater investment in deep roots, four times higher maximum RWU, a shift of RWU to deeper soil layers as the topsoil dried, and a more pronounced soil drying below 1 m depth. Soil hydraulic conductivity started to limit RWU when values fell below 10-3 to 10-5 cm/d, depending on the soil. As drought progressed, oak and beech may also have benefited from their leaf osmoregulatory capacity, but at the cost of xylem embolism with around 50% loss of hydraulic conductivity when soil water potential dropped below -1.25 MPa. Consideration of species-specific water use is crucial for forest management and vegetation modelling to improve forest resilience to drought.

anisohydric

display_name: anisohydric

root-distribution

display_name: root-distribution

stomatal-stringency

display_name: stomatal-stringency

water-potential

display_name: water-potential

water-use-strategy

display_name: water-use-strategy

en: Water-use strategies of temperate tree species


maintainer: Izabela Bujak-Ozga

en: catchment-data groundwater solutes catchment stream-water oxygen-isotopes hydrogen-isotopes

en: This document provides a detailed description of the hydrochemical dataset, including, among other things, an overview of the dataset content, an explanation of the column headers, a description of the locations where samples were collected, and an explanation of the abbreviations used in the dataset.

en: This dataset includes mean concentrations of solutes (Calcium, Iron, Magnesium, Sodium, Potassium, Boron, Nitrate, Sulfate, and Chloride) and stable isotope values (hydrogen and oxygen) in water molecules. These were measured in precipitation, groundwater, soil water, and surface water samples collected from the Haute-Mentue catchment in Switzerland during Spring and Fall 2022. Detailed information is available in the accompanying documentation (documentation_for_the_hydrochemical_data_collected_during_spring_fall_2022_in_the_haute-mentue_catchment.pdf).

en: The dataset contains hydrochemical data collected from spring to fall 2022 in the Haute-Mentue catchment, Switzerland. This hydrochemical data includes solute concentrations and stable isotopes of oxygen and hydrogen in water molecules, measured in streamwater, soil water, groundwater, and rainfall samples. Samples were collected during eight rainfall events with higher temporal resolution (hourly) as well as at lower resolution between events. A detailed description of the dataset is provided in the documentation.

catchment

display_name: catchment

catchment-data

display_name: catchment-data

hydrogen-isotopes

display_name: hydrogen-isotopes

oxygen-isotopes

display_name: oxygen-isotopes

solutes

display_name: solutes

stream-water

display_name: stream-water

educ

en: Education, culture and sport

en: Hydrochemical Data Collected during Spring-Fall 2022 in the Haute-Mentue Catchment


maintainer: Frank Techel

en: snowpack-model machine-learning avalanche-size natural-avalanches avalanche

en: Data set DL was used for validation of the avalanche-day classifier and the avalanche-size estimator. The data set contains the quality-checked danger levels merged with the respective SNOWPACK simulations (Sections 2.3 and 3.3(2) in Mayer et al.).

en: Data set AV2 used for training the avalanche-size estimator (Sections 2.1.2 and 3.2 in Mayer et al.)

en: Data set AV1 was used for training the avalanche-day classifier (Sections 2.1.1, 2.2, and 3.1 in Mayer et al.).

en: Data set AV3 was used for validation of the avalanche-day classifier (Sections 2.1.3 and 3.3(1) in Mayer et al.).

en:
The data set contained in this repository was used in the analysis by Mayer et al. (2023):
Mayer, S. I., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, EGUsphere, https://doi.org/10.5194/egusphere-2023-646, 2023.

avalanche

display_name: avalanche

avalanche-size

display_name: avalanche-size

machine-learning

display_name: machine-learning

natural-avalanches

display_name: natural-avalanches

snowpack-model

display_name: snowpack-model

envidat

en: EnviDat: the Environmental Data Portal

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

en: Data-set for prediction of natural dry-snow avalanche activity and avalanche size using physics-based snowpack simulations


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

en: Site photo of the field station (NEF)

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

en: Data plot of the field station (NEF)

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

en: Field station data (NEF)

en: Data file.

en: Site aerial photo (Neunkirch)

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

en: Site photo of the forest station (NEB)

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

en: Data plot of the forest station (NEB)

en: Forest station data (NEB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Neunkirch in Switzerland where one station is located within a natural deciduous forest stand (NEB) with European beech (_Fagus sylvatica_; 160 yrs), sycamore maple (_Acer pseudoplatanus_; 160 yrs) and lime trees (_Tilia sp._; 110 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NEF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Neunkirch is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


en: paleo-climate air-temperature precipitation glaciers last-glacial-maximum orography

en: Technical documentation

en: Technical documentation containing file metadata and variable descriptions.

en: Data Access

en:
High resolution, downscaled climate model data are used in a wide variety of applications in environmental sciences. Here we present the CHELSA-TraCE21k downscaling algorithm to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100 year time steps for the last 21,000 years. Paleo orography at high spatial resolution and at each timestep is created by combining high resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases with the interpolation of a dynamic ice sheet model (ICE6G) and a coupling to mean annual temperatures from CCSM3-TraCE21k. Based on the reconstructed paleo orography, mean annual temperature and precipitation was downscaled using the CHELSA V1.2 algorithm.

The data is published under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

air-temperature

display_name: air-temperature

glaciers

display_name: glaciers

last-glacial-maximum

display_name: last-glacial-maximum

orography

display_name: orography

paleo-climate

display_name: paleo-climate

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: CHELSA-TraCE21k: Downscaled transient temperature and precipitation data since the last glacial maximum


maintainer: Anita C Risch

en: herbivore-exclusion soil-properties climate microbial-biomass-carbon nutrient-addition microbial-respiration anthropogenic-management

en: Risch et al. GEB_data.xlsx

en:
Study sites and experimental design
We collected data from 23 sites that are part of the Nutrient Network Global Research Cooperative (NutNet, https://nutnet.umn.edu/). The mean annual air temperature (MAT) across these sites ranged from -4 to 22°C, mean annual precipitation (MAP) from 252 to 1592 mm, and elevations from 6 to 4261 m above sea level (Fig 1a, Supplementary Table S1), hence cover a wide range of climatic conditions under which grasslands occur (Fig 1b). Soil organic C concentrations ranged between 0.8 to 7.8%, soil total N concentrations between 0.1 and 0.6%, and the soil C:N ratio between 9.1 and 21.5. Soil clay content spanned from 3.0 to 35%, and soil pH from 3.4 to 7.6 (Supplementary Table S2).
At each site, the effects of nutrient addition and herbivore exclusion were tested via a randomized-block design (Borer et al., 2014). Three blocks with 10 treatment plots each were established at each site, except for the site at bldr.us (only two blocks). Each of these 10 plots was randomly assigned to a nutrient or fencing treatment. An individual plot was 5 x 5 m, divided into four 2.5 x 2.5 m subplots. Each subplot was further divided into four 1 x 1 m square sampling plots, one of which was set aside for soil sampling (Borer et al., 2014). Plots were separated by at least 1 m wide walkways. We collected soil samples from four different treatments for this study: (i) untreated control plots (Control), (ii) herbivore exclusion plots (Fence), (iii) plots fertilized with N, P, K, plus nine essential macro and micronutrients (NPK), and (iv) plots with simultaneous fertilizer addition and herbivore exclusion (NPK+Fence). The experiments were established at different times in the past, with years of treatment different among sites (2 – 9 years since start of treatment; Supplementary Table S1). For the nutrient additions, all sites applied 10 g N m-2 each year as time-release urea; 10 g P m-2 yr-1 as triple-super phosphate; 10 g K m-2 yr-1 as potassium sulfate. A micro-nutrient mix (Fe, S, Mg, Mn, Cu, Zn, B, Mo, Ca) was applied at 100 g m-2 together with K in the first year of treatments but not thereafter.
We excluded large vertebrate herbivores (Fence) by fencing two plots, one with and one without NPK additions, within each block. The fences excluded all aboveground mammalian herbivores with a body mass of over 50 g (Borer et al., 2014). At most sites, the fences were 180 cm high, and the fence contained a wire mesh (1 cm holes) for the bottom 90 cm with a 30 cm outward-facing flange stapled to the ground to exclude burrowing animals. Climbing and subterranean animals may potentially still access these plots (Borer et al., 2014). For slight modifications in fence design at a few sites see Supplementary Table S3. Most sites only had wild herbivores, although four sites were also grazed by domestic animals (Supplementary Table S1).

Collection of soil samples, soil microbial respiration, microbial biomass, and other soil properties
Each of the 23 sites received a package containing identical material from the Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland to be used for sampling (Risch et al., 2015; Risch et al., 2019). We collected two soil cores of 5 cm diameter and 12 cm depth in each sampling plot and composited them to measure MR, MBC, and soil chemical properties (see below). An additional sample (5 x 12 cm) was collected to assess soil physical properties. This sample remained within a steel sampling core after collection and both ends were tightly closed with plastic caps to avoid disturbance. All soils were shipped cooled to the laboratory at (Location will be disclosed after manuscript acceptance) within a few days after collection. Soils were sampled roughly 6 weeks prior to peak biomass at each site during 2015 and 2016.
To assess MR (CO2 production) in a laboratory incubation experiment we weighed duplicate soil samples (8 g dry soil equivalent) into 50-ml Falcon tubes. No additional substrate (glucose, sugar) was added to these samples. We adjusted the soil moisture of each sample to 60% field capacity. We then placed a 15 ml plastic test tube (Semadeni 1701A) containing 7.25 ml 0.05 M NaOH over each soil sample. The test tube was fixed with a plastic rod so that it was not in contact with the soil sample. The Falcon tubes were then sealed with a screw cap and placed in an incubator under completely dark conditions at 20°C. The CO2 produced by microbial respiration was absorbed by the 0.05 M NaOH. For five weeks we measured the decrease in conductivity within the 0.05 M NaOH solution on a weekly basis with a Multimeter WTW Multi 3410 (WTW GmbH, Germany) and replaced the 0.05 M NaOH with fresh solution. We included Falcon tubes without soil samples in each incubation run as blanks to test if tubes were tight and no CO2 could enter or escape. We calibrated the relationship between conductivity reduction and NaOH absorbed as follows: 400 ml 0.05 M NaOH was placed in a beaker and its conductivity was measured with the multimeter. While stirring, air containing CO2 was blown into the solution for approximately one minute, which reacts with NaOH to form Na2CO3. After this process, conductivity was measured again. We then transferred 7.25 ml of the solution into a smaller beaker and added 1 ml of 0.1 M BaCl2 to precipitate Na2CO3 and then titrated the solution with 0.05 M HCl to determine the remaining NaOH. We then repeated these steps with the remaining solution a total of nine times and plotted the conductivities (y-axis) against the NaOH consumed (x-axis, Supplementary Fig S1). This regression line was used to infer the consumption of NaOH from the conductivity reduction in the incubation experiments and to calculate CO2 evolution during incubation. In addition, we determined the optimum concentration for the NaOH solution in series of preliminary experiments, so that the concentration was not too high to become insensitive, but also not too low so that not all NaOH reacts during incubation. We then calculated MR (mg CO2-C kg dry soil-1 h-1) as total amount of CO2 released over the 5 weeks divided by the duration of the entire incubation in hrs.
Soil microbial biomass carbon (MBC; mg C kg soil-1 ) was measured at the beginning of the experiment by measuring the maximal respiratory response to the addition of glucose solution (4 mg glucose per g soil dry weight dissolved in distilled water; substrate-induced respiration method) on approximately 5.5 g of soil (J. P. E. Anderson & Domsch, 1978; Nico Eisenhauer et al., 2018; Scheu, 1992). For this purpose we used an O2-micro-compensation apparatus (Scheu, 1992). More specifically, substrate-induced respiration was calculated from the respiratory response to D-glucose for 10 hr at 20°C. Glucose was added according to preliminary studies to saturate the catabolic enzymes of microorganisms (4 mg g soil-1 dissolved in 400 ml deionized water). The mean of the lowest three readings within the first 10 hrs (between the initial peak caused by disturbing the soil and the peak caused by microbial growth) was taken as maximum initial respiratory response (MIRR; ml O2 kg soil-1 h-1) and microbial biomass (mg C kg soil-1) was calculated as 38 x MIRR (Beck et al., 1997; Cesarz et al., 2022; Thakur et al., 2015).
The rest of the composited sample was dried at 65°C for 48 h, ground and sieved (2 mm mesh) to assess the soil pH, mineral soil total C and N and C:N ratio, and mineral soil organic C (Risch et al., 2019). The undisturbed sample was used to assess water holding capacity (WHC), bulk density (BD), and soil texture [sand, silt, clay; methods in (Risch et al., 2019)]. We used the percentage of sand and clay as an indicator of soil texture in this study. MAT (°C), MAP (mm) and temperature of the wettest quarter (°C) were obtained from www.worldclim.com (Fick & Hijmans, 2017; Hijmans, Cameron, Parra, Jones, & Jarvis, 2005). These variables were selected as they were found to be drivers of soil nutrient processes across these sites in earlier studies (Risch et al., 2020; Risch et al., 2019). Mean annual soil temperatures (MAST; °C) for the 0 to 5 cm soil layer were obtained for each site from the SoilTemp maps (J. Lembrechts et al., 2021; J. J. Lembrechts et al., 2022), global gridded modelled products of soil bioclimatic variables for the 1979-2013 period at a 1-km² resolution, based on CHELSA, ERA5 and in-situ soil temperature measurements.
Numerical calculations and statistical analyses
We calculated MMQ as MR/MBC. We corrected this measure using the average soil temperature of each site (MMQsoil). This temperature correction is necessary as incubation temperatures are usually much higher than site mean annual soil temperatures (see Xu et al. 2017). MMQsoil = MMQ x Q10(MAST – 20)/10, where Q10 was assumed to be 2 (Xu et al. 2017). See Supplementary Fig S2 for comparison of air and soil temperatures across the 23 sites as well as the incubation temperature.
Some of the explanatory variables (clay, soil organic C, C:N ratio) were skewed and were thus log-transformed prior to analyses. All continuous explanatory variables were centred and scaled to have a mean of zero and variance of one. To avoid collinearity between them we filtered them using correlation analysis (Supplementary Fig S3). From the variables that were strongly correlated (Pearson’s |r| > 0.70) (Dormann et al., 2013), we selected the ones that allowed us to minimize the number of variables (Supplementary Fig S3). Specifically, soil total N concentration, soil total C concentration, soil sand content and soil bulk density were dropped from the dataset. We then assessed how these edaphoclimatic variables are related to MMQ across our global grasslands.
For this, we used linear mixed effects models (LMMs) fitted by maximum likelihood with the lme function in the nlme package (version 3.1-153) (Pinheiro, Bates, DebRoy, & Sarkar, 2021) in R version 3.6.3. (R Core Team, 2019). We used treatment as a fixed effect and plot nested in site as random effects to assess treatment differences in MMQsoil, as well as MR, and MBC. The number of years since the treatment started was included as a fixed effect in all the initial models but was not significant and therefore not retained in the models. To assess how differences in MMQsoil were affected by environmental factors (soil, climatic properties) we again used LMMs. Soil and climatic properties were included as fixed effects and plot nested in site as random effects. We did not include interactions between environmental variables. We then used the MuMin package (Barton, 2018) (version 1.42.1) to select the best models that explained the most variation based on Akaike’s information criterion (AIC; model.avg function). We used the corrected AIC (AICc) to account for our small sample size and selected the top models that fell within 4 AICc units (delta AICc < 4) (Burnham & Anderson, 2002; Johnson & Omland, 2004). We present all our top models rather than model averages. Conditional averages are provided in the Supplementary material.
Based on findings from analyses described above and the literature, we developed a conceptual model of direct and indirect relationships between both edaphoclimatic variables and experimental treatments (Supplement Figure S4) to obtain a more holistic approach in understanding how these properties affect MMQsoil. We had data from 23 sites with 272 observations. We tested this model using structural equation modelling based on a d-sep approach (Lefcheck, 2016; Shipley, 2009). We considered those environmental drivers that were included in our top LMMs, namely temperature of the wettest quarter (T.q.wet), soil pH, water holding capacity (WHC) and soil organic C (organic C; Supplementary Figure S4). These factors were allowed to directly affect MMQsoil, and via their interactions with treatments. In addition, treatments were allowed to directly affect MMQsoil. Treatments were included as dummy variables in the model. We tested our conceptual model (Supplementary Fig S4) using the piecewiseSEM package (version 2.0.2; Lefcheck, 2016) in R 3.4.0, in which a structured set of linear models are fitted individually. This approach allowed us to account for the nested experimental design, and overcome some of the limitations of standard structural equation models, such as small sample sizes (Lefcheck, 2016; Shipley, 2009). We used the lme function of the nlme package to model response variables, including site as a random factor. Good fit of the SEM was assumed when Fisher’s C values were non-significant (p > 0.05). For all significant interactions between soil or climate variables and treatments detected in the SEMs, we calculated treatment effect sizes, i.e., the differences in MMQsoil between Control and treatments as log response ratios (LRR) and plotted these values against the climate or soil factors. The LRR were defined as log(Control/Treatment), where treatment was either Fence, NPK or NPK+Fence. To assess which of the LRR-climate or soil property relationships were significant we again used LMMs, in which soil and climatic properties were included as fixed effects and plot nested in site as random effects.

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

Paper Citation:

Risch Anita C., Zimmermann, Stefan, Schütz, Martin, Borer, Elizabeth T., Broadbent, Arthur A.D., Caldeira, Maria C., Davies, Kendi F., Eisenhauer, Nico, Eskelinen, Anu, Fay, Philip A., Hagedorn, Frank, Knops, Johannes M.H., Lembrechts, Jonas, J., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Seabloom, Eric W., Silveira, Maria L., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Drivers of the microbial metabolic quotient across global grasslands. Global Ecology and Biogeography

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

The microbial metabolic quotient (MMQ; mg CO2-C mg MBC-1 h-1), defined as the amount of microbial CO2 respired (MR; mg CO2-C kg soil-1 h-1) per unit of microbial biomass C (MBC; mg C kg soil-1), is a key parameter for understanding the microbial regulation of the carbon (C) cycle, including soil C sequestration. Here, we experimentally tested hypotheses about the individual and interactive effects of multiple nutrient addition (NPK+micronutrients) and herbivore exclusion on MR, MBC, and MMQ across 23 sites (5 continents). Our sites encompassed a wide range of edaphoclimatic conditions, thus we assessed which edaphoclimatic variables affected MMQ the most and how they interacted with our treatments. Soils were collected in plots with established experimental treatments. MR was assessed in a five-week laboratory incubation without glucose addition, MBC via substrate-induced respiration. MMQ was calculated as MR/MBC and corrected for soil temperatures (MMQsoil). Using LMMs and SEMs, we analysed how edaphoclimatic characteristics and treatments interactively affected MMQsoil. MMQsoil was higher in locations with higher mean annual temperature, lower water holding capacity, and soil organic C concentration, but did not respond to our treatments across sites as neither MR nor MBC changed. We attributed this relative homeostasis to our treatments to the modulating influence of edaphoclimatic variables. For example, herbivore exclusion, regardless of fertilization, led to greater MMQsoil only at sites with lower soil organic C (<1.7%). Our results pinpoint the main variables related to MMQsoil across grasslands and emphasize the importance of the local edaphoclimatic conditions in controlling the response of the C cycle to anthropogenic stressors. By testing hypotheses about MMQsoil across global edaphoclimatic gradients, this work also helps to align the conflicting results of prior studies.

anthropogenic-management

display_name: anthropogenic-management

climate

display_name: climate

herbivore-exclusion

display_name: herbivore-exclusion

microbial-biomass-carbon

display_name: microbial-biomass-carbon

microbial-respiration

display_name: microbial-respiration

nutrient-addition

display_name: nutrient-addition

soil-properties

display_name: soil-properties


en: antarctica topography wrf cryowrf landuse

en: Scripts

en: Scripts to run the preprocessing to produce WRF binary tiles from REMA and AntarcticaLC2000 for topography and landuse (see Documentation for procedure).

en: Documentation

en: Documentation about how the provided dataset was produced including details to reproduce the dataset.

en: WPSinput

en: Input files to run the Weather Research and Forecasting model (WRF) preprocessing system WPS with REMA topography and AntarcticaLC2000 landuse data (see Documentation for details).

en:
Reference Elevation Model of Antarctica (REMA) topography and AntarcticaLC2000 landuse data
are now available as static data input for the Weather Research and Forecasting model (WRF). Topography
and landuse are made available at a spatial resolution of 1 km. This documentation describes the methods
applied to convert REMA and AntarcticaLC2000 to WRF readable format and shows how this improves
the representation of the Antarctic topography and landuse categories over coastal Antarctic regions.

antarctica

display_name: antarctica

cryowrf

display_name: cryowrf

landuse

display_name: landuse

topography

display_name: topography

wrf

display_name: wrf

en: REMA topography and AntarcticaLC2000 for WRF


en: wind-direction raclets wind-speed wind-velocity wind anemometer

en: Sonic wind component v

en:
The data contains values for v direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: Sonic temperature

en: The data set contains values of temperature (°C) measured with the anemometer at a sampling frequency of 20 Hz.

en: Sonic wind component u

en:
The data contains values for u direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: Sonic wind component w

en:
The data contains values for w direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: Sonic time

en: The data set contains the time in YYYY-mm-dd HH:MM:SS.sss for the anemometer data. The time is given in UTC+1.

en: A Young 81000 sonic anemomenter was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849) to record three components of the wind velocity (u, v, w in [m s‾ ¹]) and air temperature (Ts in [°C]). The anemomenter was mounted in direction North at a height of 1.5 m above snow surface at the beginning. The time within each data set is given in UTC+1. Instrument specifications can be found [here](http://www.youngusa.com/Manuals/81000-90(I).pdf) .

anemometer

display_name: anemometer

raclets

display_name: raclets

wind

display_name: wind

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wind-velocity

display_name: wind-velocity

educ

en: Education, culture and sport

en: Snow Drift Station - 3D Ultrasonic


maintainer: Marco Moretti

en: urbanization sustainability honeybees remote-sensing urban-biodiversity pollination

en: Not used:

en: Not used for the analyses and not link to the paper.

en: Dataset 2

en: Dataset 2: Processed data for modelling balance urban greenspace

en: Dataset 1

en: Dataset 1: Processed data for urban beekeeping 2012 & 2018

en: Dataset 3

en: Dataset on the the number of beehives and apiaries on the years 2012, 2013, 2014, 2015, 2016, 2017, 2018 in several Swiss cities.

en: Data on: (1) (Dataset 1) spatial distribution of urban beekeeping (number of hives and number of beekeeping locations) in 14 Swiss cities (Geneva, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) for the period 2012-2018; (2) (Dataset 2) aggregated data to model the sustainability of urban beekeeping.

honeybees

display_name: honeybees

pollination

display_name: pollination

remote-sensing

display_name: remote-sensing

sustainability

display_name: sustainability

urban-biodiversity

display_name: urban-biodiversity

urbanization

display_name: urbanization

envidat

en: EnviDat: the Environmental Data Portal

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

en: Challenging the sustainability of urban beekeeping: evidence from Swiss cities


en: soil-mulitfunctionality prokaryotes microbial-biodiversity soil-microbiome fungi latitudinal-gradient

en: ITS_Han et al_EUgradient.xlsx

en: Diversity of fungi (ITS region sequencing) containing: 1) ASV (Amplicon Sequence Variants) table: abundance of ASV in each sample and 2) Taxonomy table: taxonomic classification of each ASV.

en: 16S_Han et al_EUgradient.xlsx

en: Diversity of prokaryotes (16S gene sequencing) containing: 1) ASV (Amplicon Sequence Variants) table: abundance of ASV in each sample and 2) Taxonomy table: taxonomic classification of each ASV.

en: Metadata, soil properties and soil microbial functions for each sites across the European gradient.

en: The diversity and network complexity of prokaryotic and fungal communities and their relationships with soil multifunctionality (SMF) – an integrative index for C-, N- and P-cycling functions – was investigated along a 3,000-km latitudinal transect across Europe (37° to 62°N), spanning biomes from Mediterranean drylands, temperate to boreal forests.

fungi

display_name: fungi

latitudinal-gradient

display_name: latitudinal-gradient

microbial-biodiversity

display_name: microbial-biodiversity

prokaryotes

display_name: prokaryotes

soil-microbiome

display_name: soil-microbiome

soil-mulitfunctionality

display_name: soil-mulitfunctionality

en: Soil microbial diversity and multifunctionality across European biomes


maintainer: Lisa Hülsmann

en: primeval-forests forest-reserves forest-inventory tree-mortality mortality-models fagus-sylvatica

en: Swiss data full

en:
READ ME for

Hülsmann L, Bugmann H, Commarmot B, Meyer P, Zimmermann S, Brang P
Data from: Does one model fit all? Patterns of beech mortality in natural forests of three European regions.
Ecological Applications http://dx.doi.org/10.5061/dryad.h4s6t

Forest inventory data of beech for the development of individual-tree mortality models from strict forests reserves in Switzerland, Germany and Ukraine.

The data package provides the files
CH_reduced: inventory data of the reduced dataset from Switzerland that was used to assess the influence of additional environmental and stand characteristics on beech mortality
CH: inventory data from strict forest reserves in Switzerland
DE: inventory data from strict forest reserves in Germany
UA: inventory data from a primeval beech forest in Ukraine

A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory.
Diameter at breast height: dbh (mm)
Annual relative basal area increment: relBAI (), calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area (Bigler and Bugmann 2004)
Status of the tree: y, 0 = alive, 1 = dead
Length of the mortality period: dt (years)

Additional environmental and stand characteristics were derived for the reduced Swiss dataset.
Note the varying seasonal level of the covariates considering the entire year, the growing season from April to September (G) and the winter month January to March (W).
All climatic covariates were calculated for both the ‘growth’ (2) and the ‘mortality’ period (3). The stand covariates were derived for the second inventory of each record (2).

Mean diameter at breast height: dbh.mean.2 (mm)
Interquartile range of diameter at breast height: dbh.IQR.2 (mm)
Median diameter at breast height: dbh.median.2 (mm)
Basal area per hectare: BA.2 (m2 ha-1)
Number of trees per hectare: N.2 (ha-1)
pH-value: pH ()
Available water capacity in the soil: AWC (mm)
Mean annual precipitation sum: P.2, P.3 (mm)
Mean precipitation sum during growing season: PG.2, PG.3 (mm)
Mean annual air temperature: mT.2, mT.3 (°C)
Mean air temperature during growing season: mTG.2, mTG.3 (°C)
Mean air temperature during winter months: mTW.2, mTW.3 (°C)
Mean growing degree-days: GDD.2, GDD.3 ()
Mean number of months during growing season with water deficit: mDEFdurG.2, mDEFdurG.3 ()
Mean annual maximum water deficit: mDEFmax.2, mDEFmax.3 (mm)
Overall maximum of the annual maximum water deficit: maxDEFmax.2, maxDEFmax.3 (mm)
Mean of cumulative water deficit during growing season: mDEFcumG.2, mDEFcumG.3 (mm)
Overall maximum of cumulative water deficitduring growing season: maxDEFcumG.2, maxDEFcumG.3 (mm)
Percentage of years with at least one month water deficit: percDEF.2, percDEF.3 (%)

en: Ukrainian data

en: Swiss data reduced

en: German data

en:
The datasets comprise nearly 19’000 trees of European beech (_Fagus sylvatica_ L.) from unmanaged forests in Switzerland, Germany / Lower Saxony and Ukraine. Tree death was modelled as a function of size and growth, i.e., stem diameter (DBH) and relative basal area increment (relBAI). To explain the spatial and temporal variability in mortality patterns, we considered a large set of environmental and stand characteristics.

 Inventory data
The strict forest reserves in Switzerland and Germany had been established in the period of 1961-1975 and 1971-1974, respectively. Every reserve included up to 10 permanent plots ranging from 0.09 to 1.8 ha in size, with slightly irregular re-measurement intervals. Permanent plots with pure or mixed beech stands were selected from the reserves of both networks. Reserves with considerable wind disturbance during the monitored intervals were excluded from the analysis. In addition to data from the Swiss and German reserves, data from a 10 ha plot in the primeval beech forest Uholka in Western Ukraine including three remeasurements were used. The inventory data provide diameter measurements at breast height (dbh) for revisited trees with a diameter of more than 4, 7 and 6 cm for Switzerland, Germany and Ukraine, respectively.

 Mortality predictors
A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory. As an explanatory variable, the annual relative basal area increment (relBAI) was calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area. Tree dbh in the second inventory was used in addition to relBAI to model tree status (alive or dead) of the third inventory.

To increase the generality of the mortality models, we selected environmental variables that are known to have a considerable influence on growth and mortality of beech. We emphasized the effects of water availability using a large set of drought characteristics that were calculated based on the local site water balance. We also related beech mortality to soil pH, temperature, precipitation and growing degree-days. Additionally, we considered stand characteristics that reflect the development stage, competition and structure of the forests.

 Further information
For further information, refer to Hülsmann _et al_. (2016) Does one model fit all? patterns of beech mortality in natural forests of three European regions. _Ecological Applications_.

fagus-sylvatica

display_name: fagus-sylvatica

forest-inventory

display_name: forest-inventory

forest-reserves

display_name: forest-reserves

mortality-models

display_name: mortality-models

primeval-forests

display_name: primeval-forests

tree-mortality

display_name: tree-mortality

educ

en: Education, culture and sport

en: Data from: Does one model fit all? patterns of beech mortality in natural forests of three European regions


maintainer: Virginie Marques

en: edna marine mediterraneaen marseille teleo fish-composition

en: Data access

en: Please use cyberduck to download for the moment

en:
Description: Fish environmental DNA data set collected in 2022 in the Calanques National Park
The eDNA samples were collected in 2022 in two locations (Moyades, M−FPA and “Impérial du large”, I-LPA), during the winter (January- February), the summer (June to August)and fall (mid-September to November) seasons, with the sampling dates depending on the weather conditions. For the M−FPA, samples were collected between Moyades island and Riou island, while for the I-LPA, they were collected on the south side of the “Impérial du large” island. To account for the existing bathymetry, in the M−FPA the samples were taken at two sampling sites with depths of 20 and 40 m, while in the I-LPA they were taken at two sites with depths of 20 and 80 m.
At each site, in-situ filtration of seawater was performed using a double-head submersible pump (Subspace, Geneva, Switzerland; nominal flow of ca. 1 L/min) strapped to an underwater scooter with 2 VigiDNA 0.20 µm filtration capsules (SPYGEN, le Bourget du Lac, France), along with disposable sterile tubing. The samples were collected along two horizontal transects (up to 400 m in length) during each closed-circuit rebreather dive, enabling the filtration of a water volume of 15 L/filter per depth, as close as possible to the substrate. Two filter replicates were collected by two divers at each sampling site, except in two cases where bad weather conditions or logistical issues meant that only one replicate was sampled.
After the filtration, the remaining seawater was emptied from the capsule back on the boat and replaced by a 80 mL CL1 conservation buffer (SPYGEN, le Bourget du Lac, France). To prevent any contamination, a strict protocol was followed during the entire process, requiring disposable gloves and single-use filtration equipment. Finally, the samples were stored at room temperature. We followed a strict contamination control protocol in both field and laboratory stages. Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

edna

display_name: edna

fish-composition

display_name: fish-composition

marine

display_name: marine

marseille

display_name: marseille

mediterraneaen

display_name: mediterraneaen

teleo

display_name: teleo

envidat

en: EnviDat: the Environmental Data Portal

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

en: Environmental DNA Marine France Calanques 2022


maintainer: Elisabeth Hafner

en: snow satellite avalanche-hazard-mapping validation avalanche

en: Satellite Avalanche Mapping Validation data and data description

en: 14 shapefiles to be used only together with the data description also provided as a pdf.

en:
Validation points, validation area, ground truth coverage, SPOT 6 avalanche outlines, Sentinel-1 avalanche outlines, Sentinel-2 avalanche outlines, Davos avalanche mapping (DAvalMap) avalanche outlines as shapefiles and a detailed attribute description (DataDescription_EvalSatMappingMethods.pdf).

Coordinate system: CH1903+_LV95

The generation of this dataset is described in detail in:

Hafner, E. D., Techel, F., Leinss, S., and Bühler, Y.: Mapping avalanches with satellites – evaluation of performance and completeness, The Cryosphere, https://doi.org/10.5194/tc-2020-272, 2021.

avalanche

display_name: avalanche

avalanche-hazard-mapping

display_name: avalanche-hazard-mapping

satellite

display_name: satellite

snow

display_name: snow

validation

display_name: validation

en: Satellite avalanche mapping validation data


en: spatially-distributed-sampling stable-water-isotopes precipitation stream-water geochemical-solutes

en: The dataset contains daily measurements of volumes, isotopes and solutes of stream water as well as precipitation measured several locations; meteorological variables are added. Date: 18.07.2019

en:
This dataset contain measurements of solute and stable water isotopes in stream water and precipitation in the Alp catchment and two of its tributaries (between 2015 -2018) . The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l. The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (precipitation rates, discharge) at daily intervals from several sampling locations.
An updated version of the isotope dataset is available here: https://www.doi.org/10.16904/envidat.242

geochemical-solutes

display_name: geochemical-solutes

precipitation

display_name: precipitation

spatially-distributed-sampling

display_name: spatially-distributed-sampling

stable-water-isotopes

display_name: stable-water-isotopes

stream-water

display_name: stream-water

en: Daily data of solute and stable water isotopes in stream water and precipitation in the Alp catchment, Central Switzerland


maintainer: Alexis Shakas

en: irrigation ert drone-data drought pinus-sylvestris

en: This zip file contains all raw and processed data for the electrical resistivity and drone campaigns that took place in Pfynwald, 2022

en: This .csv file contains the soil water potential data for all treatments (irrigation, irrigation-stop and control) and for the whole year of 2022. The data is given as a mean value and with an upper and lower uncertainty bound, given from the manufacturer. The depth of the sensor is given in cm, and the measured values are in units of kPa.

en:
This collection of datasets consists of various measurements taken during the year 2022 in Pfynwald. It combines 2 electrical resistivity transects which were monitored in May and July, before and after the irrigation season. The transects transverse all treatments (irrigation, control, irrigation stop). Each transect was repeated twice during the day for a period of 3 days to a week.
In addition there are raw and post-processed drone images (and resulting PRI maps), which were used to compare the below ground responses (from resistivity) to the above ground (crown) stress.
Here, the raw data is stored and a link to a git project is provided where python code is stored to reproduce all the results the published manuscript: "Does optimality partitioning theory fail for belowground traits? Insights from geophysical imaging of a drought-release experiment in a dry Scots Pine forest", New Phytologist, Shakas et al., 2024. The in-depth explanations from each processing step are found in the code (git project).

drone-data

display_name: drone-data

drought

display_name: drought

ert

display_name: ert

irrigation

display_name: irrigation

pinus-sylvestris

display_name: pinus-sylvestris

educ

en: Education, culture and sport

en: Pfynwald Geoelectric Experiment 2022


maintainer: Roman Zweifel

en: forest-biomass atmospheric-conditions ecosystem-functioning carbon-storage metadata carbon-dioxide forest-ecosystems ilter stem-growth dendrometer icp-forests carbon-cycle automated-monitoring davos forest-canopy dbh biomass lwf seehornwald climate-change water-relations forest-dynamics subalpine carbon-sequestration eddy-covariance-measurements flux icos ecophysiology sap-flow eddy-covariance twd tree-stem climate-growth treenet picea-abies forest-characteristics co2 forest-function forest-area forest open-data forest-monitoring

en: Collection of files with information about the research site Seehornwald Davos CH-DAV

en: Readme

en: Readme file contains the list of material that comes with the download

en: Site description of the Seehornwald Davos (CH-DAV) research site. The material provided offers information on the biomass and growth of trees in CH-DAV, including supporting materials such as maps, geolocations and analyses. CH-DAV is part of networks such as ICOS, LWF, TreeNet, ICPForests and is dedicated to forest monitoring and research.

atmospheric-conditions

display_name: atmospheric-conditions

automated-monitoring

display_name: automated-monitoring

biomass

display_name: biomass

carbon-cycle

display_name: carbon-cycle

carbon-dioxide

display_name: carbon-dioxide

carbon-sequestration

display_name: carbon-sequestration

carbon-storage

display_name: carbon-storage

climate-change

display_name: climate-change

climate-growth

display_name: climate-growth

davos

display_name: davos

dbh

display_name: dbh

dendrometer

display_name: dendrometer

ecophysiology

display_name: ecophysiology

ecosystem-functioning

display_name: ecosystem-functioning

eddy-covariance

display_name: eddy-covariance

eddy-covariance-measurements

display_name: eddy-covariance-measurements

flux

display_name: flux

forest

display_name: forest

forest-area

display_name: forest-area

forest-biomass

display_name: forest-biomass

forest-canopy

display_name: forest-canopy

forest-characteristics

display_name: forest-characteristics

forest-dynamics

display_name: forest-dynamics

forest-ecosystems

display_name: forest-ecosystems

forest-function

display_name: forest-function

forest-monitoring

display_name: forest-monitoring

icos

display_name: icos

icp-forests

display_name: icp-forests

ilter

display_name: ilter

lwf

display_name: lwf

metadata

display_name: metadata

open-data

display_name: open-data

picea-abies

display_name: picea-abies

sap-flow

display_name: sap-flow

seehornwald

display_name: seehornwald

stem-growth

display_name: stem-growth

subalpine

display_name: subalpine

tree-stem

display_name: tree-stem

treenet

display_name: treenet

twd

display_name: twd

water-relations

display_name: water-relations

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: Site description Davos Seehornwald CH-DAV


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

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

forest-inventory

display_name: forest-inventory

lidar

display_name: lidar

mobile-laser-scanning

display_name: mobile-laser-scanning

structure-from-motion

display_name: structure-from-motion

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

terrestrial-photogrammetry

display_name: terrestrial-photogrammetry

unmanned-aerial-vehicle-laser-scanning

display_name: unmanned-aerial-vehicle-laser-scanning

en: EnviDat: the Environmental Data Portal

en: Ramerenwald Close Range Remote Sensing Benchmark


maintainer: Adam Winstral

en: snow-simulation snow-depth snow jules-investigation-model jim

en: HS and SWE observations

en: Automated (HS) and manual (SWE) data used to evaluate model simulations

en: Station forcing data

en: All forcing data need to run JIM at HS and SWE stations. These are data downscaled directly from COSMO NWP data (WY14, WY15) and further enhanced by additional station observations of snow depth (HS) and air temperature (Ta) in WY17. Further information can be found in README file. Note that in WY14 and WY15, shortwave radiation was not split into direct and diffuse (not necessary for JIM) and that the default method in JIM for splitting precipitation phase was used. In WY 17, direct and diffuse were combined as input to JIM and JIM was forced with pre-processed liquid and solid precipitation phases.

en: File/data descriptor

en: DEM

en: 1-km resolution DEM used for downscaling gridded NWP data to stations and for distributing BDE evaluations at HS-stations to SWE-stations

en: These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the "deterministic" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations.

jim

display_name: jim

jules-investigation-model

display_name: jules-investigation-model

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-simulation

display_name: snow-simulation

en: Meteorological data used to develop and validate the bias-detecting ensemble (BDE)


maintainer: EnviDat Support

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

en: Forest station data (LAB)

en: Data file.

en: Field station data (LAF)

en: Site photo of the forest station (LAB)

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

en: Site photo of the field station (LAF)

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

en: Data plot of the field station (LAF)

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

en: Site aerial photo (Lausanne)

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

en: Data plot of the forest station (LAB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Lausanne in Switzerland where one station is located within a natural mixed forest stand (LAB) with European beech (_Fagus sylvatica_; 160-170 yrs), European silver fir (_Abies alba_; 160-170 yrs) and Norway spruce (_Picea abies_; 160-170 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, LAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lausanne is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

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


en: intercomparison snow microct icecube specific-surface-area

en: Statistics of samples

en: IceCube and MicroCT data

en: The file contains all micro-CT and IC measurements for snow types A, B, C, D and E used in our study. All samples were processed (and stored) in a cold laboratory at -15°C.

en: TARTES simulations

en: Jupyter notebook with TARTES simulations.

en: SMP measurement files for snow types A, B, C and D.

en: Grain survey of snow particles

en: The file contains all measurements of the artificial particles for snow types A, B, C, D and E, including the artificial particle mass in the Petri dish.

en: artificial_snow_data set

en: The file contains all micro-CT and IC measurements of the artificial snow data set. The data set was originally produced to investigate the influence of black carbon on SSA measurements.

en: Influence of black carbon on the measurement of the specific surface of snow with the near infrared method - Philipp Baumann

en: These experiments investigate different possible errors by measuring the specific surface area (SSA) of snow with the near infrared method. The influence of black carbon on this measurement was disproved, the theory was therefore validated. It shows that sunlight has no influence on the measurement with the IceCube. Wind drift snow as thin layer on the sample can increase the measured value of the specific surface by a few percent. This could be part of the error, observed in former measurements at SLF. As reference for the measured SSA values with the near infrared method, computer tomography was used to verify the SSA values.

en: The file contains all data for the plot in figure 1 of the co-located density and SSA measurement at Summit, Greenland in May 2015, conducted by Schneebeli, Proksch, Schmid.

en: The specific surface area (SSA) of different snow types were measured with the IceCube instrument and the Scanco Medical microCT 40. In addition, the snow particles created during the preparation of IceCube samples were counted. The difference in SSA between these instruments is explained by the formation of the surface particles. A numerical simulation using TARTES simulation support the observations.

icecube

display_name: icecube

intercomparison

display_name: intercomparison

microct

display_name: microct

specific-surface-area

display_name: specific-surface-area

envidat

en: EnviDat: the Environmental Data Portal

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


en: methoxy-groups carbohydrates isotopes tree-species common-garden

en: The resource contains a metadata sheet explaning the variables, as well as a sheet with the actual species and isotope data.

en: This dataset contains hydrogen and carbon isotope data from 73 tree and shrub species grown in a common garden (Kannenfeldpark) in Basel, Switzerland. Leaf and twig samples were collected in August 2020. The isotope measurements cover various organic compounds, including sugars, cellulose, and (lignin) methoxy groups, as well as leaf and twig xylem water. Details on sample preparation and isotope analysis are provided in Schuler et al. (2023) for carbohydrates and water, and in Wieland et al. (in review) for methoxy groups.

carbohydrates

display_name: carbohydrates

common-garden

display_name: common-garden

isotopes

display_name: isotopes

methoxy-groups

display_name: methoxy-groups

tree-species

display_name: tree-species

educ

en: Education, culture and sport

en: Stable isotope data from 73 tree and shrub species in a common garden in Basel Switzerland