en: land-use-modelling pathways scenarios land-change agriculture

en: This dataset contains the spatial layers showing future land change pathways in Europe between 2015 and 2050, as simulated by the CLUMondo model according to the Agricultural Futures Framework (AFF) scenarios.

en:
This dataset contains the spatial layers showing future land change pathways in Europe between 2015 and 2050, as simulated by the CLUMondo model and according to the Agricultural Futures Framework (AFF) scenarios.

Three scenarios are included:
- Land for Food and Land for Nature (LFLN)
- Land as Culture (LaC)
- Land for Society (LfS)

agriculture

display_name: agriculture

land-change

display_name: land-change

land-use-modelling

display_name: land-use-modelling

pathways

display_name: pathways

scenarios

display_name: scenarios

en: AFF agricultural land change scenarios in Europe 2015-2050


present-weather-sensor-klosters

maintainer: Maxime Hervo

title_for_slug: present-weather-sensor-klosters

en: precipitation sensor visibility raclets weather

state: active

type: dataset

en: Present Weather Sensor Klosters

en: Present Weather sensor measuring visibility and weather type installed at Klosters.

format: SERVICE

en: A present weather sensor (Vaisala PWD22) was deployed in Klosters (LON: 9.880413, LAT: 46.869019) for weather observation, combining the functions of a forwardscatter visibility meter and a present weather sensor. Besides measuring ambient light, it detects the intensity as well as the amount of both liquid and solid precipitation. More information can be found in the [User's Manual](ftp://ftp.cmdl.noaa.gov/aerosol/doc/manuals/PWD22_Manual.pdf).

precipitation

display_name: precipitation

raclets

display_name: raclets

sensor

display_name: sensor

visibility

display_name: visibility

weather

display_name: weather

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


maintainer: Esther R. Frei

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

media_type: text/plain

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

format: TXT

mimetype: text/plain

en: GTREE Website

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

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

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

climate-change

display_name: climate-change

forest

display_name: forest

g-tree

display_name: g-tree

larix-decidua

display_name: larix-decidua

picea-abies

display_name: picea-abies

seedling-recruitment

display_name: seedling-recruitment

stillberg

display_name: stillberg

swiss-alps

display_name: swiss-alps

treeline

display_name: treeline

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


en: fish-composition chondrichthyes atlantic-region teleo edna actinopterygii bay-biscay dna-metabarcoding

en: Data access

en:
Environmental DNA complements scientific trawling in surveys of marine fish biodiversity (Dataset 2020)

In October 2020 we chose 16 sites from the 2020 EVHOE survey for eDNA sampling. The French international EVHOE bottom trawl survey is carried out annually during autumn in the BoB to monitor demersal fish resources. All sites were located on the continental shelf (26–170 m depth), except one on the upper slope with a depth of 1045 m. To perform eDNA sampling, we collected water samples at 15 sites (Figure 1a). At each site, we sampled seawater using Niskin bottles deployed with a circular rosette. There were nine bottles on the rosette, each of them able to hold ∼5 l of water. At each site, we first cleaned the circular rosette and bottles with freshwater, then lowered the rosette (with bottles open) to 5 m above the sea bottom, and finally closed the bottles remotely from the boat. The 45 l of sampled water was transferred to four disposable and sterilized plastic bags of 11.25 l each to perform the filtration on-board in a laboratory dedicated to the processing of eDNA samples. To speed up the filtration process, we used two identical filtration devices, each composed of an Athena® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.0 l min–1 ), a VigiDNA 0.20 μm filtration capsule (SPYGEN, le Bourget du Lac, France), and disposable sterile tubing. Each filtration device filtered the water contained in two plastic bags (22.5 l), which represent two replicates per sampling site. We followed a rigorous protocol to avoid contamination during fieldwork, using disposable gloves and single-use filtration equipment and plastic bags to process each water sample. At the end of each filtration, we emptied the water inside the capsule that we replaced by 80 ml of CL1 conservation buffer and stored the samples at room temperature following the specifications of the manufacturer (SPYGEN, Le Bourget du Lac, France). We processed the eDNA capsules at SPYGEN, following the protocol proposed by Polanco-Fernández et al., (2020). We performed library preparation and sequencing at Fasteris (Geneva, Switzerland). Specifically, we prepared four libraries using the MetaFast protocol (a ligation-based method) and sequenced them separately. We carried out paired-end sequencing using a MiSeq sequencer (2 × 125 bp, Illumina, San Diego, CA, USA) on two MiSeq Flow Cell Kits (v3; Illumina), following the manufacturer’s instructions. We analysed the sequence reads using the OBITools package (http:
//metabarcoding.org/obitools; Boyer et al., 2016), following the protocol described by Valentini et al. (2016).

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)

actinopterygii

display_name: actinopterygii

atlantic-region

display_name: atlantic-region

bay-biscay

display_name: bay-biscay

chondrichthyes

display_name: chondrichthyes

dna-metabarcoding

display_name: dna-metabarcoding

edna

display_name: edna

fish-composition

display_name: fish-composition

teleo

display_name: teleo

en: Environmental DNA Marine France Evhoe 2020


maintainer: EnviDat Support

en: snow python vapor-transport numerical-model pattern-formation

media_type: application/zip

en:
Data and code v1.0 downloaded from the git.

Running the code requires an installation of FEniCS via docker.

format: ZIP

en:
This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format.

The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication:

_Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical
instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_

The code and potential updates can be accessed directly through git via:

https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics

numerical-model

display_name: numerical-model

pattern-formation

display_name: pattern-formation

python

display_name: python

snow

display_name: snow

vapor-transport

display_name: vapor-transport

en: A numerical solver for heat and mass transport in snow based on FEniCS


maintainer: Michael Lehning

en: snow-distribution snow snow-hydrology alpine-precipitation alpine3d alpine3d-simulations snow-cover

en: ADS datasets and simulation inputs

en: The file contains the ADS datasets of snow depth and input data used for simulations.

mimetype: application/zip

en:
Dataset (Model input, snow distribution and validation) for the precipitation scaling paper, which should be cited along with the data set citation.

This data is useful for distributed hydrological modelling or other tasks that involve the study of snow distribution and precipitation in the high Alpine.

The format of the data is for Alpine3D (models.slf.ch) model runs but other models could be used, too.

Please cite:

_Vögeli, C., Lehning, M., Wever, N., Bavay M., 2016: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution., Front. Earth Sci. 4: 108. doi: 10.3389/feart.2016.00108._

Dataset is provided as a single zip file.
The archive contains two directories, the valuable distributed snow depth maps for the landscape Davos and the simulation input.
The archive also contains the file: "ReadMeMetadataDataSetPrecipitationScaling" which explains the data structure.

alpine-precipitation

display_name: alpine-precipitation

snow-cover

display_name: snow-cover

snow-distribution

display_name: snow-distribution

snow-hydrology

display_name: snow-hydrology

en: Precipitation Scaling Data Set (Vögeli et al., Frontiers)


en: splash large-eddy-simulation aerodynamic-entrainment saltation-parameterizations snow-saltation drifting-snow

en: Code and input files to reproduce the simulations in Melo et al. (2024), as well as the post-processing scripts used in the analysis.

en:
This dataset includes the necessary files to reproduce the results presented in Melo et al. (2024):
- code version used in the simulations (also available at the institutional [GitLab repository](https://gitlabext.wsl.ch/atmospheric-models/les-lsm) - tag 'r2024')
- input files to run the simulations
- post-processing scripts (MATLAB and FORTRAN)
- selected raw output files
- post-processed results (MATLAB data files).
 	**Note:** Direct model outputs are not provided because of their size, but they can be shared upon request. They can be reproduced by running the code. The post-processed results allow you to reproduce all figures in Melo et al. (2024).

aerodynamic-entrainment

display_name: aerodynamic-entrainment

drifting-snow

display_name: drifting-snow

large-eddy-simulation

display_name: large-eddy-simulation

saltation-parameterizations

display_name: saltation-parameterizations

snow-saltation

display_name: snow-saltation

splash

display_name: splash

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: Understanding snow saltation parameterizations - Reproducibility


maintainer: Frank Techel

en: switzerland norway avalanche-observations avalanche-danger extended-column-test rutschblock-test

en: avalanche occurrence data (Norway, Sitzerland) - avalanche sizes per day and warning region

media_type: text/csv

format: CSV

mimetype: text/csv

en: Rutschblock data (Switzerland)

en: avalanche occurrence data (Norway, Sitzerland) - largest avalanche size per day and warning region

en: ECT data (Norway, Switzerland)

en:
Observational data used to quantitatively describe the key elements describing avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size.
The data set consists of
- Rutschblock test results (Switzerland)
- Extended Column Test results (Switzerland, Norway)
- Avalanche occurrence data (Switzerland, Norway).
The data were extracted from the respective operational databases of the national avalanche warning services in Switzerland (WSL Institute for Snow and Avalanche Research SLF Davos, Switzerland) and Norway (The Norwegian Water Resources and Energy Directorate NVE).
For further information regarding the data, please refer to the publication or contact the author.

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland

en: Stability tests, avalanche observations, Switzerland, Norway


ceilometer-klosters

title_for_slug: ceilometer-klosters

en: cloud-base-height vertical-visibility ceilometer klosters raclets

en: Ceilometer data Klosters

en: Ceilometer installed at Klosters measuring cloud base height, cloud cover and backscatter profile. The time resolution is 30 seconds, with time given in number of days from January 0, 0000, UTC. More information on the variables in the manufacturer manual.

en: Cloud base height (m) and vertical visibility (m) were measured with the VAISALA Ceilometer CL31 in Klosters (LON: 9.880413, LAT: 46.869019). The CL31 is an instrument with constant reliability for all weather conditions and simultaneous detection of three cloud layers in heights up to 7.6 km.

ceilometer

display_name: ceilometer

cloud-base-height

display_name: cloud-base-height

klosters

display_name: klosters

raclets

display_name: raclets

vertical-visibility

display_name: vertical-visibility

en: Ceilometer Klosters


en: hydrologic-and-hydrodynamic-modelling climate-change perialpine-lakes switzerland lake-level-regulation

en: Lake characteristics

en: Lake characteristics: stage-area relation for Lake Walen, Lake Zurich, Lake Brienz and Lake Thun, stage-discharge relations for the unregulated Lake Walen and semi-regulated Lake Brienz and, lake level management rules for the two regulated lakes Zurich and Thun.

format: XLS

en: Lake outflow scenarios CH2018

en: Lake outflow scenarios for Lake Walen, Lake Zurich, Lake Brienz and Lake Thun.

en: Lake water level scenarios CH2018

en: Lake water level scenarios for Lake Walen, Lake Zurich, Lake Brienz and Lake Thun.

en:
The dataset "Lake_climate_change_scenarios_CH2018" provides simulation-based climate change impact scenarios for perialpine lakes in Switzerland. These transient future scenarios were produced by combining the hydrologic model PREVAH with the hydrodynamic model MIKE11 to simulate daily lake water level (Lake_water_level_scenarios_CH2018.xls) and outflow scenarios (Lake_outflow_scenarios_CH2018.xls) from 1981 to 2099, using the Swiss Climate Change Scenarios CH2018. The future scenarios contain a total of 39 model members for three Representative Concentration Pathways, RCP2.6 (concerted mitigation efforts), RCP4.5 (limited climate mitigation) and RCP8.5 (no climate mitigation measures). These scenarios result from the study titled "Lower summer lake levels in regulated perialpine lakes, caused by climate change," authored by Wechsler et al. in 2023.

The dataset emphasizes four specific Swiss lakes, each subject to different degrees of lake level management: an unregulated lake (Lake Walen), a semi-regulated lake (Lake Brienz), and two regulated lakes (Lake Zurich and Lake Thun).

In addition, the file (Lake_characteristics.xlsx) includes data used in the modeling process, encompassing the stage-area relation for the four lakes, stage-discharge relations for the unregulated and semi-regulated lakes, and lake level management rules for the two regulated lakes.

climate-change

display_name: climate-change

hydrologic-and-hydrodynamic-modelling

display_name: hydrologic-and-hydrodynamic-modelling

lake-level-regulation

display_name: lake-level-regulation

perialpine-lakes

display_name: perialpine-lakes

en: Lake climate change scenarios CH2018


en: transport water snow pore-water measurements soil

en: Description of the videos

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

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

en: Videos of the capillary rise experiments

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

measurements

display_name: measurements

pore-water

display_name: pore-water

snow

display_name: snow

soil

display_name: soil

transport

display_name: transport

water

display_name: water

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: Capillary rise rise experiments in snow using neutron radiography


en: fagus-sylvatica beech tree-ring dendrochronology dendroecology network-analysis

en:
This dataset contains the raw ring-width and climate data (1952-2020) used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".
RW= ring width
Year= Calendar year
CN = core number
ID3= plot identifier
longitude = longitude (°)
latitude = latitude (°)
elevation = elevation (m)
age= estimated age in calendar year
radius= estimated radius in calendar year (mm)
MAT= mean annual temperature (°C)
MAP= mean annual precipitation (mm)
MCWB= mean climatic water balance (P-PET; mm)
amplitude= simple continentality measure (July - January mean maximum temperatures)
MATspline1 and 2, MCWBspline1 and 2, radiusspline4 and 5= natural spline transformed values of MAT, MCWB and radius ns(x,2)
CWD_pMay_Jun= Climatic Water Balance of previous year May to June !!!#please note CWD here is NOT Climatic Water Deficit, this was a labeling error in the creation of the dataset headers
CWD_May_Jun= Climatic Water Balance of current year May to June
Tmax_pMay_Jun= mean monthly maximum temperature of previous year May to June
Tmax_May_Jun= mean monthly maximum temperature of current year May to June
weight = site replication-based weighting input for model
SOM= membership of regional cluster 1-8 based on Supervised Organized Mapping

en: Model output

en: Output of the Mixed-effects model

format: OCTET

en:
R code to run the model behind the results of the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".
Requires beech_rawtrw.txt.

en: This dataset contains the raw ring-width and climate data (1952-2020), as well as the R code to run the model and the model output used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".

beech

display_name: beech

dendrochronology

display_name: dendrochronology

dendroecology

display_name: dendroecology

fagus-sylvatica

display_name: fagus-sylvatica

network-analysis

display_name: network-analysis

tree-ring

display_name: tree-ring

en: European Beech tree-ring dataset 1952-2020


lwf-tea-bag-sites

maintainer: Marcus Schaub

title_for_slug: lwf-tea-bag-sites

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

en: Site Information

en: Descriptive information on sites

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

carbon

display_name: carbon

decomposition

display_name: decomposition

forest

display_name: forest

green-tea

display_name: green-tea

litter

display_name: litter

modeling

display_name: modeling

roiboos

display_name: roiboos

tea-bag

display_name: tea-bag

verification

display_name: verification

en: LWF-Tea bag sites


modeling-snow-failure-with-dem

title_for_slug: modeling-snow-failure-with-dem

en: snow discrete-element-modelling snow-avalanche snow-failure snow-micro-mechanics

en: Dataset

en: The zip file contains the data underlying the figures presented in Bobillier et al. (2019). The figures can be reproduced with the python file, which is also provided.

en: Read-me

media_type: application/pdf

en: The read-me file contains a description of all the data provided in the zip file.

format: PDF

mimetype: application/pdf

en: Python file

en: Python file to reproduce the figures in Bobillier et al. (2019)

mimetype: text/x-python

en: This data set includes the modeling results described in the research article by Bobiller et al. (2020). All the figures in the article can be reproduced with the data provided.

discrete-element-modelling

display_name: discrete-element-modelling

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-micro-mechanics

display_name: snow-micro-mechanics

en: Modeling snow failure with DEM


maintainer: Franziska Koch

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

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

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

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

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

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

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

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

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

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


en: review switzerland biogas manure bioenergy

en: Production de biogaz à partir d’engrais de ferme en Suisse

en: L'objectif de ce livre blanc est de fournir aux décideurs, aux administrations et aux parties prenantes les résultats de recherche les plus récents afin de promouvoir l'utilisation optimale de la bioénergie issue des engrais de ferme dans la transition énergétique suisse. A cette fin, les résultats du centre de compétence suisse pour la recherche en bioénergie - SCCER BIOSWEET - sont résumés et présentés dans un contexte plus large. Si rien d'autre n'est mentionné, les résultats se réfèrent à la Suisse et, dans le cas de la matière première, aux potentiels nationaux de biomasse.

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland


en: microstructure metamorphism fem snow x-ray-tomography time-series

en: Simulation parameters and outputs for "A rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism"

format: ODS

en:
In the associated study [1], two time-lapse temperature gradient metamorphism series of three-dimensional micro-computed tomography images of snow (obtained by [2]) have been used to model the decrease of specific surface area (SSA) over time based on the pore-scale physics. We conducted finite element simulations of one-way coupled heat and mass diffusion in order to estimation the spatial pattern of water vapor deposition and sublimation, which controls the evolution of the SSA over time. We notably studied the influence of the condensation coefficient, a key but poorly constrained physical parameter.
This dataset provides the parameters used for the mesh generation and the finite element simulations. It also includes the ice fraction, specific surface area per unit volume and surface average of mean curvature and vapor field obtained as outputs from the mesh process and the simulations.
[1]: Braun, A., Fourteau, K., and Löwe, H.: A rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1947, 2023.
[2]: Pinzer, B. R., Schneebeli, M., and Kaempfer, T. U.: Vapor flux and recrystallization during dry snow metamorphism under a steady temperature gradient as observed by time-lapse micro-tomography, The Cryosphere, 6, 1141–1155, https://doi.org/10.5194/tc-6-1141-2012, 2012.

fem

display_name: fem

metamorphism

display_name: metamorphism

microstructure

display_name: microstructure

time-series

display_name: time-series

x-ray-tomography

display_name: x-ray-tomography

en: Simulation parameters and outputs for a rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism


en: remote-sensing snow-depth uas swe snow

en: .tfw file

en: coordinates for the .tif raster

en: HS map Brämabül, 14. April 2015

media_type: image/tiff

en:
10 cm resolution
15 cm accuracy

format: TIFF

mimetype: image/tiff

en:
This snow depth map was generated 14 January 2015, close to peak of winter accumulation, applying Unmanned Aerial System digital surface models with a spatial resolution of 10 cm. The covered area is 285'000 m2 at the top of Brämabüel, 2490 m a.s.l. covering all expositions. Coordinate system: CH1903LV03.

A detailed description is given here:

Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075-1088, 10.5194/tc-10-1075-2016, 2016.

Abstract:
Detailed information on the spatial and temporal distribution, and variability of snow depth (HS) is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Nowadays, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network is not able to capture the large spatial variability of snow depth in alpine terrain.
Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, such data acquisition is costly, if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand, is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UAS), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Such systems have the advantage that they are comparatively cost-effective and can be applied very flexibly to cover also otherwise inaccessible terrain. In this study we map snow depth at two different locations: a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and b) an exposed location (2500 m a.s.l.) on a peak in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) better than 0.07 to 0.15 m on meadows and rocks and a RMSE better than 0.30 m on sections covered by bushes or tall grass. This new measurement technology opens the door for efficient, flexible, repeatable and cost effective snow depth monitoring for various applications, investigating the worlds cryosphere.

remote-sensing

display_name: remote-sensing

snow

display_name: snow

swe

display_name: swe

uas

display_name: uas

en: UAS based snow depth maps Brämabüel, Davos, CH


maintainer: Ionuț Iosifescu Enescu

en: map 0.2GB

en: huge map 100GB

en: map 0.5GB

en: map 2GB

en: Bulk Data Access

en: Link to the S3 bucket where all the data resources can be downloaded in bulk

en: map preview 0.5MB

mimetype: image/jpeg

en: map 1GB

en: large map 10GB

en:
The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat.

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Large GIS raster data derived from Natural Earth Data (Cross Blended Hypso with Shaded Relief and Water)


en: wind-power photovoltaic marxan energy social-acceptance ecosystem-services optimization landscape spatial-planning

en:
This is the compressed Shapefile (ESRI) from the MARXAN optimization with 2216 rows (objects) and 18 columns (variables) with sf (simple feature) and data frame classes.
Column description:
X01 = Planning_units (PUs)
X02 = incl.OS. number of times pu was selected in MARXAN when optimized for ecological costs
X03 = incl.OS. number of times pu was selected in MARXAN when optimized for social costs
X04 = incl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X05 = incl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X06 = incl.OS. total energy (MWh/a)
X07 = incl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X08 = incl.OS. energy from wind turbines (MWh/a)
X09 = incl.OS. energy from ground-mounted PV infrastructure (MWh/a)

X10 = excl.OS. number of times pu was selected in MARXAN when optimized for ecological costs
X11 = excl.OS. number of times pu was selected in MARXAN when optimized for social costs
X12 = excl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X13 = excl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X14 = excl.OS. total energy (MWh/a)
X15 = excl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X16 = excl.OS. energy from wind turbines (MWh/a)
X17 = excl.OS. energy from ground-mounted PV infrastructure (MWh/a)
geometry = Simple feature XY geometry (SFC_POINT) each representing the center of a 4x4km planning unit (PU) in EPSG 21781 (CRS CH1903 / LV03).

en:
This study examines the siting scenarios for renewable energy infrastructure (REI) in Switzerland, incorporating the external costs of ecosystem services and, innovatively, social preferences. This approach challenges the prevalent techno-economic siting paradigm, which often overlooks these externalities. To minimize the external costs of the scenarios while maximizing energy yield, Marxan, an optimization software, was employed.
MARXAN was run for 2 versions: a) without ground-mounted open space PV infrastructure (excl. OS) and b) with ground-mounted open space PV infrastructure (incl. OS). In each version optimization was done using ecological costs (ECUess) or social costs (ECUsoc) in a regular grid of 4x4km (planning unit).

File:
PU_data.shp: compressed Shapefile (ESRI) from the MARXAN optimization with 2216 rows (objects) and 18 columns (variables) with sf (simple feature) and data frame classes.

Headers are described below:
X01 = Planning_units (PUs)

X02 = incl.OS. number of times pu was selected in MARXAN when optimized for ecological costs
X03 = incl.OS. number of times pu was selected in MARXAN when optimized for social costs
X04 = incl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X05 = incl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X06 = incl.OS. total energy (MWh/a)
X07 = incl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X08 = incl.OS. energy from wind turbines (MWh/a)
X09 = incl.OS. energy from ground-mounted PV infrastructure (MWh/a)

X10 = excl.OS. number of times pu was selected in MARXAN when optimized for ecological costs
X11 = excl.OS. number of times pu was selected in MARXAN when optimized for social costs
X12 = excl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X13 = excl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X14 = excl.OS. total energy (MWh/a)
X15 = excl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X16 = excl.OS. energy from wind turbines (MWh/a)
X17 = excl.OS. energy from ground-mounted PV infrastructure (MWh/a)

geometry = Simple feature XY geometry (SFC_POINT) each representing the center of a 4x4km planning unit (PU) in EPSG 21781 (CRS CH1903 / LV03).

For MARXAN optimizations (for meaning of standard files in MARXAN see https://scholar.google.ch/scholar_url?url=https://courses.washington.edu/cfr590/software/Marxan1810/marxan_manual_1_8_2.pdf&hl=de&sa=X&ei=NAWHZI6JJ8PFmAGMo47oDQ&scisig=AGlGAw-DBrsV4kUzkR6GkN1jtG66&oi=scholarr), pu_data.shp was used to generate MARXAN files input.dat, pu.dat, puvfeat.dat, spec.dat.
Coordinate Reference System: CH1903 / LV03. EPSG: 21781.

ecosystem-services

display_name: ecosystem-services

energy

display_name: energy

landscape

display_name: landscape

marxan

display_name: marxan

optimization

display_name: optimization

photovoltaic

display_name: photovoltaic

social-acceptance

display_name: social-acceptance

spatial-planning

display_name: spatial-planning

wind-power

display_name: wind-power

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: Optimizing renewable energy siting in the Swiss landscape


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

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

en: vignette data

en: A simple data set for the vignette script

en: eemma user's guide

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

en: eemma vignette script

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

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


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

en: Entremon subtitles FR

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

en: Seeland subtitles FR

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

en: Seeland subtitles DE

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

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Teaser videos Swiss landscapes under climate change


en: schweiz wasserkraft datensatz kleinstwasserkraftwerke speed2zero

en: Shapefile von CH-Kleinstwasserkraftwerke: 1'682 Kleinstwasserkraftwerke (≤ 300 kW) in der Schweiz.

en: Shapefile der Einzugsgebiete (2 km² ), in denen sich die jeweiligen Kleinstwasserkraftwerke befinden (erstellt von Yasser Haddad. ETH).

en: Die 1'682 erfassten Kleinstwasserkraftwerke im Datensatz CH-Kleinstwasserkraftwerke

en: .XLSX-Datei von CH-Kleinstwasserkraftwerke: 1'682 Kleinstwasserkraftwerke (≤ 300 kW) in der Schweiz.

en: Shapefile der 2’388 registrierten Wasserkraftanlagen in der Schweiz aus den beiden Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW).

en: .XLSX-Datei der 2’388 registrierten Wasserkraftanlagen in der Schweiz aus den beiden Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW).

en:
In der Schweiz werden Wasserkraftanlagen (WKA) mit einer Leistung von mehr als 300 kW systematisch erfasst und in der nationalen Wasserkraftstatistik (WASTA) zusammengetragen. Die WASTA bildet eine umfangreiche Datengrundlage für Analysen bezüglich Wasserkraft, auch im Kontext von Klimawandel, Biodiversitätsverlust und Energiewende. Nicht erfasst auf nationaler Ebene waren bis anhin die sogenannten Kleinstwasserkraftwerke, WKA mit einer Leistung von weniger als 300 kW. Kleinstwasserkraftwerke tragen zwar nicht wesentlich zur Schweizer Elektrizitätsversorgung bei, haben jedoch aufgrund ihrer hohen Anzahl Auswirkungen auf die Gewässerökologie, die mit den Klimawandel noch stärker unter Druck gerät.
Der neu erstellte Datensatz CH-Kleinstwasserkraftwerke umfasst 1'682 WKA mit einer Leistung von weniger als 300 kW. In den zwei Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW) sind zurzeit 2’388 WKA erfasst. Im Austausch mit den 26 Kantonen wurde jedoch klar, dass es sich nicht um eine vollständige Sammlung von Kleinstwasserkraftwerken handelt. Die 1'682 Kleinstwasserkraftwerke machen rund 70% aller erfassten WKA in der Schweiz aus, tragen jedoch mit weniger als 600 GWh/Jahr lediglich rund 1.5% zur schweizerischen Wasserkraftproduktion bei. In einem optimistischen Szenario, in dem die jährliche Produktionserwartung tendenziell überschätzt wird, liegt dieser Anteil um 0.3% höher. Von den erfassten 2’388 WKA erzeugen die 10% der grössten Anlagen über 90% der schweizerischen Wasserkraftproduktion. Der Datensatz CH-Kleinstwasserkraftwerke ist im Rahmen der ETH-Initiative SPEED2ZERO entstanden, die Klimadienstleistungen in den Bereichen Klimawandel, Biodiversitätsverlust und Energiewende erbringt (https://speed2zero.ethz.ch).
Hier verfügbare Datensätze:
- CH-Kleinstwasserkraftwerke - der hier vorgestellte Datensatz mit 1'682 Kleinstwasserkraftwerken
- EZG-Kleinstwasserkraftwerke - die Einzugsgebiete (2 km² ), in denen sich die 1'682 Kleinstwasserkraftwerke befinden (erstellt von Yasser Haddad. ETH)
- Metadaten - sämtliche Metadaten zum Datensatz CH-Kleinstwasserkraftwerke
- CH-WKA - eine Zusammenstellung sämtlicher 2'388 erfassten Wasserkraftanlagen in der Schweiz aus den beiden Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW)

datensatz

display_name: datensatz

kleinstwasserkraftwerke

display_name: kleinstwasserkraftwerke

schweiz

display_name: schweiz

wasserkraft

display_name: wasserkraft

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: CH-Kleinstwasserkraftwerke - ein schweizweiter Datensatz zu Kleinstwasserkraftwerken


maintainer: Chunhong Zhao

en: urban-morphology austin land-use land-dynamics landscape

en: Land use dynamics in Austin since 1992

en: The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006–2011 and 2011–2016. The study also aims to understand to what extent the changes in the built environment (in terms of ‘efficient growth’ versus ‘inefficient growth’) from the 1990s to 2016 in the Austin metropolitan area corresponded with ‘compact and efficient growth’ planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 × 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package.

austin

display_name: austin

land-dynamics

display_name: land-dynamics

land-use

display_name: land-use

landscape

display_name: landscape

urban-morphology

display_name: urban-morphology

en: Land use/cover dynamics in Austin metropolitan area since 1992


maintainer: Stephan Zimmermann

en: fagus-orientalis-lipsky biogeochemical-cycling calcium potassium magnesium

en: Concentrations of Ca, Mg, K, and Na in throughfall, litterflow and soil solution of a Hyrcanian forst in Northern Iran

en:
Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap.
In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.

biogeochemical-cycling

display_name: biogeochemical-cycling

calcium

display_name: calcium

fagus-orientalis-lipsky

display_name: fagus-orientalis-lipsky

magnesium

display_name: magnesium

potassium

display_name: potassium

educ

en: Education, culture and sport

en: Base cation dynamics in an Oriental beech forest


en: metamorphism advection 18o stable-water-isotope snow

en: Data files and read-me

en: raw data and micro-CT data available from contacts

en: Read-me

en: A short description of the data contained in the zipped-file. Recommended for reading before downloading the dataset.

en:
Stable water isotopes (δ18O) obtained from snow and ice samples of polar regions are used to reconstruct past climate variability, but heat and mass transport processes can affect the isotopic composition. Here we present an experimental study on the effect on the snow isotopic composition by airflow through a snow pack in controlled laboratory conditions. The influence of isothermal and controlled temperature gradient conditions on the δ18O content in the snow and interstitial water vapor is elucidated. The observed disequilibrium between snow and vapor isotopes led to exchange of isotopes between snow and vapor under non-equilibrium processes, significantly changing the δ18O content of the snow. The type of metamorphism of the snow had a significant influence on this process.
Ebner, P. P., Steen-Larsen, H. C., Stenni, B., Schneebeli, M., and Steinfeld, A.: Experimental observation of transient δ18O interaction between snow and advective airflow under various temperature gradient conditions, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-16, accepted, 2017.

advection

display_name: advection

metamorphism

display_name: metamorphism

snow

display_name: snow

stable-water-isotope

display_name: stable-water-isotope

envidat

en: EnviDat: the Environmental Data Portal

en: Experiments on stable water isotopes,  snow metamorphism, and advection


en: vert01 edna freshwater vertebrates switzerland environmental-dna spermatophytes sper01

en: Data access

en:
Catchment-based sampling of river eDNA integrates terrestrial and aquatic biodiversity of alpine landscapes

From 22-Jun-2020 to 26-Jun-2020, we sampled five sites comprising one low, two intermediate and two high-elevation sites per catchment (Fig. 1a). We visited two catchments per day—one in the morning and one in the afternoon—for a total of ten catchments. All samples per catchment were collected within a maximum four-hour period by three groups of samplers. The intermediate and high-elevation sites were situated along two tributaries leading into the low-elevation site of the river. We used three filters for each relative elevation class and filtered 60 L per relative elevation class. We sampled 30 L per tributary for a combined volume of 60 L at the intermediate and high-elevation sites. In total, 180 L were sampled in total per catchment. A filtration device composed of either the Athena® peristaltic pump (Proactive Environmental Products LLC; 1 L/min nominal flow) or the Subspace® underwater peristaltic pump (Subspace Pictures; 1 L/min nominal flow), combined with a VigiDNA® 0.2 µM cross-flow filtration capsule (VigiDNA, SPYGEN) was used in order to filter a large water volume. We used a finer mesh than the recommended VigiDNA® 0.45 µM cross-flow filtration capsule to maximise the capture of biological material since mountain water does not transport high quantities of sediments. For each filtration capsule, we used disposable sterile tubing. At the end of each filtration, we emptied the water inside the capsules, replaced it with 80 ml of CL1 conservation buffer (SPYGEN), and stored it at room temperature. We followed a strict contamination control protocol in both field and laboratory stages (Goldberg et al. 2016; Valentini et al. 2016). Each water sample was processed using disposable gloves and single-use filtration equipment. We used two primer sets targeting vertebrates (Vert01, forward: − TTAGATACCCCACTATGC, reverse: − TAGAACAGGCTCCTCTAG, mean marker length: 97 bp) and spermatophytes (g-h/Sper01, forward: − GGGCAATCCTGAGCCAA, reverse: CCATTGAGTCTCTGCACCTATC, mean marker length: 48 bp). Though both primers are relatively broad with low species-level resolution, we selected them as the goal was to minimise cost and effort and maximise the identification of a broad range of taxa which can represent the species assemblages of the region. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).

edna

display_name: edna

environmental-dna

display_name: environmental-dna

freshwater

display_name: freshwater

spermatophytes

display_name: spermatophytes

switzerland

display_name: switzerland

vertebrates

display_name: vertebrates

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 Freshwater Switzerland VaudCatchment Modipl 2020


en: soil heatwave land-use soil-biodiversity pesticide drought soil-nutrients

en: All soil data

en: Soil physicochemical characteristics

en:
Soil chemical and biological properties of soils affected by 10 different perturbations related with global change, applied individually or in combination. Greenhouse experiment. Perturbations applied to intact soil cores (15 cm diameter, 15 cm deep) collected in an extensively managed grassland on the WSL grounds in June 2023.
This dataset contains all data on which the publication below was based:
"Fioratti Junod M, Gombeer S, Holmes J, Zimmerman S, Rillig M, Risch AC and Cordero I. Soil functionality declines under multiple superimposed global change perturbations. XXXX "
Please, cite this publication together with the citation of the datafile.
Database includes:
Pot_ID: pot or soil core identifier.
Batch: numeric, 1 or 2.
Enzyme_plate: numeric, from 1 to 6.
Number_of_perturbations: numeric, from 0 to 10.
N_addition: binary. Yes = 1, No = 0.
P_addition: binary. Yes = 1, No = 0.
Defoliation: binary. Yes = 1, No = 0.
Trampling: binary. Yes = 1, No = 0.
Insecticide: binary. Yes = 1, No = 0.
Fungicide: binary. Yes = 1, No = 0.
Herbicide: binary. Yes = 1, No = 0.
Antibiotic: binary. Yes = 1, No = 0.
Drought: binary. Yes = 1, No = 0.
Heat_wave: binary. Yes = 1, No = 0.
Treatment: character. Description of the treatment, 15 levels.
SWC: soil water content. Numeric, unitless.
Green_biomass: green plant biomass. Numeric, g.
Brown_biomass: brown or dead plant biomass. Numeric, g.
Biomass_cut: plant biomass cut during defoliation treatments. Numeric, g.
CO2_flux_light: net CO2 flux under ambient light. Numeric, mg CO2 m-2 h-1.
CO2_flux_dark: ecosystem respiration. Numeric, mg CO2 m-2 h-1.
S: organic matter stabilisation factor (tea bag index). Numeric, unitless.
k: organic matter decomposition rate (tea bag index). Numeric, unitless.
DOC: dissolved organic carbon, mg Kg-1 dry soil.
IC: inorganic carbon, mg Kg-1 dry soil.
Ammonium_KCl: plant available ammonium, mg Kg-1 dry soil.
Nitrate_KCl: plant available nitrate, mg Kg-1 dry soil.
Phosphate: phosphate, mg Kg-1 dry soil.
MBC: microbial biomass carbon, mg Kg-1 dry soil.
MBN: microbial biomass nitrogen, mg Kg-1 dry soil.
PHO: phosphatase activity, nmol h-1 g-1 dry soil.
BG: β-glucosidase, nmol h-1 g-1 dry soil.
XYL: xylosidase, nmol h-1 g-1 dry soil.
CBH: cellobiohydrolase, nmol h-1 g-1 dry soil.
NAG: N-acetylglucosaminidase, nmol h-1 g-1 dry soil.
LAP: leucine aminopeptidase, nmol h-1 g-1 dry soil.
POX: phenoloxidase, nmol h-1 g-1 dry soil.
PER: peroxidase, nmol h-1 g-1 dry soil.
pH: soil pH, unitless
Water_stable_aggregates: water stable aggregates, %
Surface_infiltration_time: water surface infiltration time, s.

drought

display_name: drought

heatwave

display_name: heatwave

land-use

display_name: land-use

pesticide

display_name: pesticide

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-nutrients

display_name: soil-nutrients

en: Escalating effects of multiple perturbations on soil functionality


maintainer: Maria Elvira Murazzi

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

en: Nanoplastic in forests experiment

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

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

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

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

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

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

forest-trees

display_name: forest-trees

nanoplastic

display_name: nanoplastic

polystyrene

display_name: polystyrene

uptake

display_name: uptake

educ

en: Education, culture and sport

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


wind-lidar-davos-wolfgang

title_for_slug: wind-lidar-davos-wolfgang

en: profiles wind lidar wolfgangpass raclets

en: Wind LIDAR data Wolfgang

en: A Leosphere WidCube 100S was deployed at Davos Wolfang, measuring wind profiles as well as performing PPI and RHI scans. Time format is in UTC with highest time resolution of 5 seconds.

en: Scanning wind Lidar from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 200 m above ground to 8100 m. The time resolution is up to 5 seconds. The Lidar was measuring wind profiles but also performed plan position indicator (PPI) and range height indicator (RHI) scans.

lidar

display_name: lidar

profiles

display_name: profiles

raclets

display_name: raclets

wind

display_name: wind

wolfgangpass

display_name: wolfgangpass

envidat

en: EnviDat: the Environmental Data Portal

en: Wind LIDAR Davos Wolfgang


en: ecosystem-services alan sustainability artificial-lighting outdoor-lighting

en: R code of flight-active insects analysis.

en: Raw data for ground-dwelling arthropod NMDS analysis

en: Raw data Moran's I test

en: R code for NMDS of ground-dwelling arthropods

en: Raw data for ground-dwelling arthropod analysis

en: R code for NMDS of flight-active insects

en: R code for PERMANOVA Analysis of ground-dwelling arthropods.

en: R code of flight-active and ground-dwelling arthropod combined analysis.

en: Raw data for flying insects and ground-dwelling arthropod combined analysis

en: Raw data for flying insects analysis

en: Raw data for flying insects NMDS analysis

en: R code of ground-dwelling arthropod analysis.

en: R code for PERMANOVA Analysis of flight-active insects.

en: Raw data and R Code to do all the analysis performed for the paper "Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods". Including data for both flight-active insects and ground-dwelling arthropods combined (PT_TRF_all.csv), flight-active insects alone (ALANeX_PT_all_clean_control.csv) and ground-dwelling insects alone (ALANeX_TRF_all_clean_control.csv). For the NMDS analysis, there is a data set for the flight-active insects (ALANeX_PT_NMDS.csv) and the ground-dwelling arthropods (ALANeX_TRF_NMDS.csv). For the Morans I use the 'ALANeX_MoransI.csv' dataset.

alan

display_name: alan

artificial-lighting

display_name: artificial-lighting

ecosystem-services

display_name: ecosystem-services

outdoor-lighting

display_name: outdoor-lighting

sustainability

display_name: sustainability

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.


maintainer: Joan Casanelles Abella

en: plant-pollinator-interactions antwerp osmia-bicornis dna-metabarcoding cavity-nesting-bees paris trap-nest zurich hylaeus-communis urban-ecology poznan tartu chelostoma-florisomne osmia-cornuta

en: Explanation data variables

en: Larval food composition

en: Information on the study sites

en: For each site, IDs, city, coordinates, structure where trapnest was attahced and height of the trapnest is provided

en: Urbanization poses threats and opportunities for the biodiversity of wild bees. A main gap relates to the food preferences of wild bees in urban ecosystems, which usually harbour large numbers of plant species, particularly at the larval stage. This data sets describes the larval food of four wild bee species (i.e. Chelostoma florisomne, Hylaeus communis, Osmica bicornis and Osmia cornuta) and three genera (i.e. Chelostoma sp., Hylaeus sp, and Osmia sp.) common in urban areas in five different European cities (i.e. Antwerp, Paris, Poznan, Tartu and Zurich). This data results from a European-level study aimed at understanding the effects of urbanization on biodiversity across different cities and citiscapes, and a Swiss project aimed at understanding the effects of urban ecosystems in wild bee feeding behaviour. Wild bees were sampled using standardized trap-nests in 80 sites (32 in Zurich and 12 in each of the remaining cities), selected following a double gradient of available habitat at local and landscape scales. Larval pollen was obtained from the bee nests and identified using DNA metabarconding. The data provides the plant composition at the species or genus level of the different bee nests of the studied species in the studied sites of the five European cities. For Hylaeus communis, this is the first study in reporting larval food composition.

antwerp

display_name: antwerp

cavity-nesting-bees

display_name: cavity-nesting-bees

chelostoma-florisomne

display_name: chelostoma-florisomne

dna-metabarcoding

display_name: dna-metabarcoding

hylaeus-communis

display_name: hylaeus-communis

osmia-bicornis

display_name: osmia-bicornis

osmia-cornuta

display_name: osmia-cornuta

paris

display_name: paris

plant-pollinator-interactions

display_name: plant-pollinator-interactions

poznan

display_name: poznan

tartu

display_name: tartu

trap-nest

display_name: trap-nest

urban-ecology

display_name: urban-ecology

zurich

display_name: zurich

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Larval food composition of four wild bee species in five European cities


en: long-term-experiment afforestation mountain-forest treeline stillberg

en: Dataset V1

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

format: GDB

en: Site photo

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

en: Documentation

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

mimetype: application/msword

en:
 Important
This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry "Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland." The entry can be found under this link <https://www.envidat.ch/dataset/afforestation-stillberg> and with this DOI 10.16904/envidat.397.

afforestation

display_name: afforestation

long-term-experiment

display_name: long-term-experiment

mountain-forest

display_name: mountain-forest

stillberg

display_name: stillberg

treeline

display_name: treeline

en: Long-term treeline research dataset at Stillberg, Davos


en: dischma snowpack fsm2trans snow-hydrology fsm2 hicar

en: Input data December 2016 HICAR 50m resolution

en: Input data March 2017 COSD

en: Input data May 2017 COSD

en: Input data July 2017 HICAR 100m resolution

en: Input data February 2017 HICAR 100m resolution

en: input data january 2016 COSD

en: Input data June 2017 HICAR 100m resolution

en: Input data April 2017 HICAR 50m resolution

en: Input data May 2017 HICAR 250m resolution

en: Input data october 2016 HICAR 50m resolution

en: Input data March 2017 HICAR 100m resolution

en: Input data April 2017 COSD

en: Input data March 2017 HICAR 250m resolution

en: Input data March 2017 HICAR 50m resolution

en: Input data November 2016 HICAR 50m resolution

en: Input data July 2017 HICAR 50m resolution

en: Available input files for running with FSM2trans

en: Input data June 2017 HICAR 250m resolution

en: Input data February 2017 COSD

en: Input data January 2017 HICAR 250m resolution

en:
snowheight output of validation days (LiDAR days) for all simulation runs
- 20.03.2027
- 31.03.2017
- 17.05.2017

en: Input data July 2017 HICAR 250m resolution

en:
time series of daily averages of the whole domain for each simulation run
- rainfall
- snowfall
- runoff
- snowmelt
- temperature

en: Input data november 2016 HICAR 100m resolution

en: Input data January 2017 HICAR 50m resolution

en: Input data December 2016 HICAR 100m resolution

en: Input data October 2016 HICAR 250m resolution

en: Input data June 2017 COSD

en: Input data May 2017 HICAR 50m resolution

en: Input data November 2016 HICAR 250m resolution

en: Input data December 2016 HICAR 250m resolution

en: Input data october 2016 HICAR 100m resolution

en: Input data May 2017 HICAR 100m resolution

en: Input data February 2017 HICAR 50m resolution

en: Input data February 2017 HICAR 250m resolution

en: Input data January 2017 HICAR 100m resolution

en: Input data April 2017 HICAR 100m resolution

en: Input data June 2017 HICAR 50m resolution

en: Input data April 2017 HICAR 250m resolution

en:
 DESCRIPTION
The dataset is used to run snowpack simulations with various forcing data at different resolutions with the Flexible Snow Model (FSM2oshd). A dynamical downscaling model (HICAR) and a semi-statistical downscaling approach (COSD) are used to downscale COSMO data to resolutions of 250 m, 100 m and 50 m. Simulations are run with the operational snow cover model and a model extension including wind- and gravity-induced snow redistribution (FSM2trans).
 Input Data
This paragraph describes the input data used to run simulations with FSM2trans
 HICAR
The HICAR input data is created by the High-resolution Intermediate Complexity Atmospheric Research model. The input data is available for different resolutions (250m, 100m and 50m) for a domain covering complex terrain in the Swiss Alps (Dischma)
 COSD
The COSD data was statistically downscaled within the OSHD framework to different resolutions (250m, 100m, 50m) for a domain covering complex terrain in the Swiss Alps (Dischma)
 Simulation files
For the Simulations, a landuse file and a namelist file with the parametrizations need to be supplied to the model
 simulation output
This simulation output that is used for the analysis of the study is provided.

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: FSM2trans snowpack simulations with HICAR input


en: ecohydrology streamflow precipitation hydrology nonstationarity rainfall-runoff-modelling nonlinear-deconvolution time-series

en: Zip-compressed folder containing two versions of the ERRA script. ERRA_v1.06.R uses Rcpp to speed up several key calculations, but requires a C++ compiler. ERRA_v1.06_no_Rcpp.R is a bit slower but does not require a C++ compiler or Rcpp. Public use of these scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

en: {{deprecated version}} ERRA_scripts.zip

en: Zip-compressed folder containing two versions of the ERRA script. ERRA_v1.0.R uses Rcpp to speed up several key calculations, but requires a C++ compiler. ERRA_v1.0_no_Rcpp.R is a bit slower but does not require a C++ compiler or Rcpp. Public use of these scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

en: {{deprecated version}} An introduction to ERRA.pdf

en: "An introduction to ERRA" presents a series of vignettes that introduce some of the capabilities of the Ensemble Rainfall-Runoff Analysis (ERRA) script, and that show how to invoke them. This introduction also includes documentation for ERRA function calls, largely reproducing the documentation embedded in the ERRA script itself.

en: Demonstration scripts.zip

en: Zip-compressed folder containing demonstration scripts, source data, and output files for the ERRA results shown in "Introduction to ERRA" and in J.W. Kirchner, "Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using Ensemble Rainfall-Runoff Analysis (ERRA): proof of concept", Hydrology and Earth System Sciences, 2024.

en: An introduction to ERRA v1.06.pdf

en:
ERRA is a data-driven, nonparametric, model-independent method for quantifying rainfall-runoff relationships across a spectrum of time lags, in systems that may be nonlinear, nonstationary, and spatially heterogeneous.
Researchers using ERRA in published work should cite J.W. Kirchner, "Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using Ensemble Rainfall-Runoff Analysis (ERRA): proof of concept", Hydrology and Earth System Sciences, 2024 (for ERRA itself) and J.W. Kirchner, "Impulse response functions for nonlinear, nonstationary, and heterogeneous systems, estimated by deconvolution and de-mixing of noisy time series", Sensors, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022 (for the underlying mathematics).
This data set includes two versions of the ERRA script written in the open-source programming language R, a detailed user's guide, and sample scripts and source data for all of the results in Kirchner (2024).
These scripts are made publicly available under GNU General Public License 3; for details see https://www.gnu.org/licenses/. The data and documentation are made available under Creative Commons Attribution Share-Alike CC-BY-SA.
ETH Zurich, WSL, and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose. Users are solely responsible for determining the suitability and reliability of this software for their own purposes.

ecohydrology

display_name: ecohydrology

hydrology

display_name: hydrology

nonlinear-deconvolution

display_name: nonlinear-deconvolution

nonstationarity

display_name: nonstationarity

precipitation

display_name: precipitation

rainfall-runoff-modelling

display_name: rainfall-runoff-modelling

streamflow

display_name: streamflow

time-series

display_name: time-series

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: ERRA -- an R script for Ensemble Rainfall-Runoff Analysis


en: ground-temperature sdms soil frost-change-velocity aridity precipitation

en: Soil pH modified from SoilGrids

en: Frost change frequency modified from CHELSA

en: Soil organic carbon content modified from SoilGrids

en: Precipitation of driest quarter modified from CHELSA

en: Soil clay content modified from SoilGrids

en: Mean diurnal range modified from CHELSA

en: Precipitation seasonality from CHELSA

en: Mean annual temperature modified from CHELSA

en: The dataset contains seven environmental layers (average annual temperature, aridity [annual precipitation divided by annual potential evapotranspiration], frost change frequency, precipitation in the driest quarter, mean diurnal temperature range, and precipitation seasonality) modified from CHELSA (https://chelsa-climate.org/) and three soil layers (soil organic matter content, pH water, and clay content) modified from SoilGrids (https://soilgrids.org/).

aridity

display_name: aridity

frost-change-velocity

display_name: frost-change-velocity

ground-temperature

display_name: ground-temperature

sdms

display_name: sdms

soil

display_name: soil

en: Environmental layers for SDM simulations (GDPlants)


data-konzeptionelles-systemmodell

title_for_slug: data-konzeptionelles-systemmodell

en: landscape-change qualitative-research climate-change policy-action scenarios

en:
This folder contains documents relevant to the project step Konzeptionelles Systemmodell. It specifically contains the following documents:

1. 0201_Konzeptionelles Systemmodell_20221027: This document is the final version of the Konzeptionelles Systemmodell. The underlying structure of the Konzeptionelles Systemmodell is explained in chapter 4.4 of the document 0202_Masterarbeit Elena Siegrist_UniBern_Herbstsemester 2022 also deposited in this folder. The document contains 3 sheets. The sheet "Bausteine" shows the different possible options for table columns of the type Bausteine (see chapter 4.4 of Masterarbeit), the sheet "Direkte Effekte" contains identified direct impact pathways and the sheet "Indirekte Effekte" contains identified indirect impact pathways.

2. 0202_Masterarbeit Elena Siegrist_UniBern_Herbstsemester 2022: This document is the Master Thesis of Elena Grace Siegrist submitted to and accepted by the University of Bern in the Autumn Semester 2022. Chapter 4.4 of the document explains the underlying structure of the Konzeptionelles Systemmodell.

3. 0203_Literaturverzeichnis_konzeptionelles Systemmodell_20221026: This document lists all literature and documents cited in the Konzeptionelle Systemmodell.

climate-change

display_name: climate-change

landscape-change

display_name: landscape-change

policy-action

display_name: policy-action

qualitative-research

display_name: qualitative-research

scenarios

display_name: scenarios

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: Data Konzeptionelles Systemmodell


maintainer: Athanasios Nenes

en: wolfgangpass cloud-droplets supersaturation cloud-modeling sigma-w raclets

en: Predicted cloud droplet numbers (kappa=0.1) Davos Wolfgang

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.1 is assumed, which is the lower limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The wind-lidar derived standard deviation of updraft velocities (sigma-w) in m/s, used to apply the parameterization employing a single characteristic velocity (Morales and Nenes, 2010)
* The maximum predicted cloud parcel supersaturation (Smax%)
* The total in-situ aerosol number concentration (Naer) in cmˉ³, measured by the SMPS
* The predicted cloud droplet numbers (Nd) in cmˉ³
* The activation fraction (%)

en: Predicted cloud droplet numbers (kappa=0.25) Weissfluhjoch

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.25 is assumed, which is the upper limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The wind-lidar derived standard deviation of updraft velocities (sigma-w) in m/s, used to apply the parameterization employing a single characteristic velocity (Morales and Nenes, 2010)
* The maximum predicted cloud parcel supersaturation (Smax%)
* The total in-situ aerosol number concentration (Naer) in cmˉ³, measured by the SMPS
* The predicted cloud droplet numbers (Nd) in cmˉ³
* The activation fraction (%)

en: Cloud droplet properties were predicted between February 24 and March 8 2019 for the measurement site Davos Wolfgang (1630 m a.s.l., LON: 9.853594, LAT: 46.835577). Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from a Scanning Mobility Particle Size Spectrometer (SMPS) instrument deployed at Davos Wolfgang (https://www.envidat.ch/dataset/aerosol-data-davos-wolfgang). The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at the same station and are extracted from the first bin of the instrument, being 200 m above ground level. The hygroscopic properties of the particles measured at Davos Wolfgang could not be estimated, owing to a lack of concurrent CCN measurements. As a sensitivity test, droplet calculations are performed assuming two different values of the aerosol hygroscopicity parameter, 0.1 and 0.25, based on the analysis carried out for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

wolfgangpass

display_name: wolfgangpass

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Predicted cloud droplet numbers Davos Wolfgang


tree-species-map-of-switzerland

title_for_slug: tree-species-map-of-switzerland

en: forest tree-species switzerland remote-sensing map

media_type: image/png

en: Image of the tree species map

format: PNG

en:
Dominant tree species map of Switzerland
We created a tree species map of Switzerland for the dominant tree species in the forested areas. The spatial resolution of the map is 10 m and the coordinate system is ETRS89-extended / LAEA Europe (EPSG 3035). The map comprises Sentinel-2 index time series from the year 2020, a digital elevation model and species reference data from the Swiss National Forest Inventory. The map is available as raster (.tif) or vector dataset (.gpkg).
**Access will be granted upon request.**
In total, the following 15 species were mapped:
*Abies alba*, *Acer pseudoplatanus*, *Alnus glutinosa*, *Alnus incana*, *Betula pendula*, *Castanea sativa*, *Fagus sylvatica*, *Fraxinus excelsior*, *Picea abies*, *Pinus cembra*, *Pinus mugo arborea*, *Pinus sylvestris*, *Quercus petraea*, *Quercus robur*, *Sorbus aucuparia*.

 Approach

 Data
- Swiss National Forest Inventory Data (stand species with > 60 % dominance in upper canopy; on at least more than 9 plots dominant)
- Sentinel-2 time series (2020, Indices: CCI, CIRE, NDMI, EVI, NDVI)
- Digital elevation model (DEM) (swissalti3d, 5 m)
- Biogeographical regions (Federal Office for the Environment FOEN)
- Forest mask 2017 (Approach: Waser et al., 2015)

 Modeling approach
We identified the most meaningful variables that led to separation of the respective groups by using random forest models with a forward feature selection (Meyer et al., 2018; Ververidis & Kotropoulos, 2005). In this approach, the final random forest model is solely built from the selected meaningful variables. By identifying meaningful variables, we can determine which variables might influence the grouping. Further, to avoid overfitting and overly optimistic results, we applied 10-fold spatial cross-validation and put all pixels from a plot in the same spatial fold.
The modeling was realized using the CAST package in R (Meyer et al., 2022), based on the well-known caret package (Kuhn, 2022). We used the ranger package in R (Wright & Ziegler, 2017) to implement the random forest models, due to its short computation time.

 Training data for modeling
- 295 Sentinel-2, DEM & Biogeographical variables
- 10525 tree species pixels

 Selected variables for final model
1. EVI of 2020.05.16
2. NDMI of 2020.03.12
3. CIRE of 2020.04.16
4. NDMI of 2020.07.05
5. CCI of 2020.05.11
6. dem
7. CCI of 2020.08.14
8. NDMI of 2020.08.24
9. CCI of 2020.12.22
10. NDMI of 2020.04.21
11. NDMI of 2020.11.17
12. NDMI of 2020.08.09
13. CIRE of 2020.03.22
14. CIRE of 2020.08.09
14. CCI of 2020.11.02
15. CIRE of 2020.06.10

 Overall Accuracy of final model
- 0.759

 Nationwide prediction
- Predicted throughout forest mask 2017 (Approach: Waser et al., 2015)
- Not applied on incomplete Sentinel-2 time series (own category in final map: incomplete_ts)
- Applied the Area of Applicability (Meyer 2022) to sort out pixels outside of the feature space; basically where the model had not the same values for pixels as in the available training data

 *Be aware that the map is only validated with the training data itself, an independent validation with other data sources remains missing*

 References
- Kuhn, M. (2022). Classification and Regression Training. 6.0-93.
- Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., & Nauss, T. (2018). *Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation*. Environmental Modelling and Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001
- Meyer, H., Milà, C., & Ludwig, M. (2022). *CAST: 'caret' Applications for Spatial-Temporal Models*. 0.7.0.
- Ververidis, D., & Kotropoulos, C. (2005). *Sequential forward feature selection with low computational cost*. 2005 13th European Signal Processing Conference.
- Waser, L., Fischer, C.,Wang, Z., & Ginzler, C. (2015). *Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition*. Forests, 6, 12, 4510–4528.
- Wright, M. N., & Ziegler, A. (2017). *ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R*. Journal of Statistical Software, 77(1), 1-17. https://doi.org/doi:10.18637/jss.v077.i01

forest

display_name: forest

map

display_name: map

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

tree-species

display_name: tree-species

en: Tree species map of Switzerland


en: ert irrigation drought drone-data 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

en: Pfynwald Geoelectric Experiment 2022


en: nuclear-microsatellite-markers multilocus-genotypes switzerland population-genetics trichopria-drosophilae simple-sequence-repeats drosophila-suzukii biological-control

en: Multilocus genotypes (biallelic coding for 21 nuclear microsatellites) of Trichopria drosophilae described in Gugerli et al. Agrarforschung Schweiz 2019. Missing data denoted as '999'. Population codes (PopNo) are the following: 1, Vezia; 3, Contone ; 5, Davesco; 6, Corteglia; 7, Mezzana; 8, Stabio; 9, Gordola; 10, Sementina; 12, Brusata di Novazzano; 14, Monteggio; 16, Giubiasco; 17, ComoBP; 18, VD; 19, Weil_D; 20, Dossenheim; 21, BioPlanet_Como. Coordinates (CoordCH_E, CoordCH_N) refer to the Swiss coordinates system CH1903.

en: Table comprising all pertinent information for multiplex PCRs of species-specific nuclear microsatellite makers for Trichopria drosophilae, comprising all 21 newly developed markers, details on full sequences with GenBank accession numbers, primer sequences (including labeling and pig-tails), and repeat motives. PCRs with respective annealing temperatures (AnnealingTemp) refer to Type-It Multiplex PCR Kit (Qiagen, Hilden, Germany).

en:
 Nuclear microsatellite markers and genotype data for _Trichopria ddrosophilae_

This data set comprises (i) the characteristics of a set of 21 species-specific nuclear microsatellites for PCR amplification in _Trichopria drosophilae_ (ii) and genotype data for samples collected in southern Switzerland (Canton of Ticino), with few reference samples from Canton of Vaud, southern Germany, and northern Italy (lab-reared population).
Markers were developed by Ecogenics GmbH, Balgach (Switzerland), using MiSeq Nano 2x250 v2 format (on a mix of 10 individuals). Multiplex PCR assays for multilocus genotyping were established by Ecological Genetics (WSL Birmensdorf), and population genetic analyses are found in Gugerli et al., Agrarforschung Schweiz 2019.

biological-control

display_name: biological-control

drosophila-suzukii

display_name: drosophila-suzukii

multilocus-genotypes

display_name: multilocus-genotypes

nuclear-microsatellite-markers

display_name: nuclear-microsatellite-markers

population-genetics

display_name: population-genetics

simple-sequence-repeats

display_name: simple-sequence-repeats

trichopria-drosophilae

display_name: trichopria-drosophilae

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: Nuclear microsatellite markers for Trichopria drosophilae, parasitoid wasp on Drosophila suzukii


en: switzerland tree-species remote-sensing sentinel-2 time-series

en: Tree species profiles for 2019, Red-Edge-2 band

en: Tree species profiles for 2020, SWIR-2 band

en: Tree species profiles for 2020, SWIR-1 band

en: Tree species profiles for 2020, EVI index

en: Tree species profiles for 2020, Red-Edge-2 band

en: Tree species profiles for 2020, Blue band

en: Tree species profiles for 2019, NDVI index

en: Tree species profiles for 2019, CCI index

en: Tree species profiles for 2019, Blue band

en: Tree species profiles for 2019, Red-Edge-3 band

en: Tree species profiles for 2019, NDMI/NDWI index

en: Tree species profiles for 2019, CIre Index

en: Tree species profiles for 2020, Red-Edge-3 band

en: Tree species profiles for 2019, Red band

en: Tree species profiles for 2019, Green band

en: Tree species profiles for 2020, CCI index

en: Tree species profiles for 2020, Red band

en: Tree species profiles for 2019, EVI index

en: Tree species profiles for 2020, Red-Edge-1 band

en: Tree species profiles for 2020, Green band

en: Tree species profiles for 2020, CIre index

en: Tree species profiles for 2020, NDMI index

en: Tree species profiles for 2019, Red-Edge-1 band

en: Tree species profiles for 2020, NIR band

en: Tree species profiles for 2019, NIR band

en: Tree species profiles for 2020, NDVI index

en: Tree species profiles for 2019, SWIR-1 band

en: Tree species profiles for 2019, SWIR-2 band

en:
 Tree species profiles from Sentinel-2 time series
We obtained tree species-specific Sentinel-2 time series for the years 2019 and 2020. The Sentinel-2 time series originated from [here](https://www.envidat.ch/#/metadata/sentinel-2-time-series-of-switzerland), and the tree species information was derived from pure plots of the Swiss National Forest Inventory, spread all over Switzerland.
In addition to the 10 and 20 m bands from Sentinel-2 (Blue, Green, Red, Red-Edge-1, Red-Edge-2, Red-Edge-3, NIR, SWIR-1, SWIR-2), we calculated indices (CCI, CIre, EVI, NDMI/NDWI, NDVI).
In total, tree species profiles of 763 pixels for 2020 and 562 pixels for 2019 are available for seven tree species (*Abies alba*, *Castanea sativa*, *Fagus sylvatica*, *Fraxinus excelsior*, *Larix* spp., *Picea abies*, *Pinus sylvestris*).

time-series

display_name: time-series


maintainer: Matthias Haeni

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

en: Site photo of the field station (BTF)

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

en: Data plot of the forest station (BTB)

en: Data file.

en: Field station data (BTF)

en: Site aerial photo (Bettlachstock)

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

en: Site photo of the forest station (BTB)

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

en: Data plot of the field station (BTF)

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

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 Bettlachstock in Switzerland where one station is located within a natural mixed forest stand (BTB) with European beech (_Fagus sylvatica_; 170-190 yrs), European silver fir (_Abies alba_; 190 yrs) and Norway spruce (_Picea abies_; 200 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Bettlachstock 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: Bettlachstock, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards