maintainer: Andrin Caviezel

en: rockfall rockfall-runout rockfall-experiments sensor-stream dem natural-hazards induced-rockfall stonenode stonenodedata deposition-points

en: Induced Rockfall Data, Tschamut, GR

en:
 Induced Rockfall Experimental Data, Tschamut, GR, Switzerland

The archive contains site specific geographical data as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally availabe are the StoneNode v1.0 sensor streams for the equipped rocks.

The _OverviewFigures_ file serves as quick representation of the included data. It also contains the overview table for the rock masses in order to set the dimensions accordingly in RAMMS::ROCKFALL.

The archive contains the following folders:
<\ul>
* DEM: ArcGIS compatible DEM Tschamut_DEM_2m (grid resolution of 2 m) as well as the .asc grid used in RAMMS::ROCKFALL simulations
* _DepositionPoints_: containing two folders _RF Tschamut Frozen_ and _RF Tschamut WET_ containing each a shapefile with the complete deposition data of each series as well as deposition shapfiles for each rock shape.
* _DigitizedRocks_: Digitized point clouds of the used rocks as used for RAMMS::ROCKFALL simulations (.pts files) (dimensions have to be checked).
* _Orthofoto_: Orthophoto of the experimental site obtained via UAV with 5 cm resolution.

en: StoneNode v1.0 Data for equipped experimental Runs.

en: Corresponding StoneNode v1.0 data streams belonging to the deposition points.

en:
 Dataset of an experimental campaign of induced rockfall in Tschamut, Grisons, Switzerland.

The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally available are all the StoneNode data streams for rocks equipped with a sensor. The data set consists of

* Deposition points from two series (wet (27/10/2016) and frozen (08/12/2016) ground)
* Digital Elevation Model (grid resolution 2 m) obtained via UAV
* Orthophoto (5 cm resolution) obtained via UAV
* Digitized rock point clouds (.pts input files for RAMMS::ROCKFALL)
* StoneNode v1.0 raw data stream for equipped rocks.

Further information is found in

* A. Caviezel et al., _Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments_, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/
* P. Niklaus et al., _StoneNode: A low-power sensor device for induced rockfall experiments_, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/

dem

display_name: dem

deposition-points

display_name: deposition-points

induced-rockfall

display_name: induced-rockfall

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

sensor-stream

display_name: sensor-stream

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata

en: Induced Rockfall Dataset (Small Rock Experimental Campaign), Tschamut, Grisons, Switzerland


maintainer: EnviDat Support

en: taxonomic-diversity functional-diversity phylogenetic-diversity species-distribution-models global-change land-use-change migclim conservation plants european-alps zonation beta-diversity

state: active

type: dataset

en: Data Access

format: GeoTIFF

en: Updated SDM outputs

en: Refined scenarios of plants distribution in the European Alps

en:
This repository contains extensive data for the European Alps:
  - Observations of ~3,500 plant species
  - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal)
  - Flora migration rates (categorical) and ecological preferences (continuous indicator values)
  - Regional maps of barriers to migration and water bodies at 100-m resolution
  - Sampling effort, distance to roads and cities predictors at 100-m resolution
  - Present and future abundances over the study region at 1-km resolution (~2,000 species)
  - Present and future multifaceted and uniqueness of the European Alps' Flora at 1-km resolution
  - Present and future conservation recommendations at 1-km resolution (26 current and future strategies)
  - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees)
  - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them

beta-diversity

display_name: beta-diversity

conservation

display_name: conservation

european-alps

display_name: european-alps

functional-diversity

display_name: functional-diversity

global-change

display_name: global-change

land-use-change

display_name: land-use-change

migclim

display_name: migclim

phylogenetic-diversity

display_name: phylogenetic-diversity

plants

display_name: plants

species-distribution-models

display_name: species-distribution-models

taxonomic-diversity

display_name: taxonomic-diversity

zonation

display_name: zonation

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Present and future multifaceted plant diversity, uniqueness and conservation in the European Alps


maintainer: Thorsten Bartels-Rausch

en: arctic metamorphism reaction alpine chemistry snow interface laboratory-study ozone antarctica solution heterogeneous brine

en: Data Figure 1

en: Data of published Figure

format: SERVICE

en: Acetone data

en:
intensity of the acetone-water-cluster and water-cluster (as derived by a chemical ionisation mass spectrometer) with time in the carrier gas downstream of packed bed flow tube holding the snow sample or the by-pass, respectively. Time "0" denotes the time when the mass spectrometer data acquisition was started. When the carrier
gas with acetone was passed via the packed bed flow tube, that is switched from by-pass to pass the snow, acetone concentration in the carrier gas rapidly dropped due to surface adsorption and slowly recovered as the adsorption sites saturate. From this, the surface area can be obtained.

en: Ozone data

en: Trend of ozone mixing ratio ( _O3mixingRatio_ppb_ with time ( _time_s_ ) as measured downstream of the snow sample during the experiments. Time "0" denotes the time when data acquisition with ozone analyser was started. And, ozone (_nOzone_molec_per_s_ ) loss for each experiment with duration of ozone exposure ( _duration_s_ ) of ozone exposure, as derived from the ozone trends shown above. Time "0" denotes the time when the carrier gas with ozone was passed via the snow sample.

en: Matlab Data

en: All data as Matlab mat file

en: Readme file

media_type: application/pdf

en: This file shows data, code, and plots.

format: PDF

mimetype: application/pdf

en: data list

en: Lists file names, snow sample properties, and flow settings for each data-set.

en: Matlab Script to view and plot data

en: Earth’s snow cover is very dynamic on diurnal time scales. The changes to the snow structure during this metamorphism have wide ranging impacts such as on avalanche formation and on the capacity of surface snow to exchange trace gases with the atmosphere. Here, we investigate the influence of dry metamorphism, which involves fluxes of water vapor, on the chemical reactivity of bromide in the snow. For this, the heterogeneous reactive loss of ozone at a concentration of 5-6E12 molecules cm-3 is investigated in artificial, shock-frozen snow samples doped with 6.2 uM sodium bromide and with varying metamorphism history. The oxidation of bromide in snow is one reaction initiating polar bromine releases and ozone depletions.

alpine

display_name: alpine

antarctica

display_name: antarctica

arctic

display_name: arctic

brine

display_name: brine

chemistry

display_name: chemistry

heterogeneous

display_name: heterogeneous

interface

display_name: interface

laboratory-study

display_name: laboratory-study

metamorphism

display_name: metamorphism

ozone

display_name: ozone

reaction

display_name: reaction

snow

display_name: snow

solution

display_name: solution

en: Data set on bromide oxidation by ozone in snow during metamorphism from laboratory study


maintainer: Frank Techel

en: avalanche-forecast avalanche-danger-level avalanche-forecasting avalanche-bulletin verification

en: D_QC for forecasting seasons 2020/2021 - 2022/2023.

format: XLS

en: D_QC for forecasting seasons 2001/2002 until 2019/2020.

en: Data for the 2023/2024 forecasting season. It was added on 26 Aug 2024 by F. Techel.

en:
The data set contains the re-analyzed (or quality-checked) regional avalanche danger levels (D_QC) for Switzerland.

D_QC relates to dry-snow avalanche conditions only. Measuring the avalanche danger level D is not possible; forecast, nowcast, and hindcast assessments of D are judgments by humans interpreting data. However, combining several pieces of information indicating the same D, it can be expected that it is more likely that D_QC represents the avalanche conditions well.

For the **forecasting seasons 2001/2002 until 2019/2020**, the approach to obtain D_QC is described in detail in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).

For the **forecasting seasons 2020/2021 and later**, D_QC is derived using the following approach:
1. *Combination of forecast (D_forecast) and nowcast (D_nowcast)*: If there was only one assessment available by an observer after a day in the field for a region, and if D_forecast = D_nowcast --> D_QC = D_forecast.
2. *Combination of several nowcast assessments (D_nowcast)*: If two (or more) observers agreed (or majority opinion) in their (independent) assessments of D_nowcast after a day in the field in the same warning region. --> D_QC = D_nowcast.
3. *Hindcast analysis (D_hindcast)*: In Switzerland, avalanche forecasters re-evaluate all situations when D = 4 (high) or D = 5 (very high) were either forecast, should have been forecast, or when forecasters discussed given one of these two levels but had not given them. Generally, two forecasters assess each situation. In these cases, D_QC = D_hindcast. The hindcast analysis, only available since the forecasting season 2020/2021, replaces what was step (2) in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).

All other cases - ties in case of (1) or (2), no new information from the warning region in question, or if no D_hindcast was available - are not considered quality-checked, and are, thus, not contained in the data set.

In addition to D_QC, the file contains information on the elevation and aspect, where D_QC likely prevails.
- The indicated elevation is the mean of the respective elevations in (1), (2), or (3). At danger level 1 (low), when no elevation is indicated in the Swiss forecast, a value of 1500 m is set.
- For the four cardinal aspects N, E, S, and W, a value of 1 means that there was agreement that D was reached in this aspect and a value of 0 means that there was agreement that D was not reached in this aspect. Intermediate values correspondingly mark disagreements in the assessments.

avalanche-bulletin

display_name: avalanche-bulletin

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forecast

display_name: avalanche-forecast

avalanche-forecasting

display_name: avalanche-forecasting

verification

display_name: verification

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: Re-analyzed regional avalanche danger levels in Switzerland


maintainer: Matthias Haeni

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

en: Site photo of the forest station (BTB)

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

mimetype: image/jpeg

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: Data plot of the forest station (BTB)

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

en: Field station data (BTF)

media_type: text/csv

en: Data file.

format: CSV

mimetype: text/csv

en: Site photo of the field station (BTF)

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

en: Data plot of the field station (BTF)

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

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


en: chondrichthyes atlantic-region teleo edna fish-composition actinopterygii bayofbiscay metabarcoding

en: Data access

en:
Environmental DNA complements scientific trawling in surveys of marine fish biodiversity (Dataset 2019)
In October 2019 we chose 15 sites from the 2019 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. 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

bayofbiscay

display_name: bayofbiscay

chondrichthyes

display_name: chondrichthyes

edna

display_name: edna

fish-composition

display_name: fish-composition

metabarcoding

display_name: metabarcoding

teleo

display_name: teleo

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Environmental DNA Marine France Evhoe 2019


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

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

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

en: Tree species profiles for 2019, Red band

en: Tree species profiles for 2020, EVI index

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

en: Tree species profiles for 2019, Green 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, Red band

en: Tree species profiles for 2020, NIR band

en: Tree species profiles for 2019, CIre Index

en: Tree species profiles for 2020, Blue band

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

en: Tree species profiles for 2020, NDMI index

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

en: Tree species profiles for 2019, NIR band

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

en: Tree species profiles for 2019, NDVI index

en: Tree species profiles for 2020, CCI index

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

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

en: Tree species profiles for 2020, Green band

en: Tree species profiles for 2020, CIre index

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

en: Tree species profiles for 2019, CCI index

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

en: Tree species profiles for 2019, Blue band

en: Tree species profiles for 2020, NDVI index

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*).

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

time-series

display_name: time-series

tree-species

display_name: tree-species

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.


tree-species-map-of-switzerland

maintainer: Tiziana Li Koch

title_for_slug: tree-species-map-of-switzerland

en: tree-species map remote-sensing switzerland forest

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

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Tree species map of Switzerland


maintainer: Peter Bebi

en: climate-change soil-temperature carbon-dioxide vegetation stillberg experiment

en: Project Description FACE Stillberg

media_type: text/html

en: Link to FACE project description on the WSL website.

format: HTML

en: Metadata FACE Stillberg Experimental Design

en: Schematic illustration and tabular listing of the FACE experimental design.

en: Metadata FACE Stillberg Data File Description

en: List of descriptions for each of the .csv FACE data files.

en: Data FACE Stillberg

media_type: application/zip

en: Folder in .zip format containing the FACE datasets.

format: ZIP

mimetype: application/zip

en:
 Background information

High elevation ecosystems are important in research about environmental change because shifts in climate associated with anthropogenic greenhouse gas emissions are predicted to be more pronounced in these areas compared to most other regions of the world. This project involved a Free Air CO2 Enrichment (FACE) and soil warming experiment located in a natural treeline environment near Davos, Switzerland (Stillberg, 2200 m a.s.l.). Elevated atmospheric CO2 concentrations (+200 ppm) were applied from 2001 until 2009, and a soil warming treatment (+4 °C) was applied from 2007 until 2012. The combined CO2 enrichment and warming treatment reflects conditions expected to occur in this region in approximately 2050. A broad range of ecological and biogeochemical research was carried out as part of this environmental change project.

 Experimental design

The experiment consisted of 40 hexagonal 1.1 m² plots, 20 with a *Pinus mugo* ssp. *uncinata* (mountain pine, evergreen) individual in the centre and 20 with a *Larix decidua* (European larch, deciduous) individual in the centre. A dense cover of understorey vegetation surrounded the tree in each plot, including the dominant dwarf shrub species *Vaccinium myrtillus* (bilberry), *Vaccinium gaultherioides* (group *V. uliginosum agg.*, northern bilberry) and *Empetrum nigrum* ssp. *hermaphroditum* (crowberry) plus several herbaceous and non-vascular species.
At the beginning of the experimental period, the 40 plots were assigned to ten groups of four neighbouring plots (two larch and two pine trees per group) in order to facilitate the logistics of CO2 distribution and regulation. Half of these groups were randomly assigned to an elevated CO2 treatment, while the remaining groups served as controls and received no additional CO2. In spring 2007, one plot of each tree species identity was randomly selected from each of the 10 CO2 treatment groups and assigned a soil warming treatment, yielding a balanced design with a replication of five individual plots for each combination of CO2 level, warming treatment and tree species.

 Data description

Soil and air conditions have been monitored closely throughout the study period, with most measurements made during the combined CO2 x warming experiment (2007-2009). The data comprise of air temperature, soil temperature, soil moisture, sapflow, tree diameter and CO2 measurements.

carbon-dioxide

display_name: carbon-dioxide

climate-change

display_name: climate-change

experiment

display_name: experiment

soil-temperature

display_name: soil-temperature

stillberg

display_name: stillberg

vegetation

display_name: vegetation

en: FACE: Stillberg CO2 enrichment and soil warming study


en:
Attention: this data is not updated after 2022 anymore.
This dataset contains the statistics on the number of avalanche fatalities per **calendar year** in Switzerland. The data collection commences with the beginning of the year 1937. After the completion of a hydrological year, which is the standard way avalanche fatalities are summarized in Switzerland and ends on the 30th of September, the new data is appended to the existing dataset.
If you require annual statistics per hydrological year, please download the data from here: [https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936]
The following information is contained (by column and column title):
 - year
 - number of fatalities in the backcountry (=tour)
 - number of fatalities in terrain close to ski areas (=offpiste, away from open and secured ski runs)
 - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors)
 - number of fatalities in or around buildings or in settlements (= buildings)
 - sum (of all four categories)
The definitions for these four categories, as described in the guidelines to the avalanche accident database are:
tour:
activities include back-country ski, snowboard or snow-shoe touring
offpiste:
access from ski area, generally from the top of a skilift with short hiking distances
transportation.corridors (Techel et al., 2016):
people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams)
buildings (Techel et al., 2016):
people inside or just outside buildings, and workers on high alpine building sites

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-fatalities

display_name: avalanche-fatalities

fatalities-in-or-around-buildings

display_name: fatalities-in-or-around-buildings

fatalities-in-settlements

display_name: fatalities-in-settlements

fatalities-in-the-backcountry

display_name: fatalities-in-the-backcountry

fatalities-on-transportation-corridors

display_name: fatalities-on-transportation-corridors

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: Number of avalanche fatalities per calendar year in Switzerland since 1937


en: raster-processing sentinel-2 switzerland remote-sensing force

media_type: image/tiff

en: An example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The date of the image is 24.07.2018.

format: TIFF

en: Parameter file that defined all the parameters for processing the Sentinel-2 Level 2 imagery

format: OCTET

en:
We processed Sentinel-2 images from 2017 to 2023 for Switzerland with the Software FORCE (Frantz 2019). The respective parameter files can be found here: [Github](https://github.com/TLKoch/Sentinel-2_CH). All the images consist of several TB and therefore, access will be granted upon request.
The available bands (in spatial reference system EPSG 3035) are the following:
*Red, Green, Blue, NIR, Red-Edge-1, Red-Edge-2, Red-Edge-3, SWIR-1, SWIR-2*
The available indices (in spatial reference system EPSG 3035) are the following:
*CCI, CIRE, NDWI/NDMI, NDVI, EVI*
Further indices might be provided upon request.

 Processing
After downloading Level 1 data for the 14 Sentinel-2 tiles covering Switzerland (T31TGN, T32TLT, T32TMT, T32UMU, T32TNT, T32TPT, T31TGM, T32TLS, T32TMS, T32TNS, T32TPS, T32TLR, T32TMR, T32TNR) with an estimated cloud cover of less than 80 \%, we processed the data further with FORCE v. 3.7.8-12.
FORCE was used for radiometric correction, including atmospheric and topographic corrections using the digital elevation model from Copernicus Land Monitoring Service (EU-DEM v. 1.1), with a 25 m spatial resolution, a bidirectional reflectance distribution function (BRDF) correction, and an adjacency effect correction. For cloud masking, the improved Fmask algorithm with buffers of 300 m for clouds, 90 m for shadows, and 30 m for snow were used (Frantz et al. 2018; Zhu et al. 2015). All bands were resampled using cubic convolution to 10 m spatial resolution. Shadows, low and poor illumination as well as saturation were masked out. The co-registration of all images was made with monthly composites of near-infrared Landsat Collection 2 images from 2014 to 2021 (Rengarajan et al. 2020;Rufin et al. 2021). With this the geometric consistency improved. The processed images are available in tiles of 30 by 30 km.

 Example images
Uploaded is an example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The date of the image is 24.07.2018.

force

display_name: force

raster-processing

display_name: raster-processing

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

educ

en: Education, culture and sport

en: Sentinel-2 imagery of Switzerland


en: ecological-indicator-values switzerland soil-mapping ecological-maps landolt soil-ph plants soil-moisture

en: Predictors

en: Predictors generated and used in Descombes et al. (2020)

en: Description

en: Description of the predictors provided in this dataset

en:
Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.

This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection ("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"). The excel file (xlsx) provides a short description of the raster layers.

Paper Citation:

Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

envidat

en: EnviDat: the Environmental Data Portal


maintainer: Anita C. Risch

en: nutrient-network grassland soil-chemistry global soil mineralization soil-biology worldwide

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

bioclim.T.ann: Mean annual temperature (°C)

bioclim.T.var: Temperature variability (°C)

bioclim.T.q.wet: Temperature of the wettest quarter (°C)

bioclim.T.q.dry: Temperature of the driest quarter (°C)

bioclim.T.q.warm: Temperature of the warmest quarter (°C)

bioclim.P.ann: Mean annual preciptiation (mm)

bioclim.P.q.wet: Precipitation of the wettest quarter (mm)

ch.pH: pH

ch.WSK.vol: Water holding capacity (vol%)

ch.bulkdensity.gcm3: Bulk density (g cm-3)

ch.porespace: Pore space (vol%)

ch.sand.perc: Soil texture: sand (%)

ch.silt.perc: Soil texture: silt (%)

ch.clay.perc: Soil texture: clay (%)

ch.NO3start.mgg: NO3 at the beginning of the incubation (mgkg)

ch.Ntot.perc: Total soil N (%)

ch.Ctot.perc: Total soil C (%)

ch.Corg.perc: Soil organic C (%)

ch.netNminfield.mgkgd: Realised soil net N mineralisation (mg kg-1 d-1)

ch.netNminlab.mgkgd: Potential soil ne N mineralisation (mg kg-1 d-1)

archaea.cpg.log: Ammonia oxidising archaea (archaeal amoA gene copies g-1 soil)

bacteria.cpg.log: Ammonia oxidising bacteria (bacterial amoA gene copies g-1 soil)

micr.biom: Microbial biomass (ug g-1 soil)

ch.CtoN: C to N ratio

ch.netNnitriffield.mgkgd.sqrt: Realised soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniffield.mgkgd.sqrt: Realised soil net ammonification, transformed (mg kg-1 d-1)

ch.netNnitriflab.mgkgd.sqrt: Potential soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniflab.mgkgd.sqrt: Potential soil net ammonifiation, transformed (mg kg-1 d-1)

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

Paper Citation:

Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2

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

We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil

display_name: soil

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide


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

en: Site aerial photo (Alpthal)

en: Site aerial photo from the Alpthal forest station. Source: Google. 2016. Site aerial photo from the Alpthal forest station. Retrieved November 11, 2016, from https://www.google.ch/maps

en: Data plot of the forest station (ALB)

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

en: Forest station data (ALB)

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 one meteorological station in Alpthal in Switzerland which is located within a natural coniferous forest (ALB) with Norway spruce (_Picea abies_; 180-230 yrs) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Alpthal 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

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


en: raster geodata sentinel-2 gridded-data video-encoding research-data-management core time-series

en: View publication in the Digital Object Repository at WSL (DORA)

en:
Shell script to extract data from the CORE format. You can 1) download and unzip the file, 2) make the script executable and 3) run it.
Disclaimer: only provided for peer review. Proper software will become available in time on https://github.com/EnviDat/?q=core (both Web/JS for Frontend and Python for Backend).

en: CORE Dataset (80 GB)

en:
Shell script to reproduce the CORE format. You can 1) download and unzip the file, 2) make the script executable and 3) run it.
Disclaimer: only provided for peer review. Proper software for working with CORE will become available in time on https://github.com/EnviDat/?q=core (both Web/JS for Frontend and Python for Backend).

en:
DISCLAIMER: CORE is still in development. Interested parties are warmly invited to join common development, to comment, discuss, find bugs, etc.

Acknowledgement: The CORE format was proudly inspired by the Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)) format, by considering how to leverage the ability of clients issuing HTTP GET range requests for a time-series of remote sensing and aerial imagery (instead of just one image).

License: The Cloud Optimized Raster Encoding (CORE) specifications are released to the public domain under a Creative Commons 1.0 CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

-----------------------

Summary:
The Cloud Optimized Raster Encoding (CORE) format is being developed for the efficient storage and management of gridded data by applying video encoding algorithms. It is mainly designed for the exchange and preservation of large time series data in environmental data repositories, while in the same time enabling more efficient workflows on the cloud. It can be applied to any large number of similar (in pixel size and image dimensions) raster data layers. CORE is not designed to replace COG but to work together with COG for a collection of many layers (e.g. by offering a fast preview of layers when switching between layers of a time series).

WARNING: Currently only applicable to RGB/Byte imagery. The final CORE specifications may probably be very different from what is written herein or CORE may not ever become productive due to a myriad of reasons (see also 'Major issues to be solved').

With this early public sharing of the format we explicitly support the Open Science agenda, which implies "shifting from the standard practices of publishing research results in scientific publications towards sharing and using all available knowledge at an earlier stage in the research process" (quote from: European Commission, Directorate General for Research and Innovation, 2016. Open innovation, open science, open to the world).

CORE Specifications:

1) a MP4 or WebM video digital multimedia container format (or any future video container playable as HTML video in major browsers)

2) a free to use or open video compression codec such as H.264, VP9, or AV1 (or any future video codec that is open sourced or free to use for end users)

Note: H.264 is currently recommended because of the wide usage with support in all major browsers, fast encoding due to acceleration in hardware (which is currently not the case for AV1 or VP9) and the fact that MPEG LA has allowed the free use for streaming video that is free to the end users. However, please note that H.264 is restricted by patents and its use in proprietary or commercial software requires the payment of royalties to [MPEG LA](https://www.mpegla.com/programs/avc-h-264/). However, when AV1 matures and accelerated hardware encoding becomes available, AV1 is expected to offer 30% to 50% smaller file size in comparison with H.264, while retaining the [same quality](https://trac.ffmpeg.org/wiki/Encode/AV1).

3) the encoding frame rate should be of one frame per second (fps) with each layer segmented in internal tiles, similar to COG, ordered by the main use case when accessing the data: either layer contiguous or tile contiguous;

Note: The internal tile arrangement should support an easy navigation inside the CORE video format, depending on the use case.

4) a CORE file is optimised for streaming with the moov atom at the beginning of the file (e.g. with -movflags faststart) and optional additional optimisations depending on the codec used (e.g. -tune fastdecode -tune zerolatency for H.264)

5) metadata tags inside the moov atom for describing and using geographic image data (that are preferably compatible with the [OGC GeoTIFF standard](https://www.ogc.org/standards/geotiff) or any future standard accepted by the geospatial community) as well as list of original file names corresponding to each CORE layer

6) it needs to encode similar source rasters (such as time series of rasters with the same extent and resolution, or different tiles of the same product; each input raster should be having the same image and pixel size)

7) it provides a mechanism for addressing and requesting overviews (lower resolution data) for a fast display in web browser depending on the map scale (currently external overviews)

Major issues to be solved:

- Internal overviews (similar to COG), by chaining lower resolution videos in the same MP4 container for fast access to overviews first); Currently, overviews are kept as separate files, as external overviews.

- Metadata encoding (how to best encode spatial extent, layer names, and so on, for each of the layer inside the series, which may have a different geographical extent, etc...; Known issues: adding too many tags with FFmpeg which are not part of the standard MP4 moov atom; metadata tags have a limited string length.

- Applicability beyond RGB/Byte datasets; defining a standard way of converting cell values from Int16/UInt16/UInt32/Int32/Float32/Float64/ data types into multi-band Byte values (and reconstructing them back to the original data type within acceptable thresholds)

Example

Notice: The provided CORE (.mp4) examples contain modified Copernicus Sentinel data [2018-2021].

For generating the CORE examples provided, 50 original Sentinel 2 (S-2) TCI data images from an area located inside Switzerland were downloaded from www.copernicus.eu, and then transformed into CORE format using ffmpeg with H.264 encoding using the [x264 library](https://www.videolan.org/developers/x264.html).

For full reproducibility, we provide the original data set and results, as well scripts for data encoding and extraction (see resources).

core

display_name: core

geodata

display_name: geodata

gridded-data

display_name: gridded-data

raster

display_name: raster

research-data-management

display_name: research-data-management

time-series

display_name: time-series

video-encoding

display_name: video-encoding

educ

en: Education, culture and sport


en: radiation-transfer biodiversity forest climate-change statistical-interpolation microclimate ecology

en: Predicted microclimate air temperatures at 100 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Predicted microclimate air temperatures at 5 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Predicted microclimate topsoil temperatures at 5 cm below ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate-change impacts on biodiversity and ecosystem functioning. Here we combine a national network of microclimate temperature measurements with a novel radiative transfer model to map monthly minimum, mean and maximum temperatures during the vegetation period at a 10 meter spatial resolution across Switzerland. The temperature measurements took place in 107 sampling plots distributed across different habitat types, with 62 plots in forests, 22 below trees outside forests, and 23 in open grasslands. In each plot we measured temperature in the topsoil (-5cm), as well as in the air at 5cm and 100cm height above ground. Spatial interpolation was achieved by using a hybrid approach based on linear mixed effects models with input from detailed radiation estimates that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography and vegetation height. Our data reveals strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 °C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 °C. We also found that in open grasslands, maximum temperatures at 5 cm above ground are on average 3.4 °C warmer than that of macroclimate, suggesting that in such habitats heat exposure close to the ground is often underestimated when using macroclimatic data. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by RSME’s ranging from 1.18 to 3.43 °C, with minimum temperatures generally being predicted more accurately than maximum temperatures. The microclimate maps presented here enable a more biologically relevant perspective when analysing climate-species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

biodiversity

display_name: biodiversity

climate-change

display_name: climate-change

ecology

display_name: ecology

forest

display_name: forest

microclimate

display_name: microclimate

radiation-transfer

display_name: radiation-transfer

statistical-interpolation

display_name: statistical-interpolation

envidat

en: EnviDat: the Environmental Data Portal

en: Monthly topsoil and near surface microclimate temperature data for Switzerland


maintainer: Justine Berg

en: hicar fsm2trans snow-hydrology fsm2 dischma snowpack

en: Input data December 2016 HICAR 50m resolution

en: input data november 2016 COSD

en: Input data June 2017 HICAR 100m resolution

en: Input data october 2016 HICAR 100m resolution

en: Input data July 2017 COSD

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

en: Input data May 2017 HICAR 50m resolution

en: Input data January 2017 HICAR 50m resolution

en: Input data April 2017 COSD

en: Input data January 2017 HICAR 100m resolution

en: Input data July 2017 HICAR 250m resolution

en: Input data February 2017 HICAR 100m resolution

en: input data october 2016 COSD

en: Input data october 2016 HICAR 50m resolution

en: Input data December 2016 HICAR 100m resolution

en: Input data November 2016 HICAR 50m resolution

en: Input data April 2017 HICAR 250m resolution

en: Available input files for running with FSM2trans

en: Input data June 2017 HICAR 50m resolution

en: Input data March 2017 HICAR 50m resolution

en: Input data February 2017 HICAR 250m resolution

en: Input data June 2017 HICAR 250m resolution

en: Input data May 2017 COSD

en: Input data December 2016 HICAR 250m resolution

en: Input data February 2017 HICAR 50m resolution

en: Input data April 2017 HICAR 100m resolution

en: Input data May 2017 HICAR 100m resolution

en: Input data March 2017 HICAR 100m resolution

en: Input data October 2016 HICAR 250m resolution

en: Input data January 2017 HICAR 250m resolution

en: Input data July 2017 HICAR 100m resolution

en: Input data May 2017 HICAR 250m resolution

en: Input data July 2017 HICAR 50m resolution

en: Input data March 2017 HICAR 250m resolution

en: Input data November 2016 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 April 2017 HICAR 50m resolution

en: Input data november 2016 HICAR 100m 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.

en: FSM2trans snowpack simulations with HICAR input


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

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

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

active-layer

display_name: active-layer

arctic

display_name: arctic

bacteria

display_name: bacteria

biobank

display_name: biobank

collections

display_name: collections

conservation

display_name: conservation

extreme-habitat

display_name: extreme-habitat

fungi

display_name: fungi

glaciers

display_name: glaciers

permafrost

display_name: permafrost

plastisphere

display_name: plastisphere

swiss-alps

display_name: swiss-alps

educ

en: Education, culture and sport

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


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

en: Data plot of the field station (NEF)

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

en: Field station data (NEF)

en: Data file.

en: Forest station data (NEB)

en: Data plot of the forest station (NEB)

en: Site photo of the field station (NEF)

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

en: Site aerial photo (Neunkirch)

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

en: Site photo of the forest station (NEB)

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

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

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


soil-respiration-exclosure-experiment

title_for_slug: soil-respiration-exclosure-experiment

en: grazing subalpine ungulates grassland soil-respiration

en: Soil respiration Switzerland

en:
ID: Fence number

Year	:Year of sampling

mean.CO2: soil respiration measured with PP-systems SRC-1 soil respriation chamber attached to a EGM-4 infrared gas analyser; units CO2 g m-2 h-1

Grassland: Grasslands where fences were errected. Note that on large grasslands (STAB, GRIM, MING) 2 pairs of fences were errected (2 in short, 2 in tall-grass vegetation), on small grasslands (DADA, MARG, BOTS) only one pair of fences was errected (1 short-grass; one-tallgrass vegetation)

Fence: Fence number; un-even numbers on short-grass vegetation (1,3,5,7,9,11,13,15,17), even numbers on tall-grass vegetation (2,4,6,8,10,12,14,16,18)

Vegetation:	1 = shortgrass vegetation, 2 = tall-grass vegetation (9 replicate fences on each vegetation type)

Treatment	: 1 = all herbivores grazing, 2 = large ungulates (red deer, chamois) excluded

en:
Location of data collection
The Swiss National Park (SNP) is located in the southeastern part of Switzerland, and covers an area of 170 km2, 50 km2 of which is forested, 33 km2 is occupied by alpine and 3 km2 by subalpine grasslands. Elevations range from 1350 to 3170 m a.s.l., and mean annual precipitation and temperature are 871 mm and 0.6°C measured at the Park’s weather station in Buffalora (1980 m a.s.l.) between 1960 and 2009 (MeteoSchweiz 2011). Founded in 1914, the SNP received minimal human disturbance for almost 100 years (no hunting, fishing, or camping, visitors are not allowed to leave the trails). Large (> 1 ha) homogeneous patches of short- and tall-grass vegetation characterize the subalpine grasslands. The average vegetation height of short-grass vegetation is 2 to 5 cm. Red fescue (Festuca rubra L.), quaking grass (Briza media L.) and common bent grass (Agrostis tenuis Sipthrob) are the predominating plant species in this vegetation type. Tussocks of evergreen sedge (Carex sempervirens Vill.) and mat grass (Nardus stricta L.) are predominant in the tall-grass vegetation, which averages 20 cm in vegetation height (Schütz and others 2006). Short-grass vegetation developed in areas where cattle and sheep rested (high nutrient input) during agricultural land-use (from 14th century until 1914); tall-grass vegetation developed in areas where cattle and sheep used to graze, but did not rest (Schütz and others 2003, 2006). Herbivores were shown to consume > 60% of the biomass in short-grass compared to < 20% in tall-grass vegetation (Schütz and others 2006). The herbivore community present in the SNP can be divided into four groups based on body size/weight: large [red deer (Cervus elaphus L.) and chamois (Rupricapra rupricapra L.); 30 - 150 kg], medium [marmot (Marmota marmota L.) and snow hare (Lepus timidus L.); 3 – 6 kg], and small vertebrate herbivores (small rodents: e.g. Clethrionomys spp., Microtus spp., Apodemus spp.; 30 – 100 g) as well as invertebrates (e.g. grasshoppers, caterpillars, cicadas, < 5 g).

Experimental design
We selected 18 subalpine grassland sites (9 short-grass, 9 tall-grass vegetation). The sites were spread across the entire park on dolomite parent material at altitudes of 1975 to 2300 meters. At each site we established an exclosure network (fences) in spring 2009 (early June), immediately after snowmelt. Each exclosure network consisted of a total of five 2 × 3 m sized plots that progressively excluded the different herbivores listed above (further labeled according to the herbivore guilds that had access to the respective plots “All”, “Marmot/Mice/Invertebrates”, “Mice/Invertebrates”, “Invertebrates”, “None”). The “All” treatment was thus accessible to all herbivores, was not fenced and was located at least 5 m away from a 2.1 m tall and 7 × 9 m main fence that enclosed the other four treatments. This fence was constructed of 10 × 10 cm wooden posts and electrical equestrian tape (AGRARO ECO, Landi, Bern, Switzerland; 20 mm width) mounted at 0.7 m, 0.95 m, 1.2 m, 1.5 m and 2.1 m above the ground that were connected to a solar charged battery (AGRARO Sunpower S250, Landi, Bern, Switzerland). We also mounted non-electrically charged equestrian tape at 0.5 m to help exclude deer and chamois, yet allow marmots and hares to enter safely. Within each main fenced area we randomly established four 2 × 3 m plots: (1) The “Marmot/Mice/Invertebrates” plot remained unfenced, thus, with the exception of red deer and chamois, all herbivores were able to access the plot, (2) The “Mice/Invertebrates” plot consisted of a 90 cm high electric sheep fence (AGRARO Weidezaunnetz ECO, Landi, Bern, Switzerland; mesh size 10 × 10 cm) connected to the solar panel and excluded all medium sized mammals (marmots, hares), but provided access for small mammals and invertebrates, (3) The “Invertebrates” plot provided access for invertebrates only and was surrounded by 1 m high metal mesh (Hortima AG, Hausen, Schweiz; mesh size 2 × 2 cm), (4) The “None” plot was surrounded by a 1 m tall mosquito net (Sala Ferramenta AG, Biasca, Switzerland; mesh size 1.5 × 2 mm) to exclude all herbivores. This plot was covered with a roof constructed of a wooden frame lined with mosquito mesh that was mounted on the wooden corner posts. We also treated this plot with a biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) when needed to remove insects that might have entered during data collection or that hatched from the soil.

!!! The here published data set only contains data for “All”, and “Marmot/Mice/Invertebrates” (= ungulates excluded) plots !!!

Data collection
In-situ soil CO2 emissions were measured with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA) on two randomly selected locations on one subplot within each of the 90 plots. For each measurement the soil chamber (15 cm high; 10 cm diameter) was placed on a permanently installed PVC collar (10 cm diameter) driven five centimeters into the soil at the beginning of the study (June 2009). The measurements were conducted between 0900 and 1700 hours every two weeks from early to early September 2009, 2010, 2011 and 2013. Freshly germinated plants growing within the PVC collars were removed prior to each measurement to avoid measuring plant respiration/photosynthesis. The two measurements collected per plot every two weeks were averaged.

Please acknowledge the funding of the study: funded by the Swiss National Science Foundation, SNF grant-no 31003A_122009/1 to Anita C. Risch, Martin Schütz and Flurin Filli

grassland

display_name: grassland

grazing

display_name: grazing

soil-respiration

display_name: soil-respiration

subalpine

display_name: subalpine

ungulates

display_name: ungulates

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: Soil respiration - exclosure experiment


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

en: A condensed instruction for running the presented simulations.

en:
1) Simulation environment TMEnvironmentExampleApplications for the simulation of the case study applications. It contains a) simulation settings (folder C), b) species parameters (folder P), c) folders S, R and P for the state files and results and plots to be produced, and the binaries of the TreeMig model.
2) Input data for Switzerland in TMInputData (Climate, soil, land cover, DEM)
3) Several R scripts (folder Scripts) to run and plot the simulations directly from R without having to use the graphical user interface.
For learning how to use TreeMig we recommend the starter kit https://www.doi.org/10.16904/envidat.533.

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

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

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

bioclimatic-variables

display_name: bioclimatic-variables

example-applications

display_name: example-applications

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-management

display_name: forest-management

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-migration

display_name: species-migration

treemig

display_name: treemig

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


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

en: Forest station data (ISB)

en: Data file.

en: Data plot of the field station (ISF)

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

en: Site aerial photo (Isone)

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

en: Data plot of the forest station (ISB)

en: Field station data (ISF)

en: Site photo of the forest station (ISB)

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

en: Site photo of the field station (ISF)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Isone in Switzerland where one station is located within a natural broad-leaved forest stand (ISB) with European beech (_Fagus sylvatica_; 70-100 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, ISF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Isone 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

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


en: deposition snow turbulence atmosphere dust hill particles

en: preferential deposition patterns of all simulations performed

en: The data consist of matlab files. Each files contains a 2D array with the values of deposition at the surface nodes. Each array has Nx x Ny values. Please refer to the original paper for additional information on the spatial resolution and grid size.

en:
Preferential deposition of snow and dust over complex terrain is responsible for a wide range of environmental processes, and accounts for a significant source of uncertainty in surface mass balances of cold and arid regions. Despite the growing body of literature on the subject, previous studies reported contradictory results on the location and magnitude of deposition maxima and minima. This study aims at unraveling the governing processes of preferential deposition in neutrally stable atmosphere and to reconcile seemingly inconsistent results of previous works. For this purpose, a comprehensive modeling approach is developed, based on large eddy simulations of the turbulent airflow, Lagrangian stochastic model of particle trajectories, and immersed-boundary method to represent the underlying topography. The model performance is tested against wind tunnel measurements of dust deposition around isolated and sequential hills. A scale analysis is then performed to investigate the dependence of snowfall deposition on the particle Froude and Stokes numbers, which fully account for the governing processes of inertia, flow advection, and gravity. Additional simulations are performed, to test whether the often used assumption of inertialess particles yields accurate deposition patterns. We finally show that our scale analysis provides qualitatively similar results for hills with different aspect ratios.

This dataset contains the results of the LES-LSM model. Each Matlab file contains a 2D array of deposition values (in kg/m2) in each surface node (ix, iy) of the Cartesian grid. The file names are consistent with the simulation numbers listed in the original paper. For additional information, please refer to "Preferential deposition of snow and dust over hills: governing processes and relevant scales" by F. Comola, M. G. Giometto, S. T. Salesky, M. B. Parlange, and M. Lehning, Journal of Geophysical Research: Atmospheres, 2019.

atmosphere

display_name: atmosphere

deposition

display_name: deposition

dust

display_name: dust

hill

display_name: hill

particles

display_name: particles

snow

display_name: snow

turbulence

display_name: turbulence

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.


data-konzeptionelles-systemmodell

maintainer: Matthias Bürgi

title_for_slug: data-konzeptionelles-systemmodell

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

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.

landscape-change

display_name: landscape-change

policy-action

display_name: policy-action

qualitative-research

display_name: qualitative-research

scenarios

display_name: scenarios

en: Data Konzeptionelles Systemmodell


maintainer: Gesa von Hirschheydt

en: occupancy-model swisslichens detectability monitoring biodiversity sessile-species bayesian-inference lichens detection-probability cryptogams observer-error inter-observer-difference

en: Repository containing the full data, code, README and workflow files.

en:
The available lichen data consists of detection/nondetection data (1/0) of 373 tree-inhabiting (*corticolous*) lichen species from 416 plots surveyed 1-2 times. The lichen data were originally collected for the purpose of the Red List of epiphytic lichen species in Switzerland ([Scheidegger *et al.* 2002](https://www.bafu.admin.ch/bafu/de/home/themen/biodiversitaet/publikationen-studien/publikationen/rote-liste-gefaehrdete-arten-baum-erdbewohnende-flechten.html)), but updated to recent nomenclature for the purpose of this study.
This repository contains all the supporting data and R code for the paper:
von Hirschheydt, G., Kéry, M., Ekman, S., Stofer, S., Dietrich, M., Keller, C., Scheidegger, C. (2024) **Occupancy model reveals limited detectability of lichens in a standardised large-scale monitoring**. *Journal of Vegetation Science*.
Results and figures presented in the manuscript should be reproducible (with small differences in the latter digits due to stochasticity of the MCMC sampler) with the provided data and code.
The downloadable `.zip` folder has the following structure:
* `0_data/`
* `1_code/`
* `2_output/`
* `lichen_detectability.Rproj`
* `README.txt`
* `workflow.html`
- `workflow.Rmd`

The main folder and the three subordinate folders each have their own `README*.txt` file. These describe each available file in detail and should be consulted prior to using the data or running any code. The file `lichen_detectability.Rproj` stores the information about the R project. The user can open the project by clicking/double-clicking on this file which will automatically define the repository as working directory for the R session. If the user does not use RStudio/Posit, they may have to set the working directory manually to the stored location in the R files. The files `workflow.*` guide the user through the analysis (`1_code/*.R`) in the correct order so that they can:
- bundle the cleaned data into a data list readable for JAGS
- fit the multi-species occupancy model to the data and store the output
- assess the goodness-of-fit of the model to the data
- conduct a prior sensitivity analysis with 2 additional sets of priors
- extract the summary statistics reported in the manuscript and supplementary materials
- generate the figures shown in the manuscript and supplementary materials

bayesian-inference

display_name: bayesian-inference

biodiversity

display_name: biodiversity

cryptogams

display_name: cryptogams

detectability

display_name: detectability

detection-probability

display_name: detection-probability

inter-observer-difference

display_name: inter-observer-difference

lichens

display_name: lichens

monitoring

display_name: monitoring

observer-error

display_name: observer-error

occupancy-model

display_name: occupancy-model

sessile-species

display_name: sessile-species

swisslichens

display_name: swisslichens

en: Repeated detection-nondetection data of corticolous lichens from a standardised monitoring across Switzerland


en: sea-ice drone snow antarctica irra uav snow-temperature snow-roughness

en:
This dataset accompanies the publication "How Flat is Flat? Investigating the spatial variability of snow surface temperature and roughness on landfast sea ice using UAVs in McMurdo Sound, Antarctica".
The dataset consists of 1) processed orthomosaic of RGB values, snow topography (digital elevation model), and surface temperature, 2) topography-dependent irradiance map, and 3) point data of measured radiation and atmospheric parameters in the McMurdo Sound, October-December 2022. The data was collected as part of the New Zealand Marsden Fund Research Grant 21-VUW-103 "Can Snow Change the Fate of Antarctic Sea Ice?". Unprocessed data associated with this field campaign are published in 10.16904/envidat.633.
For the airborne measurements, we used a DJI Matrice 30T, a multi-rotor (quadcopter) UAV with self-heating batteries, suitable for operations in polar conditions (temperature range from -20 to +50 °C). The UAV has a wide-angle camera (12 megapixels) for RGB images and a thermal camera (Uncooled Vox Microbolometer, long-wave infrared spectrum 8-14 µm, 1.31 megapixels) for TIR images with an accuracy of ±2 °C or ± 2 %.
GNSS data includes high-precision location information (latitude, longitude, ellipsoidal height, receiver clock offset, and timestamps formatted in decimal hours and day-of-year notation).
WE calculated the distribution of received solar irradiance over the sea-ice surface using a terrain-corrected radiative transfer model and measured shortwave radiation components, accounting for surface roughness, aspect, slope, and solar position during the flight, while applying terrain shading and measured diffuse radiation.
The broadband radiation station data consists of shortwave (Sw) and longwave (Lw) radiation measurements (Kipp & Zonen CMP22 pyranometers and CGR4 pyrgeometers).
Surface temperature readings from cold, sediment, and hot targets were recorded using Apogee SI-121-SS sensors.
This dataset also contains automated weather station (AWS) data (air and snow surface temperatures, relative humidity, wind direction, and wind speed).
For detailed information, please use the README file.

antarctica

display_name: antarctica

drone

display_name: drone

irra

display_name: irra

sea-ice

display_name: sea-ice

snow

display_name: snow

snow-roughness

display_name: snow-roughness

snow-temperature

display_name: snow-temperature

uav

display_name: uav

en: Snow On Antarctic Sea Ice - McMurdoSound 2022 - UAV Retrievals of Snow Topography and Snow Surface Temperature


maintainer: Varun Sharma

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

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

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

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

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

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

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

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

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

The datasets consist of two folders organized in zip format:

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

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

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

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

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

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

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

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

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

alps

display_name: alps

blowing-snow

display_name: blowing-snow

mesoscale-modelling

display_name: mesoscale-modelling

snow-atmosphere-interaction

display_name: snow-atmosphere-interaction

en: Reproducibility Dataset for CRYOWRF v1.0


en: vascular-plant biodiversity land-use-abandonment grassland-management disturbance

en: Boch et al. - Vegetation records Alpengarten Schynige Platte 2010-2015.xlsx

en: Vegetation records (species lists, percentage cover of species) of a six-year experiment to test whether different land-use treatments affect species diversity and composition in alpine to subalpine graslands.

en: This dataset was used for the publication "Boch, Blaser, Föhr, Küchler, Fischer (2024) Haymaking complemented by moderate disturbances can sustain and restore species-rich alpine to subalpine grasslands" in Alpine Botany. It contains vegetation records (species lists, percentage cover of species) of six grassland sites with each five 2x2 m treatment plots established in the Schynige Platte Alpine Botanical Garden. In a six-year experiment, we tested the response of plant species richness and mean ecological indicator values that we calculated from this dataset, to management treatments ranging from combined haymaking and scraping, to haymaking or scraping alone, to complete abandonment. We further tested whether the treatments led to changes in species composition, i.e. shifts to different vegetation types.

biodiversity

display_name: biodiversity

disturbance

display_name: disturbance

grassland-management

display_name: grassland-management

land-use-abandonment

display_name: land-use-abandonment

vascular-plant

display_name: vascular-plant

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Schynige Platte Alpine Botanical Garden land-use experiment


maintainer: Irmi Seidl

en: community-energy energy-cooperatives cooperatives switzerland renewable-energy

en: Raw date (SPSS)

en: Raw data in sav. format (SPSS). For comprehension of the data and variable structure, please consult the modified questionnaire with variable names.

en: Questionnaire with variable names

en: Modified questionnaire with variable names for each item. Should be used together with the data files (.csv or .sav) to reconstruct the data and variable structures.

en: Raw data (.csv)

en: Raw data in csv. format. For comprehension of the data and variable structure, please consult the codebook and the modified questionnaire with variable names.

en: Codebook

en: Description of variable attributes and valid n

en:
 Topic of Survey
The data at hand on energy cooperatives in Switzerland were collected in 2016 as part of the project "Collective financing of renewable energy projects in Switzerland and Germany" of the National Research Programme 71 "Managing Energy Consumption". The cooperatives were surveyed on their organizational structure, their activities in electricity and heat generation, their finances, the political context and their assessments of the future.

 Survey Method
The survey was targeted at all energy cooperatives in Switzerland (this is the basic population). The Swiss Commercial Register was searched for cooperatives and specific keywords in order to determine this basic population and collect addresses. This search in May 2016 resulted in a total of 304 energy cooperatives, to which a questionnaire was sent in July 2016. A pre-test with 8 persons had been carried out before the questionnaire was sent out. The questionnaire was provided in German and French. It was sent by mail and an attached letter referred to a link for the digital version if preferred. The online version was designed with the software "Sawtooth". After three weeks, a first, and after six weeks a second reminder letter was sent to those cooperatives that had not yet completed the questionnaire. The returned hardcopy questionnaires were manually entered into the database and then combined with the electronic data from the online survey. In the course of the survey, the total population was reduced from 304 to 289: in 4 cases the survey was not deliverable, 4 cooperatives had dissolved, 6 were not actually energy cooperatives, 1 case had recently changed its legal form. With a response rate of 47%, the final data set comprises 136 responses (from 77 digital and 59 hardcopy questionnaires). However, not all 136 of the returned questionnaires were filled out completely. We checked for answers that seemed contradictory or incomprehensible. If an error could be clearly identified and the correct answer derived, the answer was adjusted, otherwise the answer was replaced by "missing data".

 Anonymization
Participating cooperatives have been assured that their information will be kept confidential and will only be made public anonymously. For this reason, the data have been anonymized in in order to prevent any identification of individual cooperatives.

 How to Use the Data
* The data are available in CSV and SPSS (sav.) format.
* A codebook and a modified version of the used questionnaire are provided to illustrate the data and variable structure. In the questionnaire, the variable names are assigned to the corresponding questions. In the codebook, further information on these variables (valid n, answer categories) can be found. This information (of the codebook) is already integrated in the SPSS file.

 Current Embargo on Data
These data are currently under embargo and will only be released when the project is completed (not before 2020).

#Additional Information
* The used questionnaire is provided in German and French.
* Descriptive results of the survey were published in a WSL report: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:18943

community-energy

display_name: community-energy

cooperatives

display_name: cooperatives

energy-cooperatives

display_name: energy-cooperatives

renewable-energy

display_name: renewable-energy

switzerland

display_name: switzerland

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: Energy Cooperatives in Switzerland: Survey Results // Energiegenossenschaften in der Schweiz: Befragungsergebnisse


en: model-calibration-data model-validation-data snow-density snow-depth snow-water-equivalent

en: swe2hs calibration and validation data

en:
 Contents of this archive

 README.md

 A readme file containing a description of the other files.

 automatic_stations_validation_data.csv

 Daily data from the automatic weather stations used for validation.

 Data sources and providers are listed in the SWE2HS paper, Table 1.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * site_id: code for the measurement site
  - HS_interpolated: boolean flag for interpolated values
  - SWE_interpolated: : boolean flag for interpolated values

 coordinates_swe2hs_paper_stations.csv

 Coordinates of all measurement sites used in the SWE2HS paper.

 Columns:

  * site_id: code for the measurement site
  * location_name: location name of the measurement site
  * lon_[wgs84]: longitude in WGS84
  * lat_[wgs84]: latitude in WGS84
  * elevation_[m]: atitude of the measurement site in [m]
  * dataset: dataset in which the station is included

 manual_station_calibration_data.csv

 Calibration data from the Swiss manual observer stations. Long station records
 trmmed to 15 random water-years.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * SWE_from_profile_[m]: biweekly measurement from the SWE in the measurement
   pit. Corrected with the bulk density in order to match the snow depth from
   the measurement stake. Please refer to the paper for further details.
  * site_id: code for the measurement site

 manual_stations_validation_data.csv

 Validation data from the Swiss manual observer stations. Remaining water-years
 from the long sttaion record not used for the calibration data.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * SWE_from_profile_[m]: biweekly measurement from the SWE in the measurement
   pit. Corrected with the bulk density in order to match the snow depth from
   the measurement stake. Please refer to the paper for further details.
  * site_id: code for the measurement site

 manual_stations_used_deltasnow_parameters.csv

 Site specific DeltaSNOW model parameters used for the calculation of the daily
 SWE series in 'manual_stations_calibration_data.csv' and
 'manual_stations_validation_data.csv'.

 Parameters were optimized by reducing RMSE to the biweekly snow pit SWE measurements.
 Please refer to the paper for further details.

 A python implementation of the DeltaSNOW model was used for the calculation:
 pydeltasnow (https://pypi.org/project/pydeltasnow/)

 For a detailed description of the DeltaSNOW model parameters please see the
 respective publication:

 Winkler, M., Schellander, H., & Gruber, S. (2021). Snow water equivalents
 exclusively from snow depths and their temporal changes: the Δ snow model.
 Hydrology and Earth System Sciences, 25(3), 1165-1187.

 Columns:

  * site_id: code for the measurement site
  * rho_max: Maximum density of an individual snow layer produced by the DeltaSNOW model in [kg/m3]
  * rho_null: Fresh snow density for a newly created layer [kg/m3
  * c_ov: Overburden factor due to fresh snow [-]
  * k_ov: Defines the impact of the individual layer density on the compaction due to overburden [-]
  * k: Exponent of the exponential-law compaction [m3/kg]
  * tau: Uncertainty bound [m]
  * eta_null: Effective compactive viscosity of snow for “zero-density” [Pa s]

en:
The data in this repository was used for the calibration and validation of the
SWE2HS model in the following publication:
Aschauer, J., Michel, A., Jonas, T., & Marty, C. (2023). An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development Discussions, 1-19. https://doi.org/10.5194/gmd-2022-258
Contains daily snow water equivalent and snow depth timeseries from stations in the European Alps.

model-calibration-data

display_name: model-calibration-data

model-validation-data

display_name: model-validation-data

snow-density

display_name: snow-density

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

en: SWE2HS model calibration and validation data