maintainer: Frank Techel

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

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

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

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

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

en: Re-analyzed regional avalanche danger levels in Switzerland


maintainer: EnviDat Support

en: climate-change-impact model discharge hydrology water-temperature

state: active

type: dataset

en: Data access

format: SERVICE

en:
This work presents the first extensive study of climate change impacts on rivers temperature in Switzerland. Results show that even for low emissions scenarios, water temperature increase will lead to adverse effect for both ecosystems and socioeconomic sectors (such as nuclear plant cooling) throughout the 21st century. For high emissions scenarios, the effect will be worsen. This study also shows that water warming in summer will be more important in Alpine regions than in lowlands.
This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).

climate-change-impact

display_name: climate-change-impact

discharge

display_name: discharge

hydrology

display_name: hydrology

model

display_name: model

water-temperature

display_name: water-temperature

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: Dataset for: Future water temperature of rivers in Switzerland under climate change investigated with physics-based models


en: timeseries precipitation century climate minimum-temperature air-temperature free-climate-data climate-layers maximum-temperature

en: CHELSA Technical Documentation

media_type: application/pdf

en: As the CHELSA project is continuously expanded, certain new parameters, time scales etc. are not included anymore in the original publication (Karger et al. 2017). We are therefore provide this document as a guideline of the current climatic parameters available and will expand it as new parameters or time periods will become available. Please refer to the version history for changes made in the document.

format: PDF

mimetype: application/pdf

en: Data Access

format: GeoTIFF

en:
CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum. Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.

CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

air-temperature

display_name: air-temperature

century

display_name: century

climate

display_name: climate

climate-layers

display_name: climate-layers

free-climate-data

display_name: free-climate-data

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

timeseries

display_name: timeseries

en: CHELSAcruts -  High resolution temperature and precipitation timeseries for the 20th century and beyond


maintainer: Mathias Bavay

en: shortwave-radiation stillberg precipitation-sum wind-speed air-pressure soil-temperature air-temperature snow-depth treeline dew-point-temperature wind-direction relative-humidity

media_type: application/zip

en: The Davos Stillberg meteorological data (Stillberg_meteo_v1.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological research site. Metadata are provided in the folder 'Stillberg_meteo_metadata' and the data in the folder 'Stillberg_meteo_data' with subfolders for the three quality levels 'raw_standardized', ‘raw_edited’, and ‘filtered’.

format: ZIP

en: The Davos Stillberg meteorological data (Stillberg_meteo_v2.0.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological resea...

en: The Davos Stillberg meteorological data (Stillberg_meteo_v1.1.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological research site. Metadata are provided in the folder 'Stillberg_meteo_metadata' and the data in the folder 'Stillberg_meteo_data' with subfolders for the three quality levels 'raw_standardized', ‘raw_edited’, and ‘filtered’.

en:
 Background information
The Stillberg ecological treeline research site is located in the transition zone between the relatively humid climate of the Northern Alps and the continental climate of the Central Alps. In 1975, 92,000 seedlings of the high-elevation conifer species *Larix decidua* Mill. (European larch), *Pinus cembra* L. (Cembran pine), and *Pinus mugo* ssp. *uncinata* (DC.) Domin (mountain pine) were systematically planted across an area of 5 hectares along an elevation gradient of about 150 metres, with the aim to develop ecologically, technically, and economically sustainable afforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Alongside the ecological long-term monitoring of the afforestation, several meteorological stations have recorded local meteorological conditions at the Stillberg research site. Here, we provide the Davos Stillberg meteorological timeseries of five stations from 1975 (01-01-1975), the year of the afforestation establishment, until the end of the year 2022 (31-12-2022).

 Station description
The five meteorological stations were all installed at the same location (46°46′25.015″N 9°52′01.792″E) at 2090 m a.s.l., in the lower part of the afforestation area. In general, the five stations were operated sequentially (Stillberg_meteo_metadata_stations_v1.csv). However, there are some overlapping time periods when more than one station was operated in parallel. The stations have recorded environmental parameters, such as air and soil temperature, dew point temperature, air pressure, relative humidity, wind direction and velocity, radiation, precipitation, and snow depth (Stillberg_meteo_metadata_parameters_v1.csv). The meteorological measurements were recorded hourly from 1975 until 1996 and have been recorded in 10-minute intervals since 1997.

 Data description
We processed the Davos Stillberg meteorological timeseries with the MeteoIO meteorological data pre-processing library (Bavay & Egger, 2014). Data files are provided for each station and quality level separately and named according to the station (see ‘Stillberg_meteo_metadata_stations_v1.csv’). From the raw data in their original formats, we generated three data quality levels: raw standardized (folder ‘raw_standardized’), edited (folder ‘raw_edited’) and filtered (folder ‘filtered’). The processing level is indicated in the headers of the data files. The whole processing protocol is described in a set of human-readable configuration files that are used by MeteoIO to generate the required data quality levels. This improves long-term reproducibility (Bavay et al., 2022), as the data could be regenerated in the future, even using a completely different software, to account for additional data points or to introduce new data corrections. The first quality level (raw standardized) is generated by parsing the original data files and interpreting them in order to convert all data points to a common format and meteorological parameter naming scheme, while excluding unreadable or duplicated data lines. The generated data files are derivatives of CSV files, with a standardised header that contains the metadata that are necessary to interpret and use the data (use metadata) and to populate a data index (search metadata). The latter is a textual implementation of the Attribute Convention for Data Discovery (ACDD) metadata standard (Attribute Convention for Data Discovery 1-3, 2022). The second quality level (edited) builds on the raw data by performing low-level data editing, such as removing some data periods that are known to be unusable (often based on maintenance records or anecdotal evidence) or applying undocumented calibration factors (for example, when there seems to be an obvious offset on a measured parameter for a period between two documented maintenance operations). The third quality level is generated by applying statistical filters on the data (per station and per meteorological parameter) to exclude presumably wrong values. We did not perform gap filling, as no single strategy could be relied upon that would work best for all possible data usage scenarios.

air-pressure

display_name: air-pressure

dew-point-temperature

display_name: dew-point-temperature

precipitation-sum

display_name: precipitation-sum

relative-humidity

display_name: relative-humidity

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

soil-temperature

display_name: soil-temperature

stillberg

display_name: stillberg

treeline

display_name: treeline

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

en: Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.


maintainer: Esther R. Frei

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

media_type: text/plain

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

format: TXT

mimetype: text/plain

en: GTREE Website

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

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

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

climate-change

display_name: climate-change

forest

display_name: forest

g-tree

display_name: g-tree

larix-decidua

display_name: larix-decidua

picea-abies

display_name: picea-abies

seedling-recruitment

display_name: seedling-recruitment

swiss-alps

display_name: swiss-alps

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


en: defense-chemistry cross-resistance emerald-ash-borer ash-dieback fraxinus intraspecific-variation invasive-species

en: Raw data-Publication cross-resistance in ash-New Phytologist

en:
What are the research data files about: Raw data on perfomance (dry weight, development and mortality) of emerald ash borer larvae used in published bioassays. Raw data on ash dieback leasion lenghts. Raw data on untargeted and targeted specialized ash metabolites.
Which methods were used: Bioassays in greenhouses and climate chambers to collect data on emerald ash borer and ash dieback perfomance. Phytochemical analyses on ash phloem for quantifiying specialized metabolites
When and where was the data created / extracted: Summer 2020-2021

format: XLS

en:
 What are the research data files about: Raw data on perfomance (dry weight, development and mortality) of emerald ash borer larvae used in published bioassays. Raw data on ash dieback leasion lenghts. Raw data on untargeted and targeted specialized ash metabolites.

Which methods were used: Bioassays in greenhouses and climate chambers to collect data on emerald ash borer and ash dieback perfomance. Phytochemical analyses on ash phloem for quantifiying specialized metabolites.

When and where was the data created / extracted: Summer 2020-2021

ash-dieback

display_name: ash-dieback

cross-resistance

display_name: cross-resistance

defense-chemistry

display_name: defense-chemistry

emerald-ash-borer

display_name: emerald-ash-borer

fraxinus

display_name: fraxinus

intraspecific-variation

display_name: intraspecific-variation

invasive-species

display_name: invasive-species

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: Raw data-Publication cross-resistance in ash - New Phytologist


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

en: Seeland subtitles FR

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

en: Seeland subtitles DE

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

en: Entremon subtitles FR

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

en: Entremont subtitles DE

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

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

landscape-change

display_name: landscape-change

policy-action

display_name: policy-action

scenario

display_name: scenario

visualization

display_name: visualization

en: Teaser videos Swiss landscapes under climate change


maintainer: Dirk Nikolaus Karger

en: chelsa amphibians mammals range-maps interannual-climate-variability species-distribution-models random-forests generalized-additive-models reptiles climate-variablity generalized-linear-models iucn niche-models sdm

en: File containing the climate predictors as SAGA grid files (.sgrd)

en: Readme file containing the file naming convention, file dimensions, and variable names

en: File containing the record dimension, genus, and species of the respective netcdf files

en: Mammals

en: Species distribution models based on different climate predictor groups

en: Reptiles

en: Amphibians

en: We modelled the global distribution of 730 amphibian, 1276 reptile, and 1961 mammal species globally as a function of current climate at a 0.5° spatial resolution using four different predictor groups composed of different combinations of input variables: mean climatic conditions, spatial climatic variability, and temporal (interannual) climatic variability.

amphibians

display_name: amphibians

chelsa

display_name: chelsa

climate-variablity

display_name: climate-variablity

generalized-additive-models

display_name: generalized-additive-models

generalized-linear-models

display_name: generalized-linear-models

interannual-climate-variability

display_name: interannual-climate-variability

iucn

display_name: iucn

mammals

display_name: mammals

niche-models

display_name: niche-models

random-forests

display_name: random-forests

range-maps

display_name: range-maps

reptiles

display_name: reptiles

sdm

display_name: sdm

species-distribution-models

display_name: species-distribution-models

en: Global species distributions for mammals, reptiles, and amphibians


en: ice atmosphere laboratory-study snow-atmosphere-interactions chemistry

en: Processed data as shown in Figure 4 of publication. X-ray energy in eV in column one, electron count intensity in arb. units in column 2.

en:
The table lists all NEXAFS raw data with the following columns:

* name:						raw file name
* xRayEkinRange_eV: 		Smallest and largest X-ray photon energy in eV
* scientaPassEnergy_eV: 	Pass energy of electron analyzer in eV
* scientaDwellTime_ms: 		Dwell time of electron analyzer in ms
* regionName:				O K-edge for all data
* scientaAcquisitionMode: 	Fixed energy (snapshot) mode
* scientaEkinRange_eV: 		Smallest and largest energy of the kinetic energy window used for detection in this work
* scientaEkinStep_eV		Not defined in fixed energy mode
* scientaLensMode:			Tune table
* scientaRegionIterations: 	Iterations (sweeps)
* scientaSequenceIterations: Iterations (sweeps)
* sampleTemp_dC: 			Temperature as monitored at the sample holder in °C
* cellPressure_mbar: 		Total pressure in measurement cell in mbar
* iceTemp_dC: 				Temperature of ice sample calculated based on vapour pressure (assuming total pressure equals partial pressure of water) in °C
* smplX_mm: 				positon of measurement spot, x dimension in mm
* smplY_mm: 				positon of measurement spot, y dimension in mm
* smplZ_mm: 				positon of measurement spot, z dimension in mm
* sealingTemp:				temperature at base of sample manipulator
* lastModifiedDatestr: 		time and date when data aquisition was finished
* sample: 					sample description

en: Near Ambient Pressure partial Auger-Meitner electron yield X-ray absorption spectra and photon flux data. X-ray energy in eV in column 1, analyzer electron count intensity in arb. units in column 2.

en: Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice.

atmosphere

display_name: atmosphere

chemistry

display_name: chemistry

ice

display_name: ice

laboratory-study

display_name: laboratory-study

snow-atmosphere-interactions

display_name: snow-atmosphere-interactions

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: An ice oxygen K-edge NEXAFS spectroscopy data set on gas-phase processing


weissfluhjoch-dataset-for-esm-snowmip

maintainer: Nander Wever

title_for_slug: weissfluhjoch-dataset-for-esm-snowmip

en: esm-snowmip snow-depth weissfluhjoch snow-models snow

en: ini file

en: This ini file is required by data-converter from MeteoIO version 1887 (http://models.slf.ch/p/meteoio/)

en: Dataset Weissfluhjoch for ESM-SnowMIP

mimetype: application/zip

en: Conversion script

en:
Use this script to convert WFJ_optimaldataset_v6.smet to wfj.smet, the file that is used for the ESM-SnowMIP project. It also requires the file: WFJ_AirPressure_Data.zip

Use: bash create_wfj_dataset.sh > input/wfj.smet

mimetype: text/x-sh

en: This Weissfluhjoch dataset is a processed version of the Weissfluhjoch dataset version 6 from https://doi.org/10.16904/6. This dataset was specially created for the ESM-SnowMIP project. Here it is documented how this dataset has been created.

esm-snowmip

display_name: esm-snowmip

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-models

display_name: snow-models

weissfluhjoch

display_name: weissfluhjoch

en: Weissfluhjoch dataset for ESM-SnowMIP


maintainer: Christoph Marty

en: CH2014 REPORT

en:
CH2014-Impacts: Toward Quantitative Scenarios of Climate Change Impacts in Switzerland

The CH2014-Impacts report presents the results of a concerted effort of 15 Swiss research institutions to describe impacts of climate change in Switzerland quantitatively. This dataset contributes to the Crosphere/Snowcover section.

en: Dataset

en: Datasets are grouped under experiment "Aare" and "Graubunden".Data is also made available in netCDF format. A range of tools are available to [read netCDF](http://www.unidata.ucar.edu/software/netcdf/software.html).

en: Aare input

en: Grid input data for the 'Aare' CH2014 simulation.

mimetype: application/x-tar

en: Graubunden input

en: Grid input data for the 'Graubunden' CH2014 simulation.

en:
 Overview

The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change.

 Experimental Setup

Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graubünden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m × 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999–2012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085).

 Data

Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5–9 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400–800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average.

climate-change

display_name: climate-change

snow-water-equivalent

display_name: snow-water-equivalent

en: Alpine3D simulations of future climate scenarios CH2014


maintainer: Felix Neff

en: functional-diversity restoration long-term-experiment insects functional-traits topsoil-removal grassland diversity

en: Insect abundance

media_type: text/csv

en:
Abundance of adult insects recorded in the samplings on all experimental sites

- **Plot**: ID of Plot
- **Treatment**: Experimental treatment
- **Species**: Insect species name (following [Fauna Europaea](https://fauna-eu.org))
- **Author**: Author and year of species' description (following [Fauna Europaea](https://fauna-eu.org))
- **Group**: Insect group as used in the analyses
- **Number_Adults**: Number of adult individuals recorded
- **Herbivore**: Logical indicating whether the species is feeding as a herbivore (*TRUE*) or not (*FALSE*)

format: CSV

mimetype: text/csv

en: Plot information

en:
Information on the experimental plots.

- **Plot**: ID of Plot
- **Treatment**: Experimental treatment
- **Landuse_history**: recent agricultural use for Initial; agricultural use before restoration for all restored sites
- **Last_tilled**: Last time the site was tilled
- **Intensity**: Frequency and intensity of mowing and manuring (*intensity1*: mowing once and no manure, i*ntensity2*: 2-3 times mowing and no manure, *intensity3*: 2-3 times mowing and manuring, *intensity4*: 4-5 times mowing and manuring)
- **Slope**: slope gradient according to classes defined by FAO (1990)
- **X**: X coordinate of the plot (CH1903)
- **Y**: Y coordinate of the plot (CH1903)
- **Cluster**: Spatial cluster of the plot

en: Insect traits

en:
Functional traits of all herbivorous insect species sampled in the study.

- **Species**: Insect species name (following [Fauna Europaea](https://fauna-eu.org))
- **Author**: Author and year of species' description (following [Fauna Europaea](https://fauna-eu.org))
- **Group**: Insect group as used in the analyses
- **Body volume**: Body volume determined by body length, body width and body height (in mm$^3$)
- **Body_shape**: Ratio of body length to body width
- **Hind_femur_shape**: Ratio of hind-femur length to hind-femur width
- **Hind_front_leg_ratio**: Ratio of front-leg length to hind-leg length (leg length = femur length + tibia length)
- **Wing_length_rel**: Ratio of wing length to body length
- **Leg_length_rel**: Ratio of front-leg length to body length (leg length = femur length + tibia length)
- **Antenna_length_rel**: Ratio antenna length to body length
- **Eye_width_rel**: Ratio of eye width to head width (Auchenorrhyncha, Heteroptera) / to head height (Orthoptera)
- **Feeding_specialization**: Insect specialization on food source (*m*: monophagous, *o*: oligophagous, *p*: polyphagous)
- **Feeding_tissue**: Feeding tissue used for feeding (*c*: chewers, *x*: xylem, *p*: phloem, *m*: mesophyll, *m-p/r*: mesophyll as well as phloem and/or reproductive organs, *r*: reproductive organs, *se*: ripe seeds)
- **Hibernation**: Hibernation stage (*e*: egg, *e/n*: egg and nymph, *n*: nymph, *a*: adult)
- **Generations**: Number of generations per year (*1.5* stands for *1-2* generations per year)
- **Moisture_pref_mean**: mean of preferred habitat moistures (*1*: xerophilous, *2*: moderately xerophilous, *3*: moderately mesohygrophilous, *4*: mesohygrophilous, *5*: moderately hygrophilous, *6*: hygrophilous, *7*: moderately hydrophilous, *8*: hydrophilous)
- **Moisture_pref_range**: range of preferred habitat moistures (indicating degree of specialization)

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

**Paper Citation**:

Neff, F., Resch, M. C., Marty, A., Rolley, J. D., Schütz, M., Risch, A. C, Gossner, M. M. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands: a functional approach. Ecological Applications, 30, e02133. [10.1002/eap.2133](https://doi.org/10.1002/eap.2133)

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

 Methods

 Study site
The study area is situated within and nearby to Eigental nature reserve (47°27’36” to 47°29’06” N, 8°37’12” to 8°37’44” E, 461 to 507 m a.s.l.) in the vicinity of Zurich airport (Canton Zurich, Switzerland). Mean annual precipitation and temperature is 903 ± 136 mm and 9.14°C ± 0.50°C (mean ± SD for 2007-2017 (*MeteoSchweiz 2018*)). In 1967, the Eigental nature reserve was established to protect small and isolated remnants of species-rich, semi-natural grasslands (roughly 12 ha), which were embedded in an otherwise intensively managed landscape. It is characterized by oligo- to mesotrophic Molinion (semi-wet, matrix species *Molinia caerulea*) and Mesobromion (semi-dry, matrix species *Bromus erectus*) meadows (*Delarze et al. 2015*), reflecting small-scale habitat heterogeneity, mainly due to site-specific groundwater levels and slope inclination. As in most Central European grasslands, management is necessary to prevent shrub and tree invasions as well as to secure low levels of available soil nutrients and thus to maintain these species-rich habitats ([*Poschlod and WallisDeVries 2002*](https://doi.org/10.1016/S0006-3207(01)00201-4)).
In 1990, the government of the Canton Zurich decided to enlarge the Eigental nature reserve as a counter measure against degradation and biodiversity loss in semi-natural grasslands due to overutilization and the excessive input of nutrients (mostly nitrogen). Eleven patches of adjacent intensively managed grassland (in total roughly 20 ha) were targeted to be transformed into semi-natural grasslands. As a first restoration measure, fertilization was ceased, and biomass harvested three times to remove excessive soil nutrients from the original system and thus benefit plant species with low competitive ability on the long run. In 1995, the restoration efforts were increased and a large-scale experiment comprising three restoration measures with increasing intervention intensities was implemented:

- **Harvest only**: Initial restoration measures were continued with mowing and removing of the aboveground biomass two times a year (early summer and autumn).
- **Topsoil**: Removal of topsoil, depending on the thickness of the A horizon the upper 10 to 20 cm, in four randomly selected areas within the eleven patches in late autumn 1995. The size of the restoration area depended on individual patch size (2700 to 7000 m2).
- **Topsoil + Propagules**: Plant propagules were added on half of the area where topsoil was removed via application of fresh, seed-containing hay and hand-collected propagules of target species originating from semi-dry and semi-wet species-rich grasslands with local and regional provenance (within radius of 7 to 30 km) (1995, 1996, 1997).

Management of *Topsoil* and *Topsoil + Propagules* started five years after treatment implementation and included yearly mowing and removing of aboveground biomass (late summer or early autumn). The experiment was complemented with intensively managed grassland sites that share the same agricultural history as the restored sites (**Initial**; swards dominated by *Lolium perenne*, *L. multiflorum* and *Trifolium repens*): mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes. Finally, sites were selected in target semi-dry and semi-wet grasslands (**Target**) located within the Eigental nature reserve and another nature reserve nearby (Altläufe der Glatt; 47°28’29” to 47°27’41” N, 8°31’56” to 8°32’26” E, 418 to 420 m a.s.l.). The selected target sites are mown and aboveground biomass removed once a year in late summer or early autumn. For each of the five treatments, we selected eleven plots (5 m × 5 m) spread across the sites. Altogether, the experiment included 55 plots.

 Arthropod sampling
Aboveground arthropods were sampled using suction sampling on four consecutive days in early July 2017 before the grasslands were mown. Arthropods were sampled in two locations on each 5 m × 5 m plot, once in the south-western and once in the north-eastern corner to account for possible spatial heterogeneity within the plots. Arthropods were sorted to order or lower taxonomic levels and individuals were identified to species level. We focused on three groups (Hemiptera: Auchenorrhyncha, Hemiptera: Heteroptera, Orthoptera),

 Functional traits
We used two sets of functional traits in this study.

**Morphometric traits**:
Body volume, body shape, hind femur shape, hind/front leg ratio, wing length, leg length, antenna length and eye width. We used trait measurements from [*Simons et al. (2016)*](http://dx.doi.org/10.1890/15-0616.1) and [*Neff et al. (2019)*](https://doi.org/10.1007/s10980-019-00872-1) and complemented them with measurements on study specimens. These measurements were conducted using a high-resolution measuring stereo microscope (Leica DVM6, Leica Microsystems) including automated high-resolution photo stacking with the software Leica Application Suite X (LAS X, © 2018 Leica Microsystems CMS GmbH) and Leica Map Premium (Leica Microsystems, © 1996-2017 Digital Surf) at WSL Birmensdorf. The eight morphometric traits were calculated from direct measurements of body parts on specimens of all sampled species. From each species, we measured at least one female and one male specimen. Additionally, for species that show wing dimorphism, we included the different wing morphs and weighted them by their prevalence reported in literature. For few species, of which not all wing morphs were available for measurements (10 cases), we estimated relative wing length from congeneric species or from the literature.

**Life-history traits**
Based on an existing data set collected by [*Gossner et al. (2015)*](http://dx.doi.org/10.1890/14-2159.1). We included traits describing different life-history characteristics of herbivore insect species, namely: feeding specialization, feeding tissue, hibernation stage and number of generations per year, which are related to insect species’ vulnerability to changes in plant community composition, microhabitat use and disturbance tolerance. To represent potential changes in habitat moisture with abandonment of intensive land use (e.g., change in ground-water level), we also included two traits related to preferred habitat moisture of the study species: moisture preference, describing species’ optimum habitat moisture, and moisture range, which describes the species’ range of preferable moisture conditions.

 References

Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten. 3rd ed. Ott, Bern.

Gossner, M. M., N. K. Simons, R. Achtziger, T. Blick, W. H. O. Dorow, F. Dziock, F. Köhler, W. Rabitsch, and W. W. Weisser. 2015. A summary of eight traits of Coleoptera, Hemiptera, Orthoptera and Araneae, occurring in grasslands in Germany. Scientific Data 2:150013.

MeteoSchweiz. 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Zürich.

Neff, F., N. Blüthgen, M. N. Chisté, N. K. Simons, J. Steckel, W. W. Weisser, C. Westphal, L. Pellissier, and M. M. Gossner. 2019. Cross-scale effects of land use on the functional composition of herbivorous insect communities. Landscape Ecology 34:2001–2015.

Poschlod, P., and M. F. WallisDeVries. 2002. The historical and socioeconomic perspective of calcareous grasslands—lessons from the distant and recent past. Biological Conservation 104:361–376.

Simons, N. K., W. W. Weisser, and M. M. Gossner. 2016. Multi-taxa approach shows consistent shifts in arthropod functional traits along grassland land-use intensity gradient. Ecology 97:754–764.

diversity

display_name: diversity

functional-diversity

display_name: functional-diversity

functional-traits

display_name: functional-traits

grassland

display_name: grassland

insects

display_name: insects

long-term-experiment

display_name: long-term-experiment

restoration

display_name: restoration

topsoil-removal

display_name: topsoil-removal

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: Grassland restoration: insects and insect traits


intratrait

maintainer: Christian Rixen

title_for_slug: intratrait

en: elevation-range cold-adapted-plants elevation-gradient plant-traits climate-change neighbour-interactions

en: Plant trait data along elevation gradients on mountains

en:
This data set was used to test whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries (Switzerland, Australia, New Zealand, China) in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species’ height ratio, its height relative to its nearest neighbour.
The following traits and parameters were measured and are available in this data set:
As an indication of plant stature, we measured vegetative and generative height, where vegetative height was distance from soil to highest vegetative leaf and generative height was distance to the highest point on the reproductive shoot. As a measure of reproductive investment, we noted the presence of flowers on the randomly chosen individuals (see below). As a measure of individual and genet basal area, we measured individual plant and patch diameters, in two dimensions (along the largest diameter and perpendicular to it). In clonal plant species, plant diameter was equivalent to an individual rosette, whereas patch diameter referred to the whole genet and could represent the size of a tuft, tussock or cushion. For genera with more singular growth forms (e.g., some Gentiana species) plant and patch diameter were the same. The two diameter measurements were made at right angles, allowing estimates of patch and plant areas to be calculated as an ellipse (i.e., area = 0.5 a 0.5 b Π). All traits were measured on ten randomly selected individuals per site. Flower count data was considered in a binary fashion on a per individual basis (because for some species individuals only produce one flower when flowering) so that the presence or absence of flower(s) was a nominal value between 0 and 10 for each species at each site. We then collected at least three leaves (up to 30 for small and light leaves) from each of the first three individuals selected from each species for determination of leaf dry matter content (LDMC) and specific leaf area (SLA). For calculations of LDMC and SLA, fresh leaves were scanned on a flatbed scanner to determine leaf area. Leaves were then weighed on a balance to a precision of +/- 0.001g, prior to being air dried and reweighed with a balance to a precision of +/- 0.0001g. LDMC was calculated by dividing dry leaf mass by fresh leaf mass. SLA was calculated by dividing leaf area by dry leaf mass. Additionally, within an area of 10 cm diameter around the target individual, we determined the tallest neighbouring species and measured its vegetative and generative height, and estimated the percent cover of the target species, other vegetation, rock, and bare soil.
For more details see Rixen et al. 2022, Journal of Ecology.

cold-adapted-plants

display_name: cold-adapted-plants

elevation-gradient

display_name: elevation-gradient

elevation-range

display_name: elevation-range

neighbour-interactions

display_name: neighbour-interactions

plant-traits

display_name: plant-traits

en: intratrait


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

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

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

en: A condensed instruction for running the presented simulations.

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


maintainer: Ionuț Iosifescu Enescu

en: map 2GB

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: map preview 0.5MB

mimetype: image/jpeg

en: map 1GB

en: Bulk Data Access

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

en: large map 10GB

en: map 0.2GB

en: map 0.5GB

en: huge map 100GB

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

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

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis

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


en: amphibians conservation landscape occupancy-models wetlands connectivity

en: Model code 2

en: This R markdown file contains all code and descriptions necessary to load and format the provided data and to fit the dynamic occupancy models described in the second associated publication, including JAGS code for the fitted models.

format: OCTET

en: Readme

en: Description of methods and datasets. Version 1.

en: Observation data

en: This file contains detection/non-detection data for the 12 amphibian species between 1999 and 2019 for 3 annual visits. Detectable denotes whether or not a visit j was made to pond i in year t. ObservedID gives a unique ID for the observer collecting the data during visit j to pond i in year t.

en: Structural connectivity data

en: This file contains structural connectivity data (three different metrics), calculated based on all existing ponds in each year, in long format. See Readme v2.0 for details. Required to run Model code 2.

en: Model code

en: This R markdown file contains all code and descriptions necessary to load and format the provided data and to fit the dynamic occupancy models described in the associated publication, including JAGS code for the fitted models.

en: Potential population connectivity data

en: This file contains potential population connectivity data (three different metrics), calculated based on all existing and occupied ponds in each year, for each species, in long format. See Readme v2.0 for details. Required to run Model code 2.

en: Model code, description

media_type: text/html

en: This is the output of the R markdown file that provides JAGS model code and describes how to format the provided data and fit the dynamic occupancy models described in the associated publication, for a quick overview.

format: HTML

en: Readme, v 2.0

en: Description of methods and datasets. Version 2.0, including new datasets fro structural and potential population connectivity.

en: Covariate data 1

en: This file contains covariate data that does not change over time for 856 ponds: the region the pond is in, whether the pond is old or new (constructed since 1991), elevation, water surface area, fluctuations of the water table (yes, no), the percent forest cover in 100m radius around the pond, the area or large (>6m wide) roads in 1km radius around the pond.

en: Model code 2, description

en: This is the output of the R markdown file Model code 2 that provides JAGS model code and describes how to format the provided data and fit the dynamic occupancy models described in the second associated publication, for a quick overview.

en: Original raw data

en: This file contains the original raw monitoring data as provided by the canton of Aargau. Access is restricted and only considered upon request and in consultation with the canton of Aargau.

en: Covariate data 2

en: This file contains covariate data that changes over time for 856 ponds in 21 years (long format): whether or not a pond exists in year _t_, the age of constructed ponds in year _t_, structural connectivity of the pond in year _t_.

en:
In the canton of Aargau, hundreds of new ponds have been constructed since the 1990s to benefit declining amphibian populations. This dataset consists of monitoring data for all 12 pond-breeding amphibian species in the canton of Aargau from 1999 to 2019 in 856 ponds, and environmental variables that describe the ponds and the landscape surrounding the ponds. Species observation data is detection/non-detection data from repeat visits during survey years, during which all potentially suitable ponds in an area were surveyed. Environmental variables describing the ponds are whether the pond has been newly constructed since 1991 or not, pond age (if constructed), elevation a.s.l., the water surface area, and whether the water table fluctuates or not. Environmental variables describing the surroundings of the ponds are the percent area of forest within a circular buffer of radius 100m around the pond, the area of large (width ≥6m) roads within a circular buffer of radius 1km around the pond, as well as structural and potential population connectivity, quantified by three different metrics each.
The canton of Aargau is the owner of the monitoring data; the original datafile is only disclosed upon request and in consultation with the canton of Aargau.
The edited dataset contains cleaned observation data for the 12 amphibian species, as well as compiled and edited covariate data and code to fit dynamic occupancy models.

amphibians

display_name: amphibians

connectivity

display_name: connectivity

conservation

display_name: conservation

landscape

display_name: landscape

occupancy-models

display_name: occupancy-models

wetlands

display_name: wetlands

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: Amphibian observation and pond data (Aargau, Switzerland)


soil-property-maps-for-the-swiss-forest

title_for_slug: soil-property-maps-for-the-swiss-forest

en: soil-depth soil-properties switzerland dsm soc soil soil-ph soil-texture forest

en:
We used 2071 forest soil profiles to model a wide range of soil properties for the forested area of Switzerland. The spatial prediction is based on the principle of «digital soil mapping». This involves linking soil profiles with soil forming factors using statistical or machine learning methods.

A quantile regression forest (QRF) approach was applied to predict the following soil properties at six depth ranges: clay, gravel, sand, fine earth density, SOC. The depth ranges correspond to the standard depths of the [GlobalSoilMap.Net](https://www.isric.org/) specification: 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm. In addition, the total soil depth down to a non-root-permeable layer or solid rock soil thick was predicted. To quantify the uncertainty for each predicted pixel, the upper and lower limit of the 90% prediction interval derived from QRF was calculated. More details on the methods and results are described in Baltensweiler et al. 2021 and Baltensweiler et al 2022.

The soil property maps, and the uncertainty maps are provided as a GeoTIFF files at 25 m resolution. The excel file (xlsx) provides a short description of the raster layers.

**The soil and the uncertainty maps can be viewed in a simple web-GIS application available at:**
[www.wsl.ch/soilmaps](https://www.wsl.ch/soilmaps).

dsm

display_name: dsm

forest

display_name: forest

soc

display_name: soc

soil

display_name: soil

soil-depth

display_name: soil-depth

soil-ph

display_name: soil-ph

soil-properties

display_name: soil-properties

soil-texture

display_name: soil-texture

switzerland

display_name: switzerland

en: Soil property maps for the Swiss forest


en: crystallographic x-ray-tomography fem geometrical elasticity microstructure anisotropy

en:
The study aims to determine the effective elastic properties of snow, firn, and bubbly ice based on microstructural quantities. Anisotropy, one of these quantities (the other being ice volume fraction) in snow and ice, has two types: geometrical and crystallographic, resulting in snow's macroscopic anisotropic elastic behavior. The research focuses on the impact of geometrical anisotropy on potential ice volume fractions in snow and ice. 391 micro-CT images from various locations, including laboratories, the Alps, the Arctic, and Antarctica, were analyzed to achieve this.
 The analysis involved microstructure-based finite element simulations, which inherently consider microstructure and calculate the elasticity tensor. Hashin-Shtrikman bounds were utilized to predict the elastic properties of the microstructure samples. These bounds effectively captured the nonlinear interplay between geometrical anisotropy, captured by the Eshelby tensor and density. HS bounds have the advantage of the correct limiting behavior for low to high-ice volume fractions.
 We derived parameterization for five transversely isotropic elasticity tensor components, requiring only two free parameters. This parameterization was valid for ice volume fractions ranging from 0.06 to 0.93. The analysis employing the Thomsen parameter highlighted the dominance of geometrical anisotropy until an ice volume fraction of 0.7. However, to fully comprehend the elasticity of bubbly ice, a comprehensive approach is necessary to integrate coupled elastic theories that account for both geometrical and crystallographic anisotropy.
This dataset includes a Jupyter notebook with all the necessary functions required to predict the elasticity tensor of snow for the given ice volume fraction and anisotropy. Also, the code contains the least squares optimization function to compute the elasticity tensor for the six components of stress and strain. For example, we consider our dataset to calculate the samples' elasticity tensor and reproduce Fig. 7 from the paper. We take the stress and strain values obtained from load states as input for this example.
Also, a .csv file contains all the microstructural information: ice volume fraction, anisotropy, correlation functions, voxels size, and no. of voxels of the samples and the elasticity tensor obtained from finite element simulations and from present work parameterization.

anisotropy

display_name: anisotropy

crystallographic

display_name: crystallographic

elasticity

display_name: elasticity

fem

display_name: fem

geometrical

display_name: geometrical

microstructure

display_name: microstructure

x-ray-tomography

display_name: x-ray-tomography

en: Effective, anisotropic elasticity tensor of snow, firn, and bubbly ice


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

en: Readme

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

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

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

atmospheric-conditions

display_name: atmospheric-conditions

automated-monitoring

display_name: automated-monitoring

biomass

display_name: biomass

carbon-cycle

display_name: carbon-cycle

carbon-dioxide

display_name: carbon-dioxide

carbon-sequestration

display_name: carbon-sequestration

carbon-storage

display_name: carbon-storage

climate-change

display_name: climate-change

climate-growth

display_name: climate-growth

davos

display_name: davos

dbh

display_name: dbh

dendrometer

display_name: dendrometer

ecophysiology

display_name: ecophysiology

ecosystem-functioning

display_name: ecosystem-functioning

eddy-covariance

display_name: eddy-covariance

eddy-covariance-measurements

display_name: eddy-covariance-measurements

flux

display_name: flux

forest-area

display_name: forest-area

forest-biomass

display_name: forest-biomass

forest-canopy

display_name: forest-canopy

forest-characteristics

display_name: forest-characteristics

forest-dynamics

display_name: forest-dynamics

forest-ecosystems

display_name: forest-ecosystems

forest-function

display_name: forest-function

forest-monitoring

display_name: forest-monitoring

icos

display_name: icos

icp-forests

display_name: icp-forests

ilter

display_name: ilter

lwf

display_name: lwf

metadata

display_name: metadata

open-data

display_name: open-data

picea-abies

display_name: picea-abies

sap-flow

display_name: sap-flow

seehornwald

display_name: seehornwald

stem-growth

display_name: stem-growth

subalpine

display_name: subalpine

tree-stem

display_name: tree-stem

treenet

display_name: treenet

twd

display_name: twd

water-relations

display_name: water-relations

en: Site description Davos Seehornwald CH-DAV


maintainer: Océane Hames

en: mosaic openfoam snow-transport antarctica snow-deposition arctic-sea-ice

en: Neumayer III simulations

en:
Snow distribution results around Neumayer Station III obtained for the sensitivity analysis and comparison to measurements. The file contains several sub-folders, namely:
-COMP: simulation output for the comparison to measurements;
-FLOW: simulation output for the study of wind forcing impact;
-simulation_exampleCase: example case of a snowBedFoam simulation with the reference Neumayer Station mesh;
-SNOW: simulation output for the study of snow properties impact;
-STRUCT: simulation output for the study of structure properties impact.

en: MOSAiC Met City preliminary (Level 2) wind data

en:
Extract from the preliminary MOSAiC atmospheric data (Level 2) for the time period 6th to 13th of November, 2019.
This data was used to set up the fluid forcing parameters in the snowBedFoam simulations on MOSAiC sea ice. It is being published in this repository with the kind permission from Matthew Shupe and his team.
The final processed and quality controlled version of this dataset (Level 3) has not been published yet. A complete reference will be added in this description once it is released.

en: Paper Draft

en: This is the initial draft of the paper where the snowBedFoam 1.0. results are tested against Terrestrial Laser Scan (TLS) measurements of snow dynamics on Arctic sea ice (MOSAiC expedition). It highlights the numerical limitations of our model as well as the measurement uncertainties, which may occur in such extreme environments.

en: Snowdrift measurements around Neumayer Station III

en: Files containing the topographical data barometrically measured in June 2009 around the German Antarctic Research station Neumayer III. Those measurements are compared to the snowBedFoam simulations to assess the ability of the model to reproduce real snow distribution observations.

en:
OpenFOAM-5.0 scripts containing the implemented Eulerian-Lagrangian snow transport model.
The code file provided here is not updated. The most recent version of the script can be found on the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/atmospheric-models/snowbedfoam-v1-0.git

en:
OpenFOAM-2.3.0. scripts containing the implemented Eulerian-Lagrangian snow transport model.

The code file provided here is not updated. The most recent version of the script can be found on the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/atmospheric-models/snowbedfoam-v1-0.git

en:
snowBedFoam 1.0. is a snow transport solver implemented in the computational fluid dynamics software OpenFOAM. It is adapted from the standard multi-phase flow solver DPMFoam for application in snow-influenced environments.
To simulate aeolian snow transport, snowBedFoam 1.0. handles coupled Eulerian–Lagrangian phases, which involve a finite number of particles (snow) spread in a continuous phase (air). The snow erosion and deposition are modelled through physics-based equations similar to the ones employed in the well-established LES-Lagrangian Stochastic Model (Comola and Lehning, 2017 ; Sharma et al., 2018 ; Melo et al., 2022). This modelling approach is computationally intensive and thus adapted to simulate snow movement and distribution on small scale terrain.
First, snowBedFoam 1.0. was applied to topographical data collected on Arctic sea ice during the MOSAiC expedition (Clemens-Sewall, 2021). Together with atmospheric data from the MOSAiC Met City (Shupe et al., 2021) used for the fluid forcing, the model was able to accurately simulate the zones of erosion and deposition of snow along a complex ice ridge structure (Hames et al., 2022).
Second, snowBedFoam 1.0. was used to simulate the snow distribution around the German Antarctic research station Neumayer Station III. The effect of snow properties, fluid forcing and aerodynamic structures on the snow accumulation were assessed.
snowBedFoam 1.0 was implemented in 2 different OpenFOAM versions, namely OpenFOAM-2.3.0 and OpenFOAM-5.0. The latter offers more options for turbulence models and boundary conditions. The fundamental model equations were not changed from one implementation to the other, thus both still correspond to snowBedFoam 1.0. The two branches are called snowBedFoam-v1-2.3.0 (OpenFOAM-2.3.0) and snowBedFoam-v1-5.0 (OpenFOAM-5.0). The core codes of snowBedFoam 1.0. are directly accessible on the WSL/SLF GitLab repository (more details in the Resources section).

antarctica

display_name: antarctica

arctic-sea-ice

display_name: arctic-sea-ice

mosaic

display_name: mosaic

openfoam

display_name: openfoam

snow-deposition

display_name: snow-deposition

snow-transport

display_name: snow-transport

en: snowBedFoam: an OpenFOAM Eulerian-Lagrangian solver for modelling snow transport


en: urban dietary-patterns nutrient-intake bombus-pasquorum rural zurich basel foraging-behaviour bombus-lapidarius bern pollen-metabarcoding macronutrients bumblebees lad-use-chages bees

en:
The dataset contains the plant trait values obtined from published sources. It contains:
- Kingddom: taxonomic
- Phylum: taxonomic
- Order: taxonomic
- Family: taxonomic
- Genus: taxonomic
- Species: species name
- plant.species: full scientific name
- native_exotic: origin status. As presented in https://www.doi.org/10.16904/envidat.210
- pollination mode: vector of pollination. As presented in https://www.doi.org/10.16904/envidat.210
- Flowering_months_duration: Number of flowering months. As presented in https://www.doi.org/10.16904/envidat.210
- start_flowering: month of the starting of the flowering. As presented in https://www.doi.org/10.16904/envidat.210
- growth_form_category: plant growth form. As presented in https://www.doi.org/10.16904/envidat.210
- plant-height: plant heght in meters. As presented in https://www.doi.org/10.16904/envidat.210
- inflorescence: Logic, plant with single flowers (0) or inflorescences (1). As presented in https://www.doi.org/10.16904/envidat.210
- structural_blossom_class: depicting the accessibility of the flowers. As presented in https://www.doi.org/10.16904/envidat.210
- symmetry: floral symmetry. As presented in https://www.doi.org/10.16904/envidat.210
- nectar: logic, nectar present. As presented in https://www.doi.org/10.16904/envidat.210
- pollen: logic, pollen rewards present. As presented in https://www.doi.org/10.16904/envidat.210
- oil: logic. oil present. As presented in https://www.doi.org/10.16904/envidat.210
- sugar.concentration: the amount of sugar present in the nectar, in mg.
Additional information can be found in https://doi.org/10.1016/j.dib.2021.107243

en:
The dataset contains the amino acid content and ratios for the two bumblebees. Measures are taken only from pollen from the leg-baskets. SampleID is not the same as in the metabarcoding, several samples from different bumblebees were merged within a Location ,Landscape and Replica. Not for all the samples there is information on both amino acids and fatty acids. The columns are:
- SampleID_chemical: The ID for chemical analyses
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- LocationLandscape: Combining location and landscape
- Asp, Thr, ..., Arg: Content of individual amino acids. Amino acids are written as abbreviated names. FUll names be checked here: https://en.wikipedia.org/wiki/Amino_acid
- sum: total sum of amino acid content (mg)
- sumessential: total sum essential amino acids (mg)
-sumnon-essential: total sum non-essential amino acids (mg)
- proportionnonessentialessential: proportion between essential and non-essential amino acids.

en:
The dataset contains the information on the 16 study sites:
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Site_ID: combination of location, landscape and replicate
- X: X coordinate, WGS84
- Y: Y coordinate WGS84
- Plant S in landscape: Plant species richness within the 800 m calculated using both information from the Global Biodiversity Information. Facility (GBIF) and InfoFlora (www.infoflora.ch).
- % Buildings: the proportion of buildings and other impervious surfaces within 800 m radius.
- % Agricultural: the proportion of agricultural land and other impervious surfaces within 800 m radius.
- % Water: the proportion of water and other impervious surfaces within 800 m radius.
- % Meadows & grasslands: the proportion of meadows and grasslands and other impervious surfaces within 800 m radius.
The raw metabarcoding data is archived in NCBI project PRJNA1137320.

en:
Processed data. The dataset contains the plant species richness in the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Plant species richness: the number of plant species found in the pollen of the leg-baskets.

en:
Processed data. The dataset contains the functional diversity metrics based on the plant composition from the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Fric.w: Weighted functional richness
-Fric: functional richness
-FEve.w: Weighted functional evenness
- FEve: functional evenness
- FDiv.w: weighted functional divergence
- FDiv: functional divergence

en:
The dataset contains the filtered plant composition1 (removing species with too low relative abundances) and the relative abundance obtained from DNA metabarcoding on pollen samples for both the leg-baskets and the body. The dataset contains the following columns:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Abundance: numeric, relative abundance
- BumblebeeID: numerical ID for each bumblebee individual within a location, landscape and replicate sampling place.
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape:
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Transportation_structure: bumblebee pollen transportation structure, levels: B = Body; L = leg-baskets (i.e. corbicula)
- Kingddom: taxonomic
- Phylum: taxonomic
- Order: taxonomic
- Family: taxonomic
- Genus: taxonomic
- Species: plant sicentific name
-Binomial_abundance: Logic, indicating if the plant species is included (1).
The raw metabarcoding data is archived in NCBI project PRJNA1137320

en:
The dataset contains the fatty acid content and ratios for the two bumblebees. Measures are taken only from pollen from the leg-baskets. SampleID is not the same as in the metabarcoding, several samples from different bumblebees were merged within a Location ,Landscape and Replica. Not for all the samples there is information on both amino acids and fatty acids. The columns are:
- SampleID_chemical: The ID for chemical analyses
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- LocationLandscape: combination between location and landscape
- Caprylic, Pelargonic, ..., Lacceroic: Content of individual fatty acids acids.
- sum: total sum of fatty acid content (mg)
- unsaturatedsum: total sum unsaturated fatty acids (mg)
- saturatedsum: total sum saturated fatty acids (mg)
- omega3sum: total sum Omega 3 fatty acids (mg)
- omega6sum: total sum Omega 6 fatty acids (mg)
- omega9sum: total sum Omega 9 fatty acids (mg)
- omega6to3ratio: ratio Omega 6 to Omega3 fatty acids
-saturatedtounsaturatedration: ratio between saturated and unsaturated fatty acids

en:
Processed data. The dataset contains the phylogeneitc diversity metrics based on the plant composition from the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- PVar: phylogenetic variance
- PRic: Phylogenetic richness
- PEve: Phylogenetic evenness
- PClu: Phylogenetic clustering

en:
Data on the diet of urban and rural bumblebees individuals, Bombus lapidarius and Bombus pascuorum, collected in Switzerland (cantons Bern, Basel and Zürich) obtained using metabarconding and chemical analyses from two pollen transportation structures, that is, the body and the leg-baskets (i.e., corbicula). The bumblebees were collected by Egenberger et al., 2019 (https://doi.org/10.5061/dryad.qt8mr4v)
The data contains:
- The composition and relartive abundance of plant species in the body and the leg-baskets, separately.
- The content and ratios of two groups of macronutrients, that is, amino acids and fatty acids.
The dataset contains the data used in the paper "Urban bumblebees diversify their foraging strategy to maintain nutrient intake" as well as unpublished data.

basel

display_name: basel

bees

display_name: bees

bern

display_name: bern

bombus-lapidarius

display_name: bombus-lapidarius

bombus-pasquorum

display_name: bombus-pasquorum

bumblebees

display_name: bumblebees

dietary-patterns

display_name: dietary-patterns

foraging-behaviour

display_name: foraging-behaviour

lad-use-chages

display_name: lad-use-chages

macronutrients

display_name: macronutrients

nutrient-intake

display_name: nutrient-intake

pollen-metabarcoding

display_name: pollen-metabarcoding

rural

display_name: rural

urban

display_name: urban

zurich

display_name: zurich

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: habitat-availability habitat-heterogeneity forest-management saproxylic-beetles dead-wood colonisation biodiversity sloss conservation

en: Community matrix (all beetles)

en: Abundances of all beetle species for each branch bundle

en: Forest and environmental variables

en: Data on the branch bundle, forest and environmental variables

en: Community matrix (saproxylic beetles)

en: Abundances of saproxylic beetle species for each branch bundle

en: Variable description

en: Detailed description of the forest and environmental variables

en:
The data was generated during a landscape experiment performed on 69 plots in Sihlwald forest between 2016 and 2019. Branch bundles of different sizes consisting of beech branches were exposed in the forest for one year to be colonized by saproxylic beetles and later reared for one year in emergence traps. All beetles emerging from the branches were collected. Overall 244 branch bundles are included and we found 66 beetles species (23511 individuals) of which 43 were classified as saproxylic (20873 individuals).
 Species data (2 tables)
Two community matrices (including abundance): one for saproxylic beetles and one for all beetles. Beetles were classfied as saproxylic following an extended list of the list reported in Schmidl & Bussler (2004).
 Model data (1 table)
*Response variables*
Species richness and abundance of (saproxylic) beetles.
*Branch bundles*
Branch bundles consisted of 1, 3, 6 or 12 branches and each bundle size was installed at each of the 69 plots. The bundles consisted of standardized freshly cut beech branches (Fagus sylvatica) with a length of 80 cm and a diameter between 3 and 6 cm. Total surface and volume of each bundle is reported.
*Tree on which the bundle was installed*
Information on the tree to which the bundles were attached (tree species, dbh, distance and azimut from the plot center).
*Temperature and light availability*
Temperature (plot level) and light availability (bundle level) were calculated.
*Deadwood availability in the landscape*
Based on a map of lying dead wood, dead wood amount and isolation were calculated for concentric circles with a radius ranging from 20 to 200 m (in 10 m steps) around each branch bundle.
 Variable descriptions (1 table)

biodiversity

display_name: biodiversity

colonisation

display_name: colonisation

conservation

display_name: conservation

dead-wood

display_name: dead-wood

forest-management

display_name: forest-management

habitat-availability

display_name: habitat-availability

habitat-heterogeneity

display_name: habitat-heterogeneity

saproxylic-beetles

display_name: saproxylic-beetles

sloss

display_name: sloss

en: Beetle communities reared from beech branches and associated forest and environmental variables


snow-drift-station-flowcapt

maintainer: Benjamin Walter

title_for_slug: snow-drift-station-flowcapt

en: raclets flowcapt snow-blowing wind-speed acoustic-mass-flux snowdrift

en: maximum mass flux

en: The data file contains the maximum mass flux of 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: average wind speed

en: The data file contains the average wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: avergaged mass flux

en: Average mass flux of 10 min measurement interval. The Cycle duration is 1 min which includes 6 s observation and 54 s stand-by. The time is given in UTC+1.

en: minimum mass flux

en: The data file contains the minimum mass flux of 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: minimum wind spees

en: The data file contains the minimum wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: maximum wind speed

en: The data file contains the maximum wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6 s observations and 54 s stand-by. The time is given in UTC+1.

en: The FlowCapt is an ultra-robust instrument measuring solid particle acoustic mass - flux intensities (g m‾ ² s‾ ¹) and wind speeds (m s‾ ¹). It was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849). The vertical tube with a length of 1 m monitors snowdrift and snow-blowing; and is mounted at a height between 0.1 an 1.1 m above snow surface. The time within each data set is given in UTC+1.

acoustic-mass-flux

display_name: acoustic-mass-flux

flowcapt

display_name: flowcapt

raclets

display_name: raclets

snow-blowing

display_name: snow-blowing

snowdrift

display_name: snowdrift

wind-speed

display_name: wind-speed

en: Snow Drift Station - Flowcapt


maintainer: Jana von Freyberg

en: hydrology stream-water alptal precipitation stable-water-isotope catchment

en: Data Set Documentation: alptal_Isotopes_daily_2015-2019_v2

en: Documentations contains meta data of the data columns and information about the study catchments.

en:
Version 2 of the dataset contains additional information on:
- line-conditioned (lc) excess
- precipitation sample volumes
- isotope data quality

en: Single comma-separated txt-file with daily data of stable water isotopes measured in streamwater and precipitation in the Swiss Alptal catchment and two of its tributaries (Erlenbach and Vogelbach), as well as daily data of key hydrometeorological variables from the Alptal valley

en: This dataset contains four years of daily measurements of the natural isotopic composition (2H, 18O) of precipitation and stream water at the Alp catchment (area 47 km2) in Central Switzerland and two of its tributaries (0.73 km2 and 1.55 km2). In addition, the dataset contains daily measurements of key hydrometeorological variables.

alptal

display_name: alptal

catchment

display_name: catchment

hydrology

display_name: hydrology

precipitation

display_name: precipitation

stable-water-isotope

display_name: stable-water-isotope

stream-water

display_name: stream-water

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Four years of daily stable water isotope data in stream water and precipitation from three Swiss catchments


maintainer: Matthias Haeni

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

en: Site aerial photo (Nationalpark)

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

en: Data plot of the forest station (NAB)

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

en: Field station data (NAF)

en: Data file.

en: Site photo of the field station (NAF)

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

en: Site photo of the forest station (NAB)

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

en: Data plot of the field station (NAF)

en: Forest station data (NAB)

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 Nationalpark in Switzerland where one station is located within a natural coniferous forest stand (NAB) with mountain pine (_Pinus mugo_; 210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Nationalpark 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

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

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


en: tectonic-faults climate-history relief plant-biodiversity endemism

en:
This dataset includes geomorphic predictors and plant biodiversity patterns in the Hengduan Mountains, published in the Geological Society of London special volume: Asian Geodynamics, Climate and Biodiversity (https://doi.org/10.1144/SP549-2024-17).
The geomorphic predictors include catchment-restricted relief and chi maps, while the climate predictors comprise site water balance, growing degree days, and climate change velocity from the Last Glacial Maximum (LGM) to the present.
The plant biodiversity patterns are represented by a plant richness map for 13,321 species in the Hengduan Mountains. Species-level plant range maps are available in the dataset, previously published under DOI: 10.16904/envidat.424.

climate-history

display_name: climate-history

endemism

display_name: endemism

plant-biodiversity

display_name: plant-biodiversity

relief

display_name: relief

tectonic-faults

display_name: tectonic-faults

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: Tectonic and climatic controls on plant biodiversity in the Hengduan Mountains China


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

en: Field station data (BAF)

en: Site aerial photo (Beatenberg)

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

en: Site photo of the field station (BAF)

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

en: Forest station data (BAB)

en: Data plot of the forest station (BAB)

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

en: Site photo of the forest station (BAB)

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

en: Data plot of the field station (BAF)

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 Beatenberg in Switzerland where one station is located within a natural coniferous forest (BAB) with Norway spruce (_Picea abies_; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Beatenberg is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the
Swiss Federal Research Institute WSL.

wind-speed

display_name: wind-speed

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


en: soil-water-content soil-temperature soil-moisture landslide-early-warning soil-water-matrix-potential

en: In-situ soil moisture measurements, Napf, 2021-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each. Meteorological variables such as air temperature or precipitation can be received from MeteoSwiss.
Time intervall: 1 hour.
Measurement period: June 2021-Dec 2023.
Location: flat terrain, meadow (official MeteoSwiss station)
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Wasen i.E., steep slope, 2019-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, and two additional soil temperature measurements (oC), air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2019-Dec 2023.
Location: steep hillslope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Langnau i E, 2021-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each. Meteorological variables, such as air temperature or precipitation can be received from MeteoSwiss.
Time intervall: 1 hour. Measurement period: June 2021-Dec 2023.
Location: flat terrain, meadow (measurement site of MeteoSwiss)
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Ranflüh, 2021-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, as well as air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2021-Dec 2023.
Location: hillslope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Wasen i.E., flat location, 2019-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, and two additional soil temperature measurements (oC), air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2019-Dec 2023.
Location: flat terrain, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: Detailed description of soil moisture measurment sites, soil properties and sensor installations (in German)

en: In-situ soil moisture measurements, Trub, 2021-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, as well as air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2021-Dec 2023.
Location: slope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: In the context of landslide early warning research, soil moisture (e.g. volumetric soil water content and soil water potential) has been measured at six locations in the Napf-region (Emmental, central Switzerland) since April 2019. Here, we provide the hourly mean values of all sensors from all 6 locations after a comprehensive data control, filtering and aggregation (from 10-min to 1-hour values). A sub-set of these data (for the locations Wasen i.E.) has previously been published in envidat.ch at https://www.doi.org/10.16904/envidat.369 applying the same data post-processing method. The dataset is complemented with meteorological data (locally measured air temperature and precipitation) as well as soil temperature. A detailed description of the sites and measurement installations can be found in a separate report (attached with the data).

landslide-early-warning

display_name: landslide-early-warning

soil-moisture

display_name: soil-moisture

soil-temperature

display_name: soil-temperature

soil-water-content

display_name: soil-water-content

soil-water-matrix-potential

display_name: soil-water-matrix-potential

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: In-situ soil moisture measurements Napf-region


maintainer: Yves Bühler

en: drone-data uav dsm orthophoto snow-depth-mapping photogrammetry wingtra

en: Data Access

en:
- DSM (10 cm resolution)
- Ortho (3 cm resolution)
- HS (10 cm resolution)
- original RGB drone images
- Agisoft Metashape Processing Report

All products are also available as a cloud optimized geotiff.

en: To map and assess snow depth on different dates, 9 flights were conducted in the winter season of 2020/21 at the Latschüelfurgga in Davos. The data was captured with a Sony RX1R II mounted on a Wingtra drone and was processed with the Agisoft Metashape software. High-resolution DSMs, orthomosaics and snow height rasters, as well as the original RGB images from each flight are available.

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

uav

display_name: uav

wingtra

display_name: wingtra

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: Photogrammetric Drone Data Latschuelfurgga


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

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

en: Schynige Platte Alpine Botanical Garden land-use experiment