maintainer: Ionuț Iosifescu Enescu

en: map 1GB

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: huge map 100GB

en: map 0.2GB

en: large map 10GB

en: map 0.5GB

en: map 2GB

en: Bulk Data Access

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

en: map preview 0.5MB

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

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


maintainer: Giulia Mazzotti

en: forest-structure snow-energy-balance snow-modelling forest-snow snow-distribution

state: active

type: dataset

en: Canopy data

en: Includes all canopy structure metrics computed at all surveyed locations

format: XLS

en: Meteo data to drive FSM2 model

media_type: application/zip

en: Includes meteorological driving data used to run the forest snow model FSM2, processed and parsed in text files that match the format required by FSM2. Separate files are available for each field area (Laret, Drusatscha and Ischlag) and season (2012/13, 2013/14, 2014/15). Each file includes data for the period from October 1st to May 31st at hourly resolution.

format: ZIP

mimetype: application/zip

en: Snow depth data open sites

en: Contains all snow detph data accquired at open sites nearby the forest stands during the same period.

en: Snow depth data forest sites

en: Contains all snow depth data acquired at all forest sites during winters 2012/13, 2013714 and 2014/15.

en: Coordinates data

en: Includes coordinate information of each surveyed point (7 x 276 = 1932 forest points, 2 open areas)

en: Grid point nomenclature

en: The grid structure of each forest site and the respective nomenclature applied to label individual points within each grid are outlined here.  Each point marks the location of snow depth measurements and co-located canopy structure parameters.

en:
This dataset contains all snow, canopy and meteorological data presented and used in the publication: Mazzotti, G., Essery, R., Moeser, D. & Jonas T. (2020) 'Resolving spatial variability of forest snow using an energy-balance model with a 1-layer canopy'. Water Resources Research, https://doi.org/10.1029/2019WR026129.
This publication must be cited when using this dataset.

forest-snow

display_name: forest-snow

forest-structure

display_name: forest-structure

snow-distribution

display_name: snow-distribution

snow-energy-balance

display_name: snow-energy-balance

snow-modelling

display_name: snow-modelling

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

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.

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: Snow depth, canopy structure and meterorological datasets from the Davos area, Switzerland, Winters 2012/13-2014/15, used for high-resolution forest snow modelling


maintainer: Rebecca Mott

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

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

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

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

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

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

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

en: Gletschboden setup

media_type: image/png

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

format: PNG

mimetype: image/png

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

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

en: Output explanation

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

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

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

dischmex

display_name: dischmex

eddy-covariance-measurements

display_name: eddy-covariance-measurements

patchy-snow-covers

display_name: patchy-snow-covers

snow-patch

display_name: snow-patch

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: EnviDat Support

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

en: Predictors

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

en: Description

en: Description of the predictors provided in this dataset

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

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

Paper Citation:

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

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

switzerland

display_name: switzerland

education

en: Education and science


disdrometer-data-davos-wolfgang

title_for_slug: disdrometer-data-davos-wolfgang

en: fall-velocity raclets disdrometer davos precipitation

en: Disdrometer Data Davos Wolfgang

en:
The zip folder contains one netCDF file per day for Davos Wolfgang from February 8 to March 19 2019 with a temporal resolution of 30 seconds (time in UTC) and includes:

- characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...)
- precipitation rate
- radar reflectivity
- visibility range
- weather codes
- instrument performance

en: The dataset contains information on precipitation amount and type for Davos Wolfgang (LON: 9.853594, LAT: 46.835577) from February 8 to March 19 2019. It includes: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

davos

display_name: davos

disdrometer

display_name: disdrometer

fall-velocity

display_name: fall-velocity

precipitation

display_name: precipitation

raclets

display_name: raclets


ceilometer-klosters

title_for_slug: ceilometer-klosters

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

en: Ceilometer data Klosters

media_type: mat

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

format: SERVICE

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

ceilometer

display_name: ceilometer

cloud-base-height

display_name: cloud-base-height

klosters

display_name: klosters

vertical-visibility

display_name: vertical-visibility

en: Ceilometer Klosters


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

en: Site photo of the forest station (LAB)

media_type: image/jpeg

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

mimetype: image/jpeg

en: Site photo of the field station (LAF)

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

en: Data plot of the field station (LAF)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Site aerial photo (Lausanne)

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

en: Data plot of the forest station (LAB)

en: Forest station data (LAB)

media_type: text/csv

en: Data file.

format: CSV

mimetype: text/csv

en: Field station data (LAF)

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

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

envidat

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


maintainer: Marcus Schaub

en: diameter-at-breast-height crown-condition scots-pine forest tree-species lwf dbh drought irrigation tree swiss-forest-lab

en: Swiss Forest Health Inventory

media_type: text/html

format: HTML

mimetype: text/html

en: Dataset

media_type: XLSX

en: Site photo 1

media_type: JPEG

en: Shapefile	of measurement location

media_type: URL

en: Long-term Irrigation	Experiment Pfynwald homepage

en: Site photo 3

en: Site photo 2

en: To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46° 18' N, 7° 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2°C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years.

crown-condition

display_name: crown-condition

dbh

display_name: dbh

diameter-at-breast-height

display_name: diameter-at-breast-height

drought

display_name: drought

forest

display_name: forest

irrigation

display_name: irrigation

lwf

display_name: lwf

scots-pine

display_name: scots-pine

tree

display_name: tree

tree-species

display_name: tree-species

education

en: Education and science

en: Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland


maintainer: Dirk Nikolaus Karger

en: climate-change climate climate-layers precipitation free-climate-data bioclim climatology temperature

en: CHELSA V2.1 (current)

media_type: geotiff

en: CHELSA Version 2.1 based on ERA5 forcings and CMIP6

format: GeoTIFF

en: CHELSA V1.2 (deprecated)

en: CHELSA Version 1.2 based on ERA-Interim forcings and CMIP5

en: CHELSA Technical Documentation

media_type: PDF

en: Technical documentation document including the variable names, units, scaling, offset, and background.

en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled temperature and precipitation to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction.

CHELSA data published in EnviDat includes the deprecated version 1.2 (originally published under 10.5061/dryad.kd1d4). Please use the current 2.1 version.

Paper Citation:
 > _Karger DN. et al. Climatologies at high resolution for the earth’s land surface areas, Scientific Data, 4, 170122 (2017) [doi: 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._

bioclim

display_name: bioclim

climate

display_name: climate

climate-change

display_name: climate-change

climate-layers

display_name: climate-layers

climatology

display_name: climatology

free-climate-data

display_name: free-climate-data

precipitation

display_name: precipitation


en: picea-abies nitrogen face co2 forest soil

en: Collection points

en: Collection points under the trees

en: Gross N mineralisation and nitrification

en: Gross rates of N mineralisation and nitrification (15N dilution)

en: Soil C and N

en: Analyses of C, N and their stable isotopes in the soil, and soil pH

en: Resin bags

en: Ammonium, nitrate and their 15N in resin bags

en: Collection of soil solution

en: Soil solution collected by suction cups

en: Water analyses

en: Analyses of water, especially soil solution

en: Data obtained in the free-air CO2 enrichment (FACE) experiment at Hofstetten, NW Switzerland, between 2009 and 2016. This dataset contains analyses of the soil solution throughout the experiment, especially for nitrate, as well as different analyses done at the end of the experiment: ammonium and nitrate captured by ion-exchange resin bags and extracted from soil cores, gross N mineralisation and nitrification measured by isotope dilution.

face

display_name: face

nitrogen

display_name: nitrogen

picea-abies

display_name: picea-abies

soil

display_name: soil

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: Nitrogen availability under trees exposed to CO2 enrichment (FACE)


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

en: Data access

media_type: .zip

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

education

en: Education and science

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


maintainer: Michael Lehning

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

en: ADS datasets and simulation inputs

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

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

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

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

Please cite:

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

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

alpine-precipitation

display_name: alpine-precipitation

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-distribution

display_name: snow-distribution

snow-hydrology

display_name: snow-hydrology

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


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

media_type: TIFF

en: Mean diurnal range modified from CHELSA

en: Soil pH modified from SoilGrids

media_type: tif

en: Mean annual temperature modified from CHELSA

en: Soil organic carbon content modified from SoilGrids

en: Soil clay content modified from SoilGrids

en: Frost change frequency modified from CHELSA

en: Precipitation seasonality from CHELSA

en: Precipitation of driest quarter modified from CHELSA

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

aridity

display_name: aridity

frost-change-velocity

display_name: frost-change-velocity

ground-temperature

display_name: ground-temperature

sdms

display_name: sdms

en: Environmental layers for SDM simulations (GDPlants)


en: longwave-radiation radiation shortwave-radiation davos weisfluhjoch

en: Metadata

en: DATA ACCESS

media_type: SMET

en: ASRB radiation station at the Weissfluhjoch versuchsfeld.

en: Processing doc

en: Description of processing code that was ported to R by P. Frei (2016).

en: Parameter file

media_type: text/plain

en: Description of ASRB parameters.

format: TXT

mimetype: text/plain

en:
Incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch research site, Davos, CH. The experimental site at the Weissfluhjoch (WFJ, 46.83 N, 9.81 E) is located at an altitude of 2540 m in the Swiss Alps near Davos. During the winter months, almost all precipitation falls as snow at this altitude. As a consequence, a continuous seasonal snow cover builds up every winter, with a maximum snow height ranging from 153–366 cm over the period 1934–2012. The measurement site is located in an almost flat part of a southeast oriented slope.

 References
1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.
2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.

davos

display_name: davos

longwave-radiation

display_name: longwave-radiation

radiation

display_name: radiation

shortwave-radiation

display_name: shortwave-radiation

weisfluhjoch

display_name: weisfluhjoch

education

en: Education and science

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: ASRB_WFJVF: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos


en: landslide-testsite pore-water landslides tramm pressure-perturbations cces

en: TRAMM Ruedlingen data

media_type: application/zip

en: TRAMM project data from the Ruedlingen site (exp 1 + 2). Measured parameters include soil suction, water level, soil volumetric water content, rain intensity, and soil temperature.

en:
A landslide testsite dataset related to pore water pressure perturbations on the stability of unsaturated silty sand slopes leading to the initiation and propagation of the shear deformations and eventual rapid mass movements. This project was initiated and led by the Institute of Geotechnical Engineering (IGT) of the Swiss Federal Institute of Technology (ETH Zurich) and was incorporated in a Swiss national (TRAMM) and a European Union (SafeLand) multidisciplinary research project. Field site: The experimental slope is 7.5 m wide by 35 m long, located in the Swiss lowlands on an east facing slope over-looking the river Rhine, at an altitude of ~ 350 masl. Originally there were forestry covertures of circa 80%, heights of 5-20 m. Shrubs up to 1-5 m high and a free herb layer covered ~ 50% of the surface. The average gradient was determined to be from 38° to 43° with a slightly concave surface. The underlying rock consists mainly of Molasse, which is formed by alternate layers of sea deposits under the Tethys Sea (Seawater Molasse) and land deposits (Freshwater Molasse). Several augured samples, as well as an outcrop of the bedrock about 20 m above the selected field, revealed horizontal layering of fine grained sand- and marlstone at the test site. The sandstone was later proven to be highly permeable and fissured. Grain-size distributions were determined and the soil was classified as medium-low plasticity silty sand. Site instrumentation:Measurements of soil suction, groundwater level, soil volumetric water content, rain intensity and soil temperature were taken and combined with geophysical monitoring using Electrical Resistance Tomography (ERT) and investigations into subsurface flow by means of tracer experiments. Deformations were monitored during the experiment, both on the surface via photogrammetrical methods and within the soil mass, using a flexible probe equipped with strain gauges at different points and two axis inclinometers on the top and acoustic sensors. Instruments were installed mainly in three clusters at depths of 15, 30, 60, 90, 120, and 150 cm below the ground surface over the slope, including jet-fill tensiometers, TDRs, Decagon TDRs, piezometers, soil temperature sensors, deformation probes, earth pressure cells, acoustic sensors and rain gauges. A ring-net barrier (provided by Geobrugg AG) was set up at the foot of the slope to protect the road. Experiments: A sprinkling experiment was carried out in September 2008 to investigate the hydrological and mechanical response of the slope (Experiment 1), followed by a second one to trigger a landslide in March 2009 (Experiment 2).

Publications

1. Lehmann, P., F. Gambazzi, B. Suski, L. Baron, A. Askarinejad, S. M. Springman, K. Holliger, and D. Or (2013), Evolution of soil wetting patterns preceding a hydrologically induced landslide inferred from electrical resistivity survey and point measurements of volumetric water content and pore water pressure, Water Resour. Res., 49, 7992–8004, doi:[10.1002/2013WR014560](http://dx.doi.org/10.1002/2013WR014560).

2. Springman, S. M., Kienzler, P., Casini, F., & Askarinejad, A. (2009). Landslide triggering experiment in a steep forested slope in Switzerland. In 17th International Conference of Soil Mechanics and Geotechnical Engineering, Alexandria, Egypt (pp. 1698-1701). doi: [10.3233/978-1-60750-031-5-1698](http://dx.doi.org/10.3233/978-1-60750-031-5-1698)

cces

display_name: cces

landslide-testsite

display_name: landslide-testsite

landslides

display_name: landslides

pore-water

display_name: pore-water

pressure-perturbations

display_name: pressure-perturbations

tramm

display_name: tramm

en: TRAMM project Ruedlingen experimental landslide dataset, Switzerland


irkis-soil-moisture-measurements-davos

maintainer: Nander Wever

title_for_slug: irkis-soil-moisture-measurements-davos

en: meteorological-measurements soil-moisture dischma-catchment davos meteo-station

en: IRKIS soil moisture data (zip file)

en: Compressed archive (zip) containing soil moisture measurements, as well as meteorological measurements and interpolated meteorological time series.

en:
Meteorological and soil moisture measurements from soil moisture stations installed from October 2010 - October 2013 in the area surrounding Davos, in particular in the Dischma catchment.

There are in total 7 stations: 1202, 1203, 1204, 1205, 222, 333 and SLF2. For each of the stations, there is a:

* vwc_[stn].smet: containing the soil moisture measurements

* station_[stn].smet: in-situ measured meteorlogical parameters. Note, the quality of these measurements for stations 1202, 1203, 1204 and 1205 is very low, with data gaps. Use this data with care. For stations 222, 333 and SLF2, data quality is high and only the default cautiousness should be applied.

* interpolatedmeteo_[stn].smet contains per stations a dataset derived by interpolating from several stations in the Davos area to the stations location. This dataset was generated from the output of the Alpine3D model, of which simulations are presented in the Wever et al. (2017) manuscript.

At the soil moisture measurement sites, Decagon 10HS sensors were installed, at 10, 30, 50, 80 and 120 cm depth. Per depth 2 sensors were installed, labelled A and B in the datafiles. Note that at stations 1203, 1204 and 1205, sensors were only installed at 10, 30 and 50 cm depth.

The files follow the SMET format: https://models.slf.ch/docserver/meteoio/SMET_specifications.pdf and metadata for the stations can be found in the header of the smet files.

Please cite the Wever et al. (2017) reference when using this data in publications.

For a more detailed description, please refer to: Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053-4071, https://doi.org/10.5194/hess-21-4053-2017, 2017.

dischma-catchment

display_name: dischma-catchment

meteo-station

display_name: meteo-station

meteorological-measurements

display_name: meteorological-measurements

soil-moisture

display_name: soil-moisture

en: IRKIS Soil moisture measurements Davos


en: urban-planning landscape-planning urban-landscape concur land-change

media_type: .pdf

en:
Book of abstracts from the virtual conference "From Plans to Land Change: Dynamics of Urban Regions"

Cities and urban regions are among the most dynamic land-use systems in the world, with dramatic consequences for the provision of ecosystem services and the livelihood of people. Planning is a multifaceted activity with extensive experience in the management of these urbanization processes. However, our understanding of planning’s contribution to shaping urban land use, form and structure is still incomplete, with serious consequences for the efficacy of urban planning and land change models.

This international conference aims to bring together the community of scholars working on planning evaluation and urban modelling. The participants are offered the opportunity to present their current research and to discuss how theoretical developments, data sources, comparative studies and modelling approaches might advance the field.

The conference was financially supported by the CONCUR project and sustained by Swiss Federal Research Institute WSL.

concur

display_name: concur

land-change

display_name: land-change

landscape-planning

display_name: landscape-planning

urban-landscape

display_name: urban-landscape

urban-planning

display_name: urban-planning

en: From Plans to Land Change: Dynamics of Urban Regions. Book of Abstracts


maintainer: Anita C. Risch

en: nitrification herbivore nitrogen grassland soil global-change mineralization ammonification fertilization climate nutrient-network

en: Risch et al. 2020 - Raw data for publication "Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties"

en:
we assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).

Study sites and experimental design
The 22 sites contributing to this project are part of the Nutrient Network Global Research Cooperative (NutNet, https://nutnet.umn.edu/). Mean annual temperature across our 22 sites ranged from -4 to 22°C, mean annual precipitation from 252 to 1,592 mm, and elevations from 6 to 4,261 m above sea level (Fig. 1, Supplementary Table S1). Soil organic C varied from 0.8 to 7.8%, soil total N from 0.1 to 0.6%, and the soil C:N ratio from 9.1 to 21.5. Soil clay content spanned from 3.0 to 35%, and soil pH from 3.4 to 7.6 (Supplementary Table S2). Thus, the sites covered a wide range of environments in which grasslands occur (Fig. 1, Supplementary Table S1 & S2).
At each site, the effects of nutrient addition and herbivore removal were tested via a randomized-block design (Borer et al., 2014; Supplementary Fig. S1a). Three replicate blocks with 10 treatment plots each were established at each site, with the exception of the site at bldr.us, where only two blocks were established (Supplementary Fig. S1a). The 10 plots were randomly assigned to a nutrient or fencing treatment, but only a subset of four plots was used in the current study, each with a different treatment (see below; Supplementary Fig. S1a). All plots were 5 x 5 m and divided into four 2.5 x 2.5 m subplots (Supplementary Fig. S1b). Each subplot was further divided into four 1 x 1 m square sampling plots, one of which was set aside for soil sampling (Borer et al., 2014; Supplementary Fig. S1b). Plots were separated by at least 1 m wide walkways.
In this study, we collected data from the following four treatments: (i) untreated control plots (Control), (ii) herbivore removal plots (Fence), (iii) plots fertilized with N, P, K, plus nine essential macro and micronutrients (NPK), and (iv) plots with simultaneous fertilizer addition and herbivore removal (NPK+Fence; Supplementary Fig. S1a). The number of years of treatment differed among sites (2 – 9 years since start of treatment; Supplementary Table S1). For the nutrient additions, all sites applied 10 g N m-2 yr-1 as time-release urea; 10 g P m-2 yr-1 as triple-super phosphate; 10 g K m-2 yr-1 as potassium sulfate. A micro-nutrient mix (Fe, S, Mg, Mn, Cu, Zn, B, Mo, Ca) was applied at 100 g m-2 together with K in the first year of treatments but not thereafter.
The vertebrate herbivore removal treatment (Fence) was established by fencing two plots, one control and one NPK plot, within each of the blocks (Supplementary Fig. S1a). We designed the fences so that they would effectively exclude aboveground mammalian herbivores with a body mass of over 50 g (Borer et al., 2014). At the majority of sites, the height of the fences was 180 cm, and the fence design included wire mesh (1 cm holes) on the first 90 cm along with a 30 cm outward-facing flange stapled to the ground to exclude burrowing animals; climbing and subterranean animals may potentially still access these plots (Borer et al., 2014). For slight modifications in fence design at a few sites see Supplementary Table S3. While most sites only had native herbivores, a few sites (4) were also grazed by domestic animals (Supplementary Table S1).
Potential and realized soil net N mineralization, ammonification, nitrification and other soil properties
Each site participating in the study received a package containing identical material from the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) to be used for sampling and on-site N incubations. For the field incubation, we followed the protocol by Risch et al. (2015, 2019). Briefly, we drove a 5 x 15 cm (diameter x depth) steel cylinder 13.5 cm deep into the soil after clipping the vegetation at randomized locations in each plot. The top 1.5 cm of the cylinder remained empty to capture incoming N from run-off or deposition with a polyester mesh bag (mesh-size 250 µm) filled with 13.2 ± 0.9 g of acidic and alkaline exchanger resin (1:1 mixture; ion-exchanger I KA/ion-exchanger III AA, Merck AG, Darmstadt). The bag was fixed in place with a metal Seeger ring (Bruetsch-Rüegger Holding, Urdorf, Switzerland). We then removed 1.5 cm soil at the bottom of the cylinder and placed another resin bag to capture N leached from the soil column. The exchange resin was saturated with H+ and Cl- prior to filling the bags by stirring the mixture in 1.2M HCl for 1 h and then rinsing it with demineralized water until the electrical conductivity of the water reached 5 μS/cm. The cylinders were then re-inserted into the cored hole, level with the soil surface, and incubated for an average of 42 days (range 40 to 57days). The individual site coordinators chose the timing of incubation to start approximately six weeks prior to peak plant biomass production. All incubations were completed between February 2015 and January 2016 accounting for differences in growing season between northern and southern hemispheres. At the end of the incubation, the cylinders were re-collected and immediately shipped to WSL in an insulated box together with cold packs to halt further mineralization. Gloves were worn at all times to avoid contamination of the samples. Upon arrival at WSL, we extracted the resin bags and a 20 g subsample of sieved soil (4 mm) separately in a 100 ml PE-bottle with 80 ml 1 M KCl for 1.5 h on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience). We measured NO3- (colorimetrically; Norman & Stucki, 1981) and NH4+concentrations (flow injection analysis; FIAS 300, Perkin Elmer) on these filtrates.
At the start of the field incubation, we additionally collected two soil cores of 5 x 12 cm (diameter x depth) in each sampling plot and composited them to measure potential soil net Nmin, soil chemical and biological properties (see below). We also collected an additional sample (5 x 12 cm) to assess soil physical properties, which remained within the steel cylinder. Both ends were tightly closed with plastic caps. Cores were carefully packed to avoid further disturbance, and together with the composited soil samples, were shipped to the laboratory at WSL within a few days after collection.
From the composited samples, we extracted an equivalent of 20 g dry soil with KCl, as described above, and measured NO3- and NH4+concentrations. Realized soil net Nmin was then calculated as the difference between the inorganic N content of samples collected at the end of the incubation (plus N extracted from the bottom resin bag) and the N content at the beginning of the incubation; values were scaled to represent daily mineralization rates (mg N kg-1soil d-1; Risch et al., 2015). Realized soil net Nmin values represent an average period of 42 days prior to peak biomass, typically the highest period of biological activity, and not the entire year (Risch et al., 2019).
A second subsample of the composited sample was used to determine potential soil net Nmin in the laboratory (Risch et al., 2019). Briefly, we weighed duplicate samples (8 g dry soil) into 50-ml Falcon tubes. Soil moisture was brought to 60% of the field capacity of each plot, the Falcon tubes tightly closed and then incubated at 20°C for 42 days in a dark room. Every week the Falcon tubes were opened and ventilated. At the end of the incubation, the soil samples were extracted the same way as described above and NO3- and NH4+ was determined. Potential soil net Nmin was calculated as the difference between the N content before and after the incubation and scaled to represent daily values (mg N kg-1soil d-1). Using our NO3- and NH4+ measures we also calculated potential and realized soil net nitrification and soil net ammonification to be able to better understand the drivers of fertilization and herbivore removal effects on potential and realized soil net Nmin.
A third subsample of the composite soil sample was sieved (2 mm mesh) and microbial biomass (μg Cmic g-1 soil dry weight) was estimated by measuring the maximal respiratory response to the addition of glucose solution (4 mg glucose per g soil dry weight dissolved in distilled water; substrate-induced respiration method) on approximately 5.5 g of soil (Anderson & Domsch, 1978). The rest of the composited sample was dried at 65°C for 48 h, ground and sieved (2 mm mesh) to assess a series of soil chemical properties (Risch et al., 2019). We measured the percentage of clay as an indicator of soil texture (Gee & Bauder, 1986; Risch et al., 2019).
Statistical analyses
Potential and realized soil net Nmin were square root transformed to account for a highly skewed data distribution (yt = sign(y)*sqrt|y|; negative values in the data set impeded log transformation). To assess treatment effects on potential and realized soil net Nmin, we used linear mixed effects models (LMMs) fitted by maximum likelihood using the lme function from the nlme package (version 3.131.1; Pinheiro et al., 2016), R version 3.6.1; R Foundation for Statistical Computing. Treatment (Control, Fence, NPK, NPK+Fence) was a fixed factor, with site and block as random factors, where block was nested within site. We also tested for effects of time since start of treatments in preliminary analyses by adding total treatment years as an additional fixed factor. We did not find a significant effect of years of treatment, and thus dropped this variable from the models. The LMMs were corrected using varIdent if the homogeneity of variance criterion was not met (Pinheiro et al., 2016). To visualize our results, we calculated treatment effects using Cohens’ d statistic (Cohen, 1977; Koricheva et al., 2013). Note that calculating response ratios (or log response ratios) was not possible with our data, as we have both negative and positive values. We also fitted LMMs for potential and realized soil net ammonification and nitrification to gain more insight into how global change affects the processes underpinning potential and realized soil net Nmin. We also sqrt-transformed (see above) these dependent variables. Treatment was included as a fixed factor with random factors as described above. In addition, we assessed how potential and realized soil net Nmin were related to potential and realized soil net ammonification and nitrification, respectively. For this, we calculated site averages for each treatment separately. We then ran LMMs, with potential and realized soil net Nmin as the dependent variable, potential and realized soil net ammonification/nitrification as the independent ones. Site was included as a random factor.
Based on our previous work (Risch et al., 2019) and the existing literature (Schimel & Bennett, 2004; Liu et al., 2017), we developed a priori causal conceptual models of relationships among treatments, environmental drivers, and potential and realized soil net Nmin (Supplementary Fig. S2) to test with structural equation modelling (SEM) using a d-sep approach (Shipley, 2009; Lefcheck, 2016). The variables included in the model were long-term climatic conditions, specifically, site-level mean annual precipitation (MAP) and temperature of the wettest quarter (T.q.wet), plot-level soil texture (clay content) and soil microbial biomass. Mean annual precipitation and T.q.wet were obtained from WorldClim (Hijmans et al., 2005) (http://www.worldclim.org/) and together with the experimental treatments were predicted to directly affect soil properties and soil net Nmin (Supplementary Fig. S2). Soil clay content was, in turn, predicted to affect microbial biomass and soil net Nmin. Because we determined microbial biomass prior to incubating the samples in the laboratory or field, we assumed that the abundance of these microbes would be responsible for N process rates and not vice versa (Supplementary Fig. S2). We tested our conceptual model (Supplementary Fig. S2) using the piecewiseSEM package (version 2.0.2; Lefcheck, 2016) in R 3.4.0, in which a structured set of linear models are fitted individually. This approach allowed us to account for the nested experimental design, and overcome some of the limitations of standard structural equation models, such as small sample sizes (Shipley, 2009; Lefcheck, 2016). We used the lme function of the nlme package to model response variables, including site as a random factor. Good fit of the SEM was assumed when Fisher’s C values were non-significant (p > 0.05). For all significant interactions between covariates and experimental treatments detected in the SEMs, we calculated treatment effect sizes, i.e. the differences in potential or realized soil net Nmin between Control and treatments (Fence, NPK, NPK+Fence) and plotted these values against the climate or soil covariates. Finally, we fitted LMMs for the soil variables included in our SEMs, with treatment as the fixed factor, and with site and block as random factors, where block was nested within site.

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

Paper Citation:

Risch Anita C., Zimmermann, Stefan, Moser, Barbara, Schütz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology

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

We assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).

ammonification

display_name: ammonification

climate

display_name: climate

fertilization

display_name: fertilization

global-change

display_name: global-change

grassland

display_name: grassland

herbivore

display_name: herbivore

mineralization

display_name: mineralization

nitrification

display_name: nitrification

nitrogen

display_name: nitrogen

nutrient-network

display_name: nutrient-network

soil

display_name: soil

education

en: Education and science

en: Anthropogenic change and soil net N mineralization


wind-lidar-davos-wolfgang

title_for_slug: wind-lidar-davos-wolfgang

en: wolfgangpass profiles lidar raclets wind

en: Wind LIDAR data Wolfgang

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

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

lidar

display_name: lidar

profiles

display_name: profiles

raclets

display_name: raclets

wind

display_name: wind

wolfgangpass

display_name: wolfgangpass

envidat

en: EnviDat: the Environmental Data Portal

en: 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: Wind LIDAR Davos Wolfgang


en: species-range-map fagales species-distribution-model pinales species-richness species-occurrence

en: Species range maps of species in Fagales and Pinales (with enough occurrences)

media_type: ZIP

en: species_map.zip includes species 1690 range maps generated based on the integrated method described in Lyu et al., (2022). File names with (polygon) indicate that this map is only generated by polygon mapping, as there are not enough occurrences for SDMs.

en: Occurrence maps for species of Fagales and Pinales with few records

en: Point_buffer.zip includes 267 occurrence maps of species that do not have enough occurrence data for polygon mapping.

en:
This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022).
To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details).

fagales

display_name: fagales

pinales

display_name: pinales

species-distribution-model

display_name: species-distribution-model

species-occurrence

display_name: species-occurrence

species-range-map

display_name: species-range-map

species-richness

display_name: species-richness

en: Species distribution maps of Fagales and Pinales (GDPlants)


snow-drift-station-flowcapt

maintainer: Benjamin Walter

title_for_slug: snow-drift-station-flowcapt

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

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: 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: 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: 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

snow-blowing

display_name: snow-blowing

snowdrift

display_name: snowdrift

wind-speed

display_name: wind-speed

en: Snow Drift Station - Flowcapt


maintainer: Ionut Iosifescu Enescu

en: jupyter-notebooks hpc open-science reproducibility research-data-management open-source open-data

en: Open Science Support @ WSL (Poster)

en:
This poster was originally created for the swissuniversities Open Science Action Plan: Kick-Off Forum, and showed to the audience on 17.10.2019. It illustrates how the environmental data portal EnviDat provides the tools for fostering Open Science and Reproducibility of scientific research at WSL.

Supporting open science is a highly relevant user requirement for EnviDat and for implementing FAIR (Findability, Accessibility, Interoperability and Reusability) principles at dataset level.
EnviDat encourages WSL scientists to complement data publication with a complete description of research methods and the inclusion of the open source software, code or scripts used for processing the dataset or for obtaining the published results. By openly publishing open software (e.g. as Jupyter notebooks) alongside research data sets, researchers can contribute to mitigate reproducibility issues.

EnviDat also promotes and supports, where possible and practical, the publication of software as Jupyter notebooks. Jupyter notebooks provide a solution for improved documentation and interactive execution of open code in a wide range of programming languages (Python, R, Octave/Matlab, Java or Scala). These programming languages are widely used in environmental research at WSL and well supported by the Jupyter-compatible kernels. We have sucessfully interfaced EnviDat-hosted notebooks with the WSL High-Performance Computing (HPC) Linux Cluster through a JupyterHub/JuypterLab beta installation on the HPC cluster implemented in close collaboration with the WSL IT-Services.
For existing software that cannot be easily migrated to Jupyter Notebooks, the Open Science and Reproducibility is assisted by containerisation. We have proven that several Singularity containers can successfully run on WSL's HPC cluster.

Finally, the researchers can upload the data/results complemented by code (e.g. as Jupyter Notebooks, or Singularity containers) and any additional documentation in EnviDat. Consequently, they will receive a DOI for the entire dataset, which they can reference in their science paper in order to publish a more reproducible research.

_License_: This poster is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 "No Rights Reserved" international license. You can reuse this poster in any way you want, for any purposes and without restrictions.

hpc

display_name: hpc

jupyter-notebooks

display_name: jupyter-notebooks

open-data

display_name: open-data

open-science

display_name: open-science

open-source

display_name: open-source

reproducibility

display_name: reproducibility

research-data-management

display_name: research-data-management

education

en: Education and science

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: Open Science Support at the Swiss Federal Research Institute WSL. The EnviDat Concept


en: uncertainty rain-on-snow snow-water-equivalent cryosphere climate-change-impact natural-climate-variability

en: climate realizations

en: The dataset contains for three variables (snow water equivalent, surface water input and liquid precipitation) 50 realizations of current and future climate periods for two time horizons (mid end end of century), two emission senarions (RCP 4.5 and 8.5) and 10 climate model chains (all EUR11 chains within CH2018). To quantify natural climate variability for projections of snow conditions and resulting rain-on-snow (ROS) flood events, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The data was extracted in 2021 from original model output.

climate-change-impact

display_name: climate-change-impact

cryosphere

display_name: cryosphere

natural-climate-variability

display_name: natural-climate-variability

rain-on-snow

display_name: rain-on-snow

snow-water-equivalent

display_name: snow-water-equivalent

uncertainty

display_name: uncertainty

en: Multiple realizations of daily snow water equivalent, surface water input and liquid precipitation projections for mid- and late-century


en: streamflow groundwater stable-water-isotopes alptal runoff

en: Precipitation and discharge extra event

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
With any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
Q: Discharge in mm per hour at catchment outlet (697234,210472)
P: Rainfall in mm per hour averaged for two rain gauges (697494,210675) and (698004, 210680)

en: Precipitation and discharge event B

en: Deuterium streamwater event B

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date and time of sample collection (UTC; year-month-day, hours:minutes)
Q d.D: Deuterium composition (per mil) of streamwater sampled at the catchment outlet (697234,210472); the first sample is used as pre-event streamflow sample

en: Deuterium rainfall event C

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
P d.D: Deuterium composition of the rainfall samples (per mil)
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)

en: Deuterium rainfall event B

en: Deuterium rainfall event A

en: Deuterium groundwater and baseflow October 2017

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

Date: Date (year-month-day)
Location: Well number or outlet
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)
riparian: riparian wells (i.e. from the riparian-like cluster as presented in Kiewiet et al. (2019)) are marked a one, all other wells are marked a zero
d.D (per mil): Deuterium composition (per mil)

en: Deuterium streamwater event C

en: Deuterium streamwater event A

en: Deuterium streamwater extra event

en: Deuterium groundwater and baseflow August 2017

en: Precipitation and discharge event A

en: Deuterium groundwater and baseflow October 2016

en: Precipitation and discharge event C

en: Deuterium rainfall extra event

en:
This dataset includes discharge and rainfall measurements and deuterium compositions of streamflow, rainfall and groundwater, for four rainfall events and three baseflow snapshot campaigns in the Studibach (Alptal, Switzerland). More specifically, we present the following data:

- Specific discharge at the catchment outlet at 5-minute resolution (mm per hour);
- Rainfall at 5-minute resolution (mm per hour);
- Rainfall deuterium composition (‰);
- Stormflow deuterium composition (‰);
- Groundwater and baseflow deuterium compositions (‰).

For the files containing rainfall and discharge timeseries (QP), and rainfall and streamwater deuterium compositions ("Deuterium_Rainfall" and "Deuterium_Streamwater"), we added the corresponding event identifier (A, B, C or extra) to the file names. For the files containing the groundwater and baseflow deuterium values ("Deuterium_Snapshot") we added the sample collection date to the file name. We included the X and Y coordinates for each data point (coordinate system: CH1903 LV3) as well as the date and time (UTC).

More information on the data collection and preparation can be found in Kiewiet et al. (in review). A detailed description of the baseflow snapshot campaigns can also be found in Kiewiet et al., 2019.

alptal

display_name: alptal

en: Discharge, rainfall, and deuterium compositions of streamwater, rainwater and groundwater, for four rainfall events in the Studibach, Alptal, Switzerland


maintainer: Yohann Chauvier

en: environmental-stratification cluster target-group plant-species survey-effort covariate-correction random-stratified-sampling background-data independent-dataset point-process-model

en: Non-copyright data, scripts, diversity outputs

en:
Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models.

Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1’900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.

Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r≃0.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.

background-data

display_name: background-data

cluster

display_name: cluster

covariate-correction

display_name: covariate-correction

environmental-stratification

display_name: environmental-stratification

independent-dataset

display_name: independent-dataset

plant-species

display_name: plant-species

point-process-model

display_name: point-process-model

random-stratified-sampling

display_name: random-stratified-sampling

survey-effort

display_name: survey-effort

target-group

display_name: target-group

education

en: Education and science

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: Novel methods to correct for observer and sampling bias in presence-only species distribution models


maintainer: Jana von Freyberg

en: endmember-mixing-models root-zone review ecohydrology stable-water-isotopes uncertainties plant-water-uptake

en: Data set of MEE-20-04-264

en: The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal ‘Methods in Ecology and Evolution’.

en: Data set of MEE-20-04-264 (CSV)

media_type: CSV

en: The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal 'Methods in Ecology and Evolution'.

ecohydrology

display_name: ecohydrology

endmember-mixing-models

display_name: endmember-mixing-models

plant-water-uptake

display_name: plant-water-uptake

review

display_name: review

root-zone

display_name: root-zone

stable-water-isotopes

display_name: stable-water-isotopes

uncertainties

display_name: uncertainties

en: Data set of: Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources


en: canopy-height snow-distribution airborne-laser-scanning forest-structure snow-depth

en: Snow depth maps

en: Snow depth maps from two snow-on flights at each of the three field areas Davos (March 20th and 31st 2017), Engadien (March 20th and 31st, 2017) and Grand Mesa (February 8th and 16th, 2017)

en: Readme

en: Terrain elevation maps

en: Digital elevation model computed from snow-off flights for each of the field areas Davos, Engadine and Grand Mesa

en: Canopy height maps

en: Canopy height maps of each of the three field areas Davos, Engadine and Grand Mesa

en:
This dataset includes snow depth, canopy height and terrain elevation maps of forest stands in the Grisons (CH) and at Grand Mesa (CO,USA) derived from airborne lidar. Data were acquired i) within a pilot mission of NASA's Airborne Snow Observatory in the Swiss Alps in March 2017 and ii) during NASA’s SnowEx campaign at Grand Mesa in February 2017. Snow depth maps are available for two dates separated by approx.1 week, and include an area of ca. 0.5km2 for each of the three sites Davos, Engadine and Grand Mesa.
All data were presented and analyzed in the publication 'Revisiting Snow Cover Variability and Canopy Structure within Forest Stands: Insights from Airborne Lidar Data' (Mazzotti et al., 2019, WRR, doi: 10.1029/2019WR024898). This publication must be cited when using this dataset.

Paper Citation:
 > _Giulia Mazzotti; William Ryan Currier; Jeffrey S. Deems; Justin M. Pflug; Jessica D. Lundquist; Tobias Jonas (2019). Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data. Water Resources Research, 55, 6198– 6216, [doi: 10.1029/2019WR024898](https://doi.org/10.1029/2019WR024898)._

airborne-laser-scanning

display_name: airborne-laser-scanning

canopy-height

display_name: canopy-height

forest-structure

display_name: forest-structure

snow-depth

display_name: snow-depth

snow-distribution

display_name: snow-distribution

en: ALS-based snow depth and canopy height maps from flights in 2017 (Grisons, CH and Grand Mesa, CO)


maintainer: Matthias Haeni

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

en: Site aerial photo (Beatenberg)

media_type: image/jpeg

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: Data plot of the field station (BAF)

media_type: application/pdf

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: Site photo of the field station (BAF)

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

en: Field station data (BAF)

en: Data file.

en: Forest station data (BAB)

en: Data plot of the forest station (BAB)

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.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

education

en: Education and science

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


high-resolution-climate-data-for-europe

title_for_slug: high-resolution-climate-data-for-europe

en: precipitation europe eur11 cordex minimum-temperature maximum-temperature air-temperature daily-climate

en: Data Access

media_type: NetCDF

en: CHELSA EUR11 Technical Documentation

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.

en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present downscaled climate data for the CORDEX EUR11 domain at a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperature lapse rates. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height. The resulting data consist of a daily temperature and precipitation timeseries.

The data is distributed under a: Creative Commons: Attribution 4.0 International (CC BY 4.0) license.

air-temperature

display_name: air-temperature

cordex

display_name: cordex

daily-climate

display_name: daily-climate

europe

display_name: europe

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

en: High resolution climate data for Europe


weather-station-davos-wolfgang

maintainer: Jörg Wieder

title_for_slug: weather-station-davos-wolfgang

en: weather raclets pressure temperature relative-humidity

en: Weather Station Davos Wolfgang

media_type: text/csv

en: The data file contains date and time (YYYY-MM-DD hh:mm:ss) in UTC, temperature (°C), relative humidity (%), pressure (hPa) at 10 m above ground and above sea level. The data was averaged over 10 minutes.

en: The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577).

pressure

display_name: pressure

raclets

display_name: raclets

weather

display_name: weather


en:
This dataset contains the statistics on the number of avalanche fatalities per **calendar year** in Switzerland. The data collection commences with the beginning of the year 1937. After the completion of a hydrological year, which is the standard way avalanche fatalities are summarized in Switzerland and ends on the 30th of September, the new data is appended to the existing dataset.
If you require annual statistics per hydrological year, please download the data from here: [https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936]

The following information is contained (by column and column title):
 - year
 - number of fatalities in the backcountry (=tour)
 - number of fatalities in terrain close to ski areas (=offpiste, away from open and secured ski runs)
 - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors)
 - number of fatalities in or around buildings or in settlements (= buildings)
 - sum (of all four categories)

The definitions for these four categories, as described in the guidelines to the avalanche accident database are:

tour:
activities include back-country ski, snowboard or snow-shoe touring

offpiste:
access from ski area, generally from the top of a skilift with short hiking distances

transportation.corridors (Techel et al., 2016):
people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams)

buildings (Techel et al., 2016):
people inside or just outside buildings, and workers on high alpine building sites

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-fatalities

display_name: avalanche-fatalities

fatalities-in-or-around-buildings

display_name: fatalities-in-or-around-buildings

fatalities-in-settlements

display_name: fatalities-in-settlements

fatalities-in-the-backcountry

display_name: fatalities-in-the-backcountry

fatalities-on-transportation-corridors

display_name: fatalities-on-transportation-corridors

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


en: precipitation discharge prevah drought-2018 hydrological-simulations low-flow soil-moisture

en: Low-flow storage

en: Simulated low-flow storage for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

en: Precipitation

en: Simulated precipitation for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

en: Discharge

en: Simulated discharge for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

en: Shapefile of 307 catchments in Switzerland

en: Shapefile of 307 catchments for which simulations of precipitation, discharge, soil moisture, and low flow are provided. The OBJECTID entry corresponds to the data columns in the textfiles of the simulated data provided.

en: Soil moisture

en: Simulated plant-available soil moisture for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

en: The dataset provides simulated 1) precipitation, 2) discharge, 3) soil moisture, and 4) low-flow simulations for 307 medium-sized catchments in Switzerland for the period 1981-2018. The data were simulated using the hydrological model PREVAH in its gridded-version. The simulated time series are provided at daily resolution. A detailed description of the modeling approach can be found in Brunner et al. 2019 submitted to NHESS.

discharge

display_name: discharge

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

precipitation

display_name: precipitation

prevah

display_name: prevah

soil-moisture

display_name: soil-moisture

en: Hydro-meteorological simulations for the period 1981-2018 for Switzerland


snow-drift-station-snow-and-air-data

title_for_slug: snow-drift-station-snow-and-air-data

en: snow-temperature raclets snow-height snow-depth temperature snow

en: Relative Humidity

en: The relative humidity (%) was measured with a Campbell CS-215 at a height of 2.2 m above ground. The time is given in UTC+1.

en: Snow Surface Temperature

en: The snow surface temperature (°C) was measured with a Campbell SI-111 at 0.7 m above ground. The time is given in UTC+1.

en: Air Temperature

en: The air temperature (°C) was measured with a Campbell CS-215 which was mounted at a height of 2.2 m above ground. The time is given in UTC+1.

en: Snow Depth

en: The snow depth (cm) was measured with a Lufft SHM-31 which was mounted at 1.7 m above ground. The time is given in UTC+1.

en: Snow and air data was monitored at Gotschnagrat (LON: 46.859 LAT: 9.849) by an infrarot radiometer (Campbell SI-111) for snow temperature (°C), a snow height sensor (Lufft SHM-31) for snow height change (cm) and a temperature and humidity sensor (Campbell CS-215) for air temperature (°C) and relative humidity (%). No filter was applied to the sensors and the smapling frequency was 1 Hz.

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-height

display_name: snow-height

snow-temperature

display_name: snow-temperature

temperature

display_name: temperature

education

en: Education and science

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: Snow Drift Station - Snow and Air Data


en: weed-management non-native-plant grassland herbivory native-plant

media_type: XLSX

en: Correction: Data file native and non-native plant interactions in Australian grasslands

en:
taxa tab:	list and overview of all the recorded species

* family:                 family of the plant

* genus:                genus of the plant

* species:                plant species name

* origin:                indicates whether the plant is native to Australia or not (non-native)

* common name:	        common name of the plant species

* functional group:	        functional group of the plant species: forb, graminoid, fern, tree

* functional group detailed:	gives more detail about the type of graminoid and forb: legume, forb (non-leguminous),
                        grass, sedge, rush, fern, tree

* lifeform:                annual or perennial lifeform (species which can have both of these lifeforms where
                        considered as annuals)

plot tab:	list of species found in each plot and their cover (%)

* sampling.session:        provides information about the sampling time:
                        May2020 = data that was collected in May 2020
                        Nov2020 = data that was collected in November 2020

* farm.name:	            name of the farms where the sampling took place:
                        SF = farm of the Southern group
                        NF = farm of the Northern group
                        numbers 1-3 = randomly allocated to the Southern group of farms
                        numbers 4-6 = randomly allocated to the Northern group of farms

* site.type:	                KG: Kangaroo grass dominated site
                        ALG: ALG+KG site (Kangarooo grass and African lovegrass co-dominated site)

* plot.no:                plot number: 9-12

* scientific.name:	        scientific name of the plant species found in a certain plot

* functional group:	        indicates whether the species found is a tree, forb, graminoid or fern

* functional group detailed:	indicates whether the species found is a tree, legume, forb (non-leguminous),
                        grass, sedge, rush, fern

* lifeform:                specifies the lifeform of the species: annual or perennial lifeform
                        (species which can have both of these lifeforms where considered as annuals)

corrections tab:	Detailed description of corrections made in plot.data tab

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

Paper Citation:

_Schlierenzauer, C., Risch, A.C., Schütz, M., Firn, J. 2021. Non-native Eragrostis curvula reduces plant species diversity in pastures of South-eastern Australia even when native Themeda triandra remains co-dominant. Plants 10, 596._

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

Study area
The study was conducted in the lowland grassy woodlands of the Bega Valley Region, which is located in the south-east corner of New South Wales, Australia. Embedded between the Pacific Ocean and the Australian Alps, the lowland grassy woodlands are mostly located on granitic substrates and reach elevations of roughly 500 m above sea level. Typically, these grassy woodlands receive less precipitation (mean annual precipitation between 700-1100 mm) compared to the more elevated areas that surround them (NSW Government - Office of Environment and Heritage 2017).
The vegetation is dominated by an open tree canopy layer consisting of Eucalyptus tereticornis Sm, Angophora floribunda Sm. (Sweet) and a range of other eucalypt species. Sometimes shrub or small trees are also present, whereas grasses and forbs form the ground-cover. In areas without intensive agricultural history, this layer is dominated by perennial, tussock grasses such as Themeda triandra Forssk, Microlaena stipoides R.Br (Weeping Grass), Eragrostis leptostachya Steud. (Paddock Lovegrass) and Echinopogon ovatus P.Beauv (Forest Hedgehog Grass). The remaining inter-tussock spaces are occupied by a diversity of growth-restricted grasses and herbaceous forbs (NSW - Department of Planing, Industries and Environment 2019; NSW Government - Office of Environment and Heritage 2017).
Clearing, pasture sowing, fertilizer application and livestock grazing resulted in a dramatic decrease in the extent of these natural woodlands, with less than five percent within conservation reserves and overall, with only about 20% of their original extent in New South Wales still existing (Tozer et al. 2010). The remaining areas outside of reserves are threatened by altered fire frequencies, habitat clearing, livestock grazing and especially by non-native plant invasion, particularly Eragrostis curvula (Schrad.) Nees. For this reason, the grassy woodlands are listed as an endangered ecological community in the NSW state legislation. Additionally, they are considered as critically endangered by the Commonwealth of Australia (Threatened Species Scientific Committee (TSSC) 2013).

Experimental design and sampling
The study was conducted on six farms and in each of them two sites were chosen, representing a paired design. One of the sites at each farm is dominated by native Themeda triandra, the other one co-dominated by non-native Eragrostis curvula and Themeda triandra. All farms are within a radius of approximately 10 km from the town Candelo. Three of the farms are located North (36°40’ to 36°42’ S and 149°38’ to 149°42’ E) and three of them are located South (36°51’ to 36°49’ S and 149°38’ to 149°42’ E) of Candelo.
Non-native herbivores (mainly cattle, sheep and rabbits) and native herbivorous marsupials (mainly kangaroos, wallabies and wombats) are present in the area of these sites. On each site, data was collected within four plots (each 1 x 1 m) in May and November 2020. All plant species found within a plot were recorded and their relative abundance was estimated.

References
NSW - Department of Planing, Industries and Environment. 2019. “Lowland Grassy Woodland in the South East Corner Bioregion - Endangered Ecological Community Listing.” https://www.environment.nsw.gov.au/topics/animals-and-plants/threatened-species/nsw-threatened-species-scientific-committee/determinations/final-determinations/2004-2007/lowland-grassy-woodland-south-east-corner-bioregion-endangered-ecological-community-l (February 18, 2021).
NSW Government - Office of Environment and Heritage. 2017. “Lowland Grassy Woodland in the South East Corner Bioregion - Profile.” https://www.environment.nsw.gov.au/threatenedSpeciesApp/profile.aspx?id=20070 (January 31, 2021).
Threatened Species Scientific Committee (TSSC). 2013. Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) Conservation Advice for Lowland Grassy Woodland in the South East Corner Bioregion. http://www.environment.gov.au/biodiversity/threatened/communities/pubs/82-conservation-advice.pdf.
Tozer, Mark et al. 2010. “Native Vegetation of Southeast NSW: A Revised Classification and Map for the Coast and Eastern Tablelands.” Cunninghamia : a journal of plant ecology for eastern Australia 11(3): 359–406.

grassland

display_name: grassland

herbivory

display_name: herbivory

native-plant

display_name: native-plant

non-native-plant

display_name: non-native-plant

weed-management

display_name: weed-management

en: Native and no-native plant interactions in Australian grasslands


maintainer: Frank Techel

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

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

en: Rutschblock data (Switzerland)

en: ECT data (Norway, Switzerland)

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

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

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland

en: Stability tests, avalanche observations, Switzerland, Norway


maintainer: Sebastian Schlögl

en: stable-boundary-layer turbulent-sensible-heat-fluxes atmospheric-turbulence atmosphere snow

en: Turbulence data PM07

en: NWW

en: Turbulence data WFJ11

en: Turbulence data AA00

en: sensor in 0.2 m height

en: SEE

en: Turbulence data GR00

en: sensor in 1 m height

en: Turbulence data WFJ07

en: lower sensor

en: sensor in 2 m height

en: sensor in 25 m height

en: upper sensor

en:
We used five different atmospheric turbulence datasets from four test sites, with these sites showing differences in their topographical characteristics. We chose one typical alpine test site with high topographical complexity (Weissfluhjoch, Davos, Switzerland) and three test sites consisting of one glacier site (Plaine Morte, Crans-Montana, Switzerland) and two polar sites (Greenland and Antarctica) representing a quasi-ideal site with homogeneous surface and quasi infinite fetch in all directions.
The turbulent sensible heat flux was calculated using the eddy-covariance method. Note that the sonic temperature fluctuations have been converted into virtual temperature fluctuations.
Three-dimensional wind velocity and air temperature were processed using a linear detrending (Rannik and Vesala, 1999) and a planar fit approach (Massmann and Lee, 2002) to rotate the coordinate system. Air temperature, relative humidity and air pressure from weather stations were used to calculate air properties, which are required for the data processing. The weather stations are located in the immediate vicinity of the turbulence tower and are affected by the same air masses. Turbulence data were averaged to 30-min intervals, whilst changing to a 15-min time interval marginally affects the heat fluxes at the Weissfluhjoch test site (Mott et al., 2011). Note that we define a negative sensible heat flux as being directed towards the snow surface and a positive sensible heat flux as being directed upwards.
The selected datasets and corresponding test sites are briefly introduced below:

Weissfluhjoch 2007 (WFJ07): A vertical set-up of two three-dimensional ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) was used at the traditional field site Weissfluhjoch (2540 m asl.) to measure three-dimensional wind velocity and air temperature at a frequency of 20 Hz. The sensors were mounted 3 m and 5 m above the ground and provided reliable data for 50 days between 11 February 2007 and 24 April 2007. Further information on the field campaign can be found in Stössel et al. (2010) and Mott et al. (2011).

Weissfluhjoch 2011-13 (WFJ11): Three-dimensional wind velocity and air temperature were recorded at 5 m above the ground at a frequency of 10 Hz with a three-dimensional ultrasonic anemometer (CSAT3). The analysis was conducted for data obtained between February and March in the years 2011-13.

Plaine Morte 2007 (PM07): Two three-dimensional ultrasonic anemometers (CSAT3) were installed on a horizontal boom facing opposite directions (west-north-west vs. east-south-east) at 3.75 m above the ground to measure air temperature and three-dimensional wind velocity at 20 Hz. The data were collected at the almost flat field site on the Plaine Morte glacier (2750 m asl.) near Crans-Montana, Switzerland from February to April 2007. High quality meteorological data were additionally recorded and used to force the model. A detailed description about the set-up at the Plaine Morte glacier can be found in Huwald et al. (2009) and Bou-Zeid et al. (2010).

Greenland 2000 (GR00): High-frequency three-dimensional ultrasonic anemometer measurements (CSAT3) were recorded at 50 Hz at the Summit Camp (72.3 °N, 38.8 °W, 3208 m asl.) located on the northern dome of the Greenland ice sheet. Data were collected at 1 m and 2 m above the snow surface during summer in 2000 and 2001. Additionally, meteorological measurements were obtained for the post processing and used to force the model. More information about the field campaign can be found in Cullen et al. (2007, 2014).

Antarctica 2000 (AA00): A set-up of three vertical three-dimensional ultrasonic anemometers (DA-600, Kaijo Denki) were installed at Mizuho Station (70°42' S, 44°20' E, 2230 m asl.) in Eastern Antarctica at 0.2, 1 and 25 m and recorded turbulence data at a frequency of 100 Hz from October to November 2000. Longwave and shortwave radiation, relative humidity, air and snow surface temperature were additionally measured and used to force the model. More information about the field campaign can be found in Nishimura and Nemoto (2005).

atmosphere

display_name: atmosphere

atmospheric-turbulence

display_name: atmospheric-turbulence

stable-boundary-layer

display_name: stable-boundary-layer

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

en: How do stability corrections perform in the stable boundary layer over snow?