en: We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland,  sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

en: phenology cropland grassland machine-learning agriculture vegetation-indices sentinel-2 switzerland habitat remote-sensing

envidat

en: EnviDat: the Environmental Data Portal

en: Cropland and grassland map of Switzerland based on Sentinel-2 data

envi

en: Environment

en:
Resource Title: (current version) Cropland, grassland, and shrubland map of Switzerland (v.1.5)
    Size: 46196707 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/014565ae-748b-4f64-bf14-53443386cf56/download/ch_cropgrassshrubmap_v1_5.tif
    Description: (1) cropland; (2) grassland; (3) shrubland; other LC classes are masked out from the map

format: TIFF

media_type: image/tiff

en:
Resource Title: Cropland and grassland map of Switzerland  (v.1.0)
    Size: 773453532 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/0bb5fc4c-8331-4851-81fb-6c8b5f331207/download/ch_cropgrassmap_v2_4.tif
    Description: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; other LC classes are masked out from the map

en:
Resource Title: (current version) Cropland and grassland map of Switzerland (v.1.5)
    Size: 36873826 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/05a74913-fbe7-477a-9d8a-d1e4f97c30c2/download/ch_cropgrassmap_v1_5.tif
    Description: (1) cropland; (2) grassland; other LC classes are masked out from the map

en:
Resource Title: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.5)
    Size: 12925 bytes
    Format: text/javascript
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/44ff8fb6-ac93-4278-a645-9a1e81d1ce2f/download/sentinel12_wholech_croplandgraslandshrubland_published_v2.js
    Description: The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland. Compare to the previous version the newer version includes the shadow masking procedure which might be important in the mountainous areas.

format: javascript

en:
Resource Title: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.0)
    Size: 22351282 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/4643ef04-e299-4ed2-b03d-f16624ce2f29/download/comb_sen2maskbuiltup07.tif
    Description: !!! Please check out the newer version in this repository !!! Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.7.

en:
Resource Title: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.0)
    Size: 10103 bytes
    Format: text/javascript
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/5b8620ec-a059-4f05-a131-c559049e5a44/download/sentinel12_wholech_croplandgraslandshrubland_published_v1.js
    Description: !!! Please check out the newer version in the repository !!! The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland

en:
Resource Title: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.5)
    Size: 22951009 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/1433230e-25d2-40e4-b512-f9216f158c62/download/comb_sen2maskbuiltup07.tif
    Description: Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.7.

en:
Resource Title: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6  (v1.0)
    Size: 20469824 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/b358e0ad-50c8-45a8-b4fd-98ccf6d59532/download/comb_sen2maskbuiltup06.tif
    Description: !!! Please check out the newer version in the repository !!!
Mask of unsealed surfaces of Switzerland based on 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6

en:
Resource Title: Cropland, grassland, and shrubland map of Switzerland (v1.0)
    Size: 773453532 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/63a7c7f1-af24-49eb-a082-2f9244034b19/download/ch_cropgrassmap_v2_4_withshrubs.tif
    Description: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; (3) shrubland; other LC classes are masked out from the map

en:
Resource Title: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6 (v1.5)
    Size: 21028763 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/b583e418-92c3-4e34-9f1b-de7f8823234c/download/comb_sen2maskbuiltup06.tif
    Description: Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6.

agriculture

display_name: agriculture

cropland

display_name: cropland

grassland

display_name: grassland

habitat

display_name: habitat

machine-learning

display_name: machine-learning

phenology

display_name: phenology

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-indices

display_name: vegetation-indices


$oid: OBSOLETE

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

en: switzerland studibach alptal time-series groundwater

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

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

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

fr: Environnement

it: Ambiente

format: TXT

alptal

display_name: alptal

groundwater

display_name: groundwater

studibach

display_name: studibach

time-series

display_name: time-series


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

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

envidat

en: EnviDat: the Environmental Data Portal

en: Predicted cloud droplet numbers Davos Wolfgang

en:
Resource Title: Predicted cloud droplet numbers (kappa=0.1) Davos Wolfgang
    Size: 312210 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e5a506af-7ff0-47ad-afe2-36de4b4e1ea9/resource/245ebcc2-4dd5-4339-9f76-b61411fa103b/download/predicted_droplets_wop_kappa0.1.csv
    Description: In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.1 is assumed, which is the lower limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

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

format: CSV

media_type: text/csv

en:
Resource Title: Predicted cloud droplet numbers (kappa=0.25) Weissfluhjoch
    Size: 313834 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e5a506af-7ff0-47ad-afe2-36de4b4e1ea9/resource/b5c4c596-2dd4-45f4-b79f-61f91d82aad0/download/predicted_droplets_wop_kappa0.25.csv
    Description: In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.25 is assumed, which is the upper limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

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

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

wolfgangpass

display_name: wolfgangpass


en:
Cloud Condensation Nuclei (CCN) data: A Droplet Measurement Technologies (DMT) single-column continuous-flow streamwise thermal gradient chamber (CFSTGC; Roberts and Nenes, 2005) was deployed at the measurement site Weissfluhjoch (2700 m a.s.l., LON: 9.806475, LAT: 46.832964) to record the in-situ CCN number concentrations between February 24 and March 8 2019 for different supersaturations (SS). To account for the difference between the ambient (~735 mbar) and the calibration pressure (~800 mbar), the SS reported by the instrument is adjusted by a factor of 0.92. The CFSTGC was cycled between 6 discrete SS values ranging from 0.09% to 0.74%, producing a full CCN spectrum every hour. The raw CCN measurements are filtered to discount periods of transient operation and whenever the room temperature housing the instrument changed sufficiently to induce a reset in column temperature. Additional information can be found in Section 2.1.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).
Hygroscopicity data: The CCN number concentration measurements were directly related to the size distribution and total aerosol concentration data measured by the Scanning Mobility Particle Size Spectrometer (SMPS) instrument at the same station (https://www.envidat.ch/dataset/aerosol-data-weissfluhjoch) to infer the particles hygroscopicity parameter (kappa). For each SMPS scan, the particles critical dry diameter (Dcr) is estimated by integrating backward the SMPS size distribution, until the aerosol number matches the CCN concentration observed for the same time period as the SMPS scan. Assuming the particle chemical composition is internally mixed, the kappa is determined from Dcr and SS, applying Köhler theory. Additional information can be found in Section 2.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).
Predicted cloud droplet numbers: Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from the SMPS instrument deployed at Weissfluhjoch. The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at Davos Wolfgang and are extracted for the altitude of interest, being 1100 m above ground level for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

en: supersaturation dmt-ccn-chamber ccn sigma-w cloud-droplets hygroscopicity cloud-modeling raclets weissfluhjoch

en: CCN, hygroscopicity, predicted cloud droplet numbers Weissfluhjoch

envi

en: Environment

en:
Resource Title: CCN data Weissfluhjoch
    Size: 79738661 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ecb08acd-01d0-40e6-ac42-584aab9fc27f/resource/80b5fb31-8f82-4648-bc82-d11373bd186a/download/ccnspectrumwfj.csv
    Description: In-situ CCN measurements were conducted between February 24 and March 8 2019 at Weissfluhjoch. The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* Filtered CCN number concentrations (in cmˉ³) measured at 6 discrete supersaturations (SS) ranging from 0.09% to 0.74%. The name of each CCN column is indicative of the SS developed within the instrument.
* Instrument characteristics such as sample temperature (in °C) and pressure (in mbar)
* A code that refers to the reason of filtering.

Missing or filtered CCN values are indicated by "-999", while a declaration of each "Error_code" can be found in the following list:

_Error_code_:

__0000__: Measurements without error

__1000__: Period of transient operation after a supersaturation change

__2000__: Sharp change (decrease) in the room temperature housing the instrument, which induces a reset in column temperature

__3000__: Irregular values in the room temperature housing the instrument

__9999__: Missing data

en:
Resource Title: Hygroscopicity parameter Weissfluhjoch
    Size: 344260 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ecb08acd-01d0-40e6-ac42-584aab9fc27f/resource/5032c15d-ac68-4e8f-822b-3503a34db837/download/hygroscopicitywfj.csv
    Description: The aerosol hygroscopicity parameter is derived combining the CCN and SMPS size distribution data collected between February 24 and March 8 2019 at Weissfluhjoch. The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The instrument supersaturation (SS%)
* The calculated hygroscopicity parameter (kappa)
* The calculated critical dry diameter of the particles (Dcr) in nm.

Missing values are indicated by "-999".

en:
Resource Title: Predicted cloud droplet number concentrations Weissfluhjoch
    Size: 256123 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ecb08acd-01d0-40e6-ac42-584aab9fc27f/resource/f732a28e-10b1-411f-a497-b784e1b716af/download/predicted_droplets_wfj_kappa0.25.csv
    Description: In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Weissfluhjoch station between February 24 and March 8 2019. For this, a hygroscopicity parameter of 0.25 is assumed, according to the CCN-derived values. The data file contains:

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

ccn

display_name: ccn

dmt-ccn-chamber

display_name: dmt-ccn-chamber

hygroscopicity

display_name: hygroscopicity

weissfluhjoch

display_name: weissfluhjoch


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

en: cryowrf topography antarctica landuse wrf

en: REMA topography and AntarcticaLC2000 for WRF

en:
Resource Title: Documentation
    Size: 2136909 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/886f1b73-459a-4d41-aca5-0af9dc86ae7b/resource/ddb02d97-7e22-4aca-ab56-29f57ef2ad8e/download/documentation_rema_antarcticalc2000.pdf
    Description: Documentation about how the provided dataset was produced including details to reproduce the dataset.

format: PDF

media_type: application/pdf

en:
Resource Title: Scripts
    Size: 24621 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/886f1b73-459a-4d41-aca5-0af9dc86ae7b/resource/97db282f-c028-4d1e-8bf9-6cda65aa5f7a/download/scripts.zip
    Description: Scripts to run the preprocessing to produce WRF binary tiles from REMA and AntarcticaLC2000 for topography and landuse (see Documentation for procedure).

format: ZIP

media_type: application/zip

en:
Resource Title: WPSinput
    Size: 15462767 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/886f1b73-459a-4d41-aca5-0af9dc86ae7b/resource/13f9afab-ef62-4454-965f-19b3e6288cc7/download/wpsinput.zip
    Description: Input files to run the Weather Research and Forecasting model (WRF) preprocessing system WPS with REMA topography and AntarcticaLC2000 landuse data (see Documentation for details).

antarctica

display_name: antarctica

cryowrf

display_name: cryowrf

landuse

display_name: landuse

topography

display_name: topography

wrf

display_name: wrf


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.

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

envidat

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

en: EnviDat: the Environmental Data Portal

en: Stability tests, avalanche observations, Switzerland, Norway

en:
Resource Title: Rutschblock data (Switzerland)
    Size: 221029 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/30e5287d-6bc2-4810-bdc2-422976023486/download/rb_data_used_in_manuscript.csv
    Description:

en:
Resource Title: ECT data (Norway, Switzerland)
    Size: 241792 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/7618bcce-8c14-463d-8c4d-1dc4f5365085/download/ect_data_used_in_manuscript.csv
    Description:

en:
Resource Title: avalanche occurrence data (Norway, Sitzerland) - largest avalanche size per day and warning region
    Size: 256120 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/accef652-f988-4d75-8aff-4c4c06c24a74/download/maxavalsize_used_in_manuscript.csv
    Description:

en:
Resource Title: avalanche occurrence data (Norway, Sitzerland) - avalanche sizes per day and warning region
    Size: 1514094 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/23823196-1d56-47d1-b6bd-cb0add4dbd7b/download/avalsize_used_in_manuscript.csv
    Description:

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:
To validate the critical crack length as implemented in the snow cover model SNOWPACK, PST experiments were conducted for three winter seasons (2015-2017) at two field site above Davos, Switzerland. This dataset contains manually observed snow profiles and stability tests. Furthermore, corresponding SNOWPACK simulations are included.
These data were analyzed and results were published in Richter et al. (2019).
Please refer to the Readme file for further details on the data.
These data are the basis of the following publication:
Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019.

en: avalanche-forecast snow-stability-evaluation avalanche snowpack snow

en: Validating and improving the critical crack length in SNOWPACK

envi

en: Environment

en:
Resource Title: SNOWPACK simulations
    Size: 21903741 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/6b0dc93f-34d1-48e2-bf69-3f9254e34692/download/snowpack.zip
    Description: This dataset contains all SNOWPACK simulations, including input files, setup and output files.

en:
Resource Title: README
    Size: 18771 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/b6cfb0c3-2bb3-4fa1-859d-5861983aa785/download/readme.txt
    Description: README file explaining data structure and content

media_type: text/plain

en:
Resource Title: Extracted data
    Size: 197562 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/42774021-73e4-49b2-a714-b192a0e3cc14/download/extracted_data.zip
    Description: Extracted data contain validation and results published in Richter et al. (2019).

en:
Resource Title: Manually observed snow profiles
    Size: 27333 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/74bf515b-cd6b-4e97-be6d-4356f0af6d3d/download/manual_snow_profiles.zip
    Description: Manually observed snow profiles as csv-files containing depth [cm], thickness [cm], density [kg m-3], hand hardness, first grain type, second grain type, first grain size [mm], and second grain size [mm] of each layer.

en:
Resource Title: Stability tests
    Size: 6975 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/98cbbb6e-bfad-4b71-b920-e3def95143d3/download/stability_tests.zip
    Description: Stability tests including csv-files with CT, ECT and PST experiments for each year and each field site.

avalanche

display_name: avalanche

avalanche-forecast

display_name: avalanche-forecast

snow

display_name: snow

snow-stability-evaluation

display_name: snow-stability-evaluation

snowpack

display_name: snowpack


en: The WFJ_ICE_LAYERS dataset contains multi-instrument snowpack measurements at high temporal resolution, which enable to monitor the formation of deep ice layers due to preferential water flow, at the Weissfluhjoch research site, Davos, Switzerland. It covers the winter 2016/2017, with a focus on the early melting season. This dataset includes traditional snowpack profiles (weekly resolution, 15/11/2016-29/05/2017), SnowMicroPen penetration resistance profiles (daily resolution, 01/02/2017-19/04/2017), snow temperatures measured at different heights in the snowpack (half-hourly resolution, 01/03/2017-15/04/2017) and the water front height derived from an upward-looking ground penetrating radar (3-hour resolution, 04/03/2017-08/04/2017). The measurements are complemented by initialization files for SNOWPACK model simulations with the ice reservoir parameterization at Weissfluhjoch for the winter 2016/2017.

en: snowmicropen weissfluhjoch ice-layers water-front snow-temperature snow-stratigraphy

en: WFJ_ICE_LAYERS: Multi-instrument data for monitoring deep ice layer formation in an alpine snowpack

en:
Resource Title: WFJ_ICE_LAYERS dataset
    Size: 4289873 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/2bf03a26-ee90-4e13-8ed0-63fbfada5733/resource/06794e9d-cbac-4a14-9d3b-a06f7f6c2061/download/wfj_ice_layers_dataset.zip
    Description:

en:
Resource Title: README
    Size: 476 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/2bf03a26-ee90-4e13-8ed0-63fbfada5733/resource/55d88599-ae7a-4ba6-af25-9544893de2be/download/readme.txt
    Description:

ice-layers

display_name: ice-layers

snow-stratigraphy

display_name: snow-stratigraphy

snow-temperature

display_name: snow-temperature

snowmicropen

display_name: snowmicropen

water-front

display_name: water-front

weissfluhjoch

display_name: weissfluhjoch


sensitivity-of-modeled-snow-instability

en: We investigated the sensitivity of modeled snow instability to meteorological input data using SNOWPACK. We therefore used input data from the automatic weather station at the Weissfluhjoch field site for the year 2016-2017. We investigated three scenarios and performed 14'000 simulations for each scenario.  The dataset contains extracted output data from modeled SNOWPACK simulations, including setup files to reproduce the simulations. For further information read the README file.

en: numerical-avalanche-forecasting snow-instability global-sensitivity-analysis snowpack snow-cover-modeling

envidat

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

en: EnviDat: the Environmental Data Portal

en: Sensitivity of modeled snow instability

title_for_slug: sensitivity-of-modeled-snow-instability

en:
Resource Title: Case ALL
    Size: 346790131 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/716fdeb9-b2e2-4613-bba3-691eff467c1b/resource/fd47ae3d-77a9-4f79-8c95-57b11de523bb/download/case_all.zip
    Description: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the whole simulation period. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en:
Resource Title: Case WL
    Size: 342942228 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/716fdeb9-b2e2-4613-bba3-691eff467c1b/resource/84ee8947-54aa-40d6-86bb-b6e0ffc14d25/download/case_wl.zip
    Description: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the period of weak layer formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en:
Resource Title: Case SL
    Size: 277264559 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/716fdeb9-b2e2-4613-bba3-691eff467c1b/resource/7635f568-e96c-4901-a385-c369150aaad6/download/case_sl.zip
    Description: Extracted output data for case SL. Here biases were introduced to meteorological input data during the period of slab formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en:
Resource Title: Manually observed snow profiles
    Size: 12725 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/716fdeb9-b2e2-4613-bba3-691eff467c1b/resource/5bd23cea-d4e3-4f87-8b0c-bfa3dc82afec/download/manual_profiles.zip
    Description: Manually observed snow profiles at Weissfluhjoch field site for winter season 2016-2017, including layer depth, hand hardness, grain size and grain shape for each layer.

en:
Resource Title: Reference run
    Size: 9909880 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/716fdeb9-b2e2-4613-bba3-691eff467c1b/resource/7eb0f753-02d2-4592-b822-cf91745ea196/download/reference.zip
    Description: Reference run for the sensitivity analysis at Weissfluhjoch field site for winter season 2016-2017. Meteorological data from the automatic weather stations are used. No biases were introduced to the input data.

global-sensitivity-analysis

display_name: global-sensitivity-analysis

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability


en:
This dataset provides distribution data of fungi in Switzerland of the National Data and Information Centre, called  [SwissFungi](https://swissfungi.wsl.ch/en/index.html). SwissFungi is a partner of [InfoSpecies](https://www.infospecies.ch/de/), the network of Swiss data and information centres for [fauna](http://www.cscf.ch/cscf/de/home.html), [flora](https://www.infoflora.ch/en/) and [fungi](https://swissfungi.wsl.ch/en/index.html). One of its main objectives is to document the spatial and temporal distribution of species in Switzerland.
The SwissFungi database currently contains more than 670'000 geo-referenced fungi observations, distributed throughout Switzerland. The oldest observations date back to 1770. A large portion of the records are from the last two decades of the last century to the present day. The database is continuously updated with new fungi records. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature.
The data from the distribution atlas of fungi in Switzerland are available for research and practice (nature conservation projects, environmental impact assessments etc.) and can be obtained via an [application form](https://www.infospecies.ch/de/assets/content/documents/Formular_Datenanfrage20190625.pdf). Please note the tariffs for data requests and submit your request directly to the [InfoSpecies](https://www.infospecies.ch/de/) office. Applications are usually answered within two working weeks. Details on the use of data are regulated in the current guidelines of the national data centers. Please note that the data center SwissFungi is not able to verify all incoming fungal records completely for a correct identification or coordinate errors and therefore cannot guarantee the correctness of the information.
License under [InfoSpecies](https://www.infospecies.ch/de/). Data is free of charge for research projects and available on request.

en: swissfungi biodiversity fungi records-database fungi-conservation national-data-center

en: SwissFungi - Records Database for the Fungi of Switzerland

envi

en: Environment

biodiversity

display_name: biodiversity

fungi

display_name: fungi

fungi-conservation

display_name: fungi-conservation

national-data-center

display_name: national-data-center

records-database

display_name: records-database

swissfungi

display_name: swissfungi


digitizing-historical-plague

en: We present newly digitized data on 6,929 plague outbreaks that occurred between 1347 and 1900 AD across Europe. The data base on an inventory initially published 1976. For georeferencing the information of Tele Atlas 2009 was used. The coordinates are in the reference systems ETRS89 and WGS84.

en: palaeo-epidemiology plague-outbreaks black-death yersinia-pestis historical-plague plague-mapping plague-inventory bubonic-plague

en: Digitizing historical plague

title_for_slug: digitizing-historical-plague

black-death

display_name: black-death

bubonic-plague

display_name: bubonic-plague

historical-plague

display_name: historical-plague

palaeo-epidemiology

display_name: palaeo-epidemiology

plague-inventory

display_name: plague-inventory

plague-mapping

display_name: plague-mapping

plague-outbreaks

display_name: plague-outbreaks

yersinia-pestis

display_name: yersinia-pestis


en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Alpthal in Switzerland which is located within a natural coniferous forest (ALB) with Norway spruce (_Picea abies_; 180-230 yrs) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Alpthal is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

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

en:
Resource Title: Data plot of the forest station (ALB)
    Size: 277476 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/3bb25386-f2d8-4fda-b7fa-86ea4be16535/resource/50d91991-2164-4f48-9bff-7dd1ecd9f379/download/albplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Forest station data (ALB)
    Size: 7164766 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb25386-f2d8-4fda-b7fa-86ea4be16535/resource/5109ff84-b671-41af-a243-34f1eda1fc88/download/albforest.csv
    Description:

en:
Resource Title: Site aerial photo (Alpthal)
    Size: 7983732 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/3bb25386-f2d8-4fda-b7fa-86ea4be16535/resource/c331a75a-6b89-4924-8896-6582b74c7284/download/alfront.jpg
    Description: Site aerial photo from the Alpthal forest station. Source: Google. 2016. Site aerial photo from the Alpthal forest station. Retrieved November 11, 2016, from https://www.google.ch/maps

format: jpeg

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed


en: 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 Celerina in Switzerland where one station is located within a natural coniferous forest stand (CLB) with Swiss pine (_Pinus cembra_; 210-250 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CLF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Celerina is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

en:
Resource Title: Site aerial photo (Celerina)
    Size: 9781364 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/eb7f96b4-7d02-4bf1-87c4-68fc8ced4453/resource/44d469c0-a4b8-4fc3-9708-2fa8d24c3d0f/download/clfront.jpg
    Description: Site aerial photo from both Celerina stations. Source: Google. 2016. Site aerial photo from both Celerina stations. Retrieved November 11, 2016, from https://www.google.ch/maps

en:
Resource Title: Data plot of the forest station (CLB)
    Size: 262914 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/eb7f96b4-7d02-4bf1-87c4-68fc8ced4453/resource/20445457-9718-4508-a507-4fed77aed50d/download/clbplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Data plot of the field station (CLF)
    Size: 10948330 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/eb7f96b4-7d02-4bf1-87c4-68fc8ced4453/resource/0e36dac4-16c5-47a7-ac12-430087b2db27/download/clffield.csv
    Description: Data file.

en:
Resource Title: Site photo of the field station (CLF)
    Size: 617034 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/eb7f96b4-7d02-4bf1-87c4-68fc8ced4453/resource/065216fc-60f4-4525-a432-ff0b2b2c6d89/download/clf.jpg
    Description: Site phote of the field station from the side.

en:
Resource Title: Data plot of the forest station (CLB)
    Size: 7469200 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/eb7f96b4-7d02-4bf1-87c4-68fc8ced4453/resource/ecdab18b-b895-4b9d-842d-9b884cac9fab/download/clbforest.csv
    Description: Data file.

en:
Resource Title: Data plot of the field station (CLF)
    Size: 324190 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/eb7f96b4-7d02-4bf1-87c4-68fc8ced4453/resource/8d220f28-48d8-48d5-9018-c5c5330cc260/download/clfplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Site photo of the forest station (CLB)
    Size: 9755793 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/eb7f96b4-7d02-4bf1-87c4-68fc8ced4453/resource/1898ed2e-cd7e-4b2d-9185-20e2b54f3e65/download/clb.jpg
    Description: Site photo of the forest station from below (viewing upwards the slope).


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).

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

en: Anthropogenic change and soil net N mineralization

envi

en: Environment

format: XLS

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


en:
This dataset contains all data on which the following publication below is based.
Paper Citation:
Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2
Please cite this paper together with the citation for the datafile.
We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/

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

global

display_name: global

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide


en: Laboratory experiments are presented on the phase change at the surface of sodium chloride – water mixtures at temperatures between 259 K and 240 K. High selectivity to the upper few nanometres of the frozen solution – air interface is achieved by using electron yield near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. We present the NEXAFS spectrum of the hydrohalite.

en: snow antarctica sea-salt-aerosol ice arctic

en: Data set on interfacial supercooling and the precipitation of hydrohalite in frozen NaCl solutions by X-ray absorption spectroscopy

en:
Resource Title: Fig 1D
    Size: 4831 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/b483a523-00ab-46ae-8dd4-12a80135158a/download/fig1datad.txt
    Description: Data to Figure 1D

en:
Resource Title: Fig 1F_2
    Size: 4855 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/016e32e9-3a1d-433e-95b8-66a0a8cef4ec/download/fig1dataf_phoenix04042016_016.txt
    Description: Individual data to mean shown in Figure 1F

en:
Resource Title: Fig 1B_2
    Size: 5115 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/ddfc21ed-441b-457d-b97f-b798769fe961/download/fig1datab_phoenix04042016_007.txt
    Description: Individual data to mean shown in Figure 1B

en:
Resource Title: Fig 1A
    Size: 5110 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/b90f9e5e-d4a1-47ef-8654-659b2ee7734a/download/fig1dataa.txt
    Description: Data Figure 1A

en:
Resource Title: Fig 1C
    Size: 4075 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/72da8863-8f75-4cc6-8f19-e0794aba1825/download/fig1datac.txt
    Description: Data to Figure 1C

en:
Resource Title: Fig 1B
    Size: 4055 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/e34c6b10-2a60-47ff-9861-c4532f9f6cd5/download/fig1datab.txt
    Description: Data in Figure 1B

en:
Resource Title: Fig 1F_1
    Size: 4856 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/927bbb3a-590d-46a1-8cc2-8eef6b2040a9/download/fig1dataf_phoenix04042016_015.txt
    Description: Individual data to mean shown in Figure 1F

en:
Resource Title: Table 2
    Size: 1399 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/0a3fb7b3-b96d-496a-86e2-00a635c1fdab/download/table2.txt
    Description: This table lists all NEXAFS data files in chronological order with PE carbon to oxygen signal intensity ratios -from which the atomic ratio were calculated in the paper - before ("preCtoO") and after ("postCtoO") each NEXAFS measurement. Also listed is the change in Na ("lossSodi"), O ("lossOxy"), Cl ("lossChlor"), and C ("lossCarb") signal intensity after the NEXAFS measurement compared to prior to the NEXAFS measurement.

en:
Resource Title: Table1
    Size: 15287 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/89079eb2-5bc0-4032-ad8d-41db87a93c81/download/table1.txt
    Description: This table lists all data files in chronological order. "SampleTemp" refers to the reading of the PT-1000 at the side of the sample holder. "cellPressure" is the water vapour pressure during the measurements. From this, in presence of ice, the temperature of the ice sample can be calculated which is given in the column "iceTemp". "dT" is the difference, or temperature offset, between sensor's reading and the ice samples temperature. The offset is due to the sensor being mounted away from the cooled spot of the sample were it experiences heating from warmer parts of the sample holder. The off-set is used to calibrate the temperature sensor and the resulting temperature during the experiments is given in the column "temperature". "relHum" is the relative humidity above (supercooled) liquid and "concBrine" is the concentration of a NaCl solution in mol/l in equilibrium with ice at the experimental temperature. "Area" is the integrated PE signal.

en:
Resource Title: Read Me
    Size: 127587 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/06ee35fe-4ee5-4813-812c-5600ac828b25/download/bartelsrausch_cryosphere_nacl_2020-data-repository.pdf
    Description: Description of the data tables

en:
Resource Title: Code
    Size: 4438 bytes
    Format: application/vnd.wolfram.mathematica.package
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/4f8edf22-d8cf-42e9-aae8-88cf6d84ceec/download/bartelsrausch_cryosphere_nacl_2020.m
    Description: Matlab code to read and plot the data

en:
Resource Title: Fig 1B_1
    Size: 4068 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/8b41e95e-ed56-46fd-96ff-1edfd7e95592/download/fig1datab_phoenix04042016_006.txt
    Description: Individual data to mean shown in Figure 1B

en:
Resource Title: Fib 1B_3
    Size: 4849 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/83a80299-55d3-423e-9fbb-e5b0a962dd5d/download/fig1datab_phoenix04042016_020.txt
    Description: Individual data to mean shown in Figure 1B

en:
Resource Title: Fig 1E
    Size: 4834 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/b5116736-f648-48fc-b151-84f9e7425b02/download/fig1datae.txt
    Description: Data to Figure 1E

en:
Resource Title: Fig 1F
    Size: 4852 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/4066e023-0d99-4ee8-9b28-7dcc5bae6e6a/download/fig1dataf.txt
    Description: Data to Figure 1F

antarctica

display_name: antarctica

arctic

display_name: arctic

ice

display_name: ice

sea-salt-aerosol

display_name: sea-salt-aerosol

snow

display_name: snow


en:
Hydrometerological and ecohydrological time series from Sagehen Creek and Independence Creek, Sierra Nevada, USA, illustrating hydrological responses to daily cycles in snowmelt and evapotranspiration forcing.
Data include 30-minute time series of
- weather variables,
- sap flow fluxes,
- groundwater levels (in two riparian transects of shallow groundwater wells),
- and stream stages (at 12 sites spanning a 500-meter elevation gradient),
and daily time series of
- temperature, precipitation, and snow water equivalent at three nearby snow telemetry stations
- diel cycle index values for groundwater levels and stream stages,
- and MODIS normalized difference snow index (NDSI) and enhanced vegetation index (EVI2) values averaged over selected subcatchments.
Google Earth Engine scripts for extracting the MODIS data are also provided.

en: diurnal-cycle ecohydrology streamflow sap-flow groundwater snowmelt evapotranspiration diel-cycle

envidat

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

en: EnviDat: the Environmental Data Portal

en: Daily cycles in solar flux, snowmelt, transpiration, groundwater, and streamflow at Sagehen and Independence Creeks, Sierra Nevada, USA

envi

en: Environment

en:
Resource Title: Subcatchment polygons
    Size: 24333 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/5b6edcd5-514b-4928-bf8c-5d1b475b59e9/download/google-earth-engine-subcatchment-polygons.txt
    Description: Subcatchment polygons for use in the Google Earth Engine scripts.

en:
Resource Title: MODIS EVI2 script
    Size: 5924 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/03332157-eed1-47a2-8eb9-15c4963f66cc/download/google-earth-engine-script-modis-evi2-averages.txt
    Description: Google Earth Engine script for computing MODIS two-band enhanced vegetation index (EVI2) values and averaging them over subcatchments.

en:
Resource Title: MODIS NDSI script
    Size: 5976 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/88a7c3f1-0623-435d-9e93-f9a463b0211d/download/google-earth-engine-script-modis-ndsi-averages-excluding-cloudy-pixels.txt
    Description: Google Earth Engine script for computing MODIS normalized difference snow index (NDSI) values and averaging them over subcatchment areas.

en:
Resource Title: 30-minute time series
    Size: 20691717 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/839acf4c-db88-4743-9de4-13859430facf/download/sagehen-and-independence-30min-data-for-archive.txt
    Description: 30-minute hydrometeorological and ecohydrological time series.

en:
Resource Title: Metadata
    Size: 150575 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/2376965d-33fc-4c95-842c-c788d5d22830/download/sagehen-and-independence-data-archive-metadata.pdf
    Description: Column descriptions for both data files, and map coordinates for all measurement locations.

en:
Resource Title: Daily time series
    Size: 758116 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/1273e282-0cf2-4ac1-8676-187bb027ccbe/download/sagehen-and-independence-daily-data-for-archive.txt
    Description: Daily hydrometeorological and ecohydrological time series

diel-cycle

display_name: diel-cycle

diurnal-cycle

display_name: diurnal-cycle

ecohydrology

display_name: ecohydrology

evapotranspiration

display_name: evapotranspiration

groundwater

display_name: groundwater

sap-flow

display_name: sap-flow

snowmelt

display_name: snowmelt

streamflow

display_name: streamflow


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)

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

en:
Resource Title: Predictors
    Size: 1022643006 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4ab13d14-6f96-41fd-96b0-b3ea45278b3d/resource/e0faab13-0d1b-492a-8539-5370d48b9e35/download/predictors.zip
    Description: Predictors generated and used in Descombes et al. (2020)

en:
Resource Title: Description
    Size: 15032 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/4ab13d14-6f96-41fd-96b0-b3ea45278b3d/resource/81c046c3-8d1d-45bc-a833-7d8240cebd12/download/predictors_description.xlsx
    Description: Description of the predictors provided in this dataset

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


radiosondes

en: Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.

en: wind-direction wind holoballoon relative-humidity radiosonde pressure temperature raclets

en: Radiosondes

title_for_slug: radiosondes

en:
Resource Title: Radiosond Template Files
    Size: 123564 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f6c38c8c-ab20-43a6-ae37-82117733df96/resource/4c57e9a6-e0b8-430d-964a-710306dadf78/download/template.zip
    Description: The "template" files contain standard parameters, i.e. ambient pressure (mbar and Pa), temperature (°C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), as well as additional information, e.g. time (UTC and local time), dew point (°C), mixing ration (g/kg), density (kg m³), altitude (m MSL), height above ground (ft and m AGL), and geographic coordinates (longitude, latitude). If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en:
Resource Title: Radiosond Sounding Files
    Size: 43199 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f6c38c8c-ab20-43a6-ae37-82117733df96/resource/969c41f7-d197-45cc-8db2-259fedd02b9f/download/soundings.zip
    Description: The "soundings" radiosond data contains information on ambient pressure (mbar), temperature (C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), and are ordered with respect to the height above ground (m AGL) with a step size of 20 m. The first line of each file idicates the geographic location and the start time (in UTC) of the ascent. If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

wind

display_name: wind

wind-direction

display_name: wind-direction


en:
This dataset contains all data on which the following publication below is based.
Paper Citation:
Risch, A.C., Frossard, A., Schütz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology
doi: ...
Methods
Study area and study sites
This study was conducted in YNP’s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163° N, 110.4169° W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995.
For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951).
The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter “days since kill”, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP’s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter “control”) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter “carcass”). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009).
Ecosystem functions and soil properties
We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 ºC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5 g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3–V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA).
Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length -300 bp for 16S and -200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively.
Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, Lüscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).
The other half of the soil sample was bagged in paper, dried to constant weight at 60°C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60°C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 – 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s.
In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18’’, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20°C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (CTScontrol - CTSfinal/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties.
Statistical analyses
Univariate analyses for ecosystem functions, soil biotic and abiotic properties
We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (“Location”), bison and elk (“Species”) and days since kill (“DSK”). For this purpose, we used linear mixed effect models (LMM, “nlme” package v 3.1 – 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the “lsmeans” package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk.
We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR - 0 indicates higher value at control compared to carcass, LRR - 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran’s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the “moran.test” function in the “spdep” package version 1.1-3 (Bivand et al., 2019).
Multivariate analyses
Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray–Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (“vegan” package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (“nlme” package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the “lsmeans” package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the “envfit” function in the “vegan” package (Oksanen et al., 2019).
Indicator species analyses were performed using the multipatt function implemented in the “indicspecies” package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De Cáceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p - 0.05, correlation coefficient - 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017).
References
Abarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. J., Eberhardt, U., Erland, S., … Kõljalg, U. (2010). The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytologist, 186(2), 281–285. doi:10.1111/j.1469-8137.2009.03160.x
Bengtsson-Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., & Nilsson, R. H. (2015). metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15(6), 1403–1414. doi:10.1111/1755-0998.12399
Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., … Nilsson, R. H. (2013). Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution, 4(10), 914–919. doi:10.1111/2041-210X.12073
Bivand, R., Altman, M., Anselin, L., Assuncao, R., Berke, O., Blanchet, G., … Yu, D. (2019). spdep: Spatial dependence, weighthing schemes, statistics. R package version 1.1-3.
Bump, J. K., Peterson, R. O., & Vucetich, J. A. (2009). Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology, 90(11), 3159–3167.
Bump, J. K., Webster, C. R., Vucetich, J. A., Peterson, R. O., Shields, J. M., & Powers, M. D. (2009). Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems, 12(6), 996–1007. doi:10.1007/s10021-009-9274-0
Danell, K., Berteaux, D., & Brathen, K. A. (2002). Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic, 55(4), 389392.
De Caceres, M., & Jansen, F. (2016). indicspecies: relationship between species and groups of species. R package version 1.7.6.
De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119(10), 1674–1684. doi:10.1111/j.1600-0706.2010.18334.x
Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996.
Edgar, R. C., & Flyvbjerg, H. (2015). Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics, 31(21), 3476–3482. doi:10.1093/bioinformatics/btv401
Frey, B., Niklaus, P. A., Kremer, J., Lüscher, P., & Zimmermann, S. (2011). Heavy-machinery traffic impacts methane emissions as well as methanogen abundance and community structure in oxic forest soils. Applied and Environmental Microbiology, 77(17), 6060–6068. doi:10.1128/AEM.05206-11
Frey, B., Rime, T., Phillips, M., Stierli, B., Hajdas, I., Widmer, F., & Hartmann, M. (2016). Microbial diversity in European alpine permafrost and active layers. FEMS Microbial Ecology, 92(3), fiw018.
Frossard, A., Donhauser, J., Mestrot, A., Gygax, S., Bååth, E., & Frey, B. (2018). Long- and short-term effects of mercury pollution on the soil microbiome. Soil Biology and Biochemistry, 120, 191–199. doi:https://doi.org/10.1016/j.soilbio.2018.01.028
Geremia, C., Wallen, R., & White, P. J. (2017). Status report of the Yellowstone bison population, September 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center for Resources.
Gobet, A., Quince, C., & Ramette, A. (2010). Multivariate cutoff level analysis (MultiCoLA) of large community data sets. Nucleic Acids Research, 38(15), e155–e155. doi:10.1093/nar/gkq545
Latter, P., & Howson, G. (1977). The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia, (17), 145–155.
Latter, P., & Walton, D. (1988). The cotton strip assay for cellulose decomposition studies in soil: history of the assay and development. In Cotton strip assay: an index for decomposition in soils (pp. 7–9). ITE Symposium, Institute of Terrestrial Ecology, Natural Environment Research Council, UK.
Lenth, R., & Love, J. (2018). lsmeans: least-squares means. R package version 2.27-62.
Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17(1), 10–12.
Meagher, M. M. (1973). The bison of Yellowstone National Park. NPS Scientific Monograph (Vol. 1). National Park Service, Yellowstone Center for Resources.
Melis, C., Selva, N., Teurlings, I., Skarpe, C., Linnell, J. D. C., & Andersen, R. (2007). Soil and vegetation nutrient response to bison carcasses in Białowieża Primeval Forest, Poland. Ecological Research, 22(5), 807–813. doi:10.1007/s11284-006-0321-4
Nikolenko, S. I., Korobeynikov, A. I., & Alekseyev, M. A. (2013). BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics, 14(1), S7. doi:10.1186/1471-2164-14-S1-S7
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., … Wagner, H. H. (2019). vegan: community ecology package. R package version 2.5-4.
Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2018). nlme: Linear and nonlinear mixed effect models. R package version 3.1-131.1.
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., … Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590–D596. doi:10.1093/nar/gks1219
Quimby, D. C., & Johnson, D. E. (1951). Weights and measurements of Rocky Mountain elk. Journal of Wildlife Management, 15, 57–62.
R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Zurich, Switzerland.
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., & Frey, B. (2015). Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Molecular Ecology, 24(5), 1091–1108. doi:10.1111/mec.13051
Risch, A. C., Jurgensen, M. F., & Frank, D. A. (2007). Effects of grazing and soil micro-climate on decomposition rates in a spatio-temporally heterogeneous grassland. Plant and Soil, 298(1–2), 191–201. doi:10.1007/s11104-007-9354-x
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., … Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–7541. doi:10.1128/AEM.01541-09
Smith, D., Stahler, D., Cassidy, K., Stahler, E., Metz, M., Cassidy, B., … Cato, E. (2018). Yellowstone National Park wolf project annual report 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center of Resources.
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. doi:10.1128/AEM.00062-07
Withington, C., & Sanford Jr., R. (2007). Decomposition rates of buried substances increase with altitude in a forest-alpine tundra ecotone. Soil Biology and Biochemistry, (39), 68–75.
Please cite this paper together with the citation for the datafile.

en: yellowstone soil-biodiversity carrion carcass biogeochemical-cycling detritus decomposition wolf fungi bacteria

envidat

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

en: EnviDat: the Environmental Data Portal

en: Elk and bison carcasses in Yellowstone, USA

bacteria

display_name: bacteria

biogeochemical-cycling

display_name: biogeochemical-cycling

carcass

display_name: carcass

carrion

display_name: carrion

decomposition

display_name: decomposition

detritus

display_name: detritus

fungi

display_name: fungi

soil-biodiversity

display_name: soil-biodiversity

wolf

display_name: wolf

yellowstone

display_name: yellowstone


en: The data base contains timeseries of stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK. One data set contains weekly stable water isotope data from the Lower Hafren and Tanllwyth catchments, and the other data set contains 7-hourly stable water isotope data from Upper Hafren. Both data sets also include chloride concentrations in precipitation and streamflow.

en: streamflow precipitation catchment plynlimon tracer deuterium stable-water-isotopes chloride

en: Stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK

envi

en: Environment

en:
Resource Title: Data Set Documentation
    Size: 442506 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/f4e1355d-d645-4419-bf89-67c70dd09dfc/resource/e4e4cc20-e46a-404c-88d4-963dce6a00b2/download/dataset_documentation__read_me_first.pdf
    Description: This file contains a documentation of the data sets, including references to other helpful documentations.

en:
Resource Title: Weekly stable water isotopes
    Size: 154294 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f4e1355d-d645-4419-bf89-67c70dd09dfc/resource/caa392e5-341e-412e-ba17-04b5ca2f9ee7/download/plynlimon_isotopes_and_chloride_weekly_2004_2009.txt
    Description: Data set of weekly stable water isotope data collected at Carreg Wen (precipitation) and Lower Hafren and Tanllwyth (streamflow).

en:
Resource Title: 7-hourly stable water istopes
    Size: 1068983 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f4e1355d-d645-4419-bf89-67c70dd09dfc/resource/b95e376b-5e72-4b1e-ae70-16a77fbe8b2c/download/plynlimon_isotopes_and_chloride_7hourly_2007_2009.txt
    Description: Data set of 7-hourly stable water isotope data measured at Carreg Wen (precipitation) and Upper Hafren (streamflow).

catchment

display_name: catchment

chloride

display_name: chloride

deuterium

display_name: deuterium

plynlimon

display_name: plynlimon

precipitation

display_name: precipitation

stable-water-isotopes

display_name: stable-water-isotopes

streamflow

display_name: streamflow

tracer

display_name: tracer


endsplit

en:
R scripts and demonstration data for end-member mixing and splitting: using isotopes and other tracers to determine where streamflow comes from (end-member mixing) and where precipitation goes (end-member splitting).
 This package includes two R scripts:
"EndSplit_v1.0_20200516.R" implements end-member mixing and splitting.
"EndSplit_demo_v1.0_20200516.R" demonstrates the application of EndSplit to the Hubbard Brook Watershed 3 isotope data set (see below).
Both of these scripts are copyright (C) 2020 ETH Zurich and James Kirchner.  Public use is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/
But… READ THIS CAREFULLY:
ETH Zurich and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose.  Users are solely responsible for determining the suitability and reliability of this software for their own purposes.
These scripts implement end-member mixing and end-member splitting, as described in Kirchner and Allen, "Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis", Hydrology and Earth System Sciences, 24, 17-39, https://doi.org/10.5194/hess-24-17-2020, 2020.  Users publishing results based on these scripts should cite that paper.
Build 2020.05.16 is a minor bug fix of build 2019.10.25, which was previously released as EndSplit_v1.0_20191025.R.
 The zip file "demonstration input data.zip" contains 8 demonstration data files (all tab-delimited text):
"Hubbard Brook WS3 isotope data split by sampling date.txt" contains streamflow and precipitation isotope data from Hubbard Brook Watershed 3 isotope data (Campbell and Green, 2019).
"Hubbard Brook WS3 daily P and Q 1956-2014.txt" contains daily precipitation and streamflow totals for Hubbard Brook Watershed 3.  (USDA Forest Service Northern Research Station, 2016a and 2016b).
"Hubbard Brook WS3 isotope data WY2007.txt", "Hubbard Brook WS3 isotope data WY2008.txt", "Hubbard Brook WS3 isotope data WY2009.txt", "Hubbard Brook WS3 daily P and Q WY2007.txt", "Hubbard Brook WS3 daily P and Q WY2008.txt", and "Hubbard Brook WS3 daily P and Q WY2009.txt" contain subsets of these data for the designated water years.
As the work product of US federal employees, the data in these files are in the public domain.  However, any users of these data should cite the original sources:
Campbell, J. L., and Green, M. B.: Water isotope samples from Watershed 3 at Hubbard Brook Experimental Forest, 2006-2010, https://doi.org/10.6073/pasta/f5740876b68ec42b695c39d8ad790cee, 2019.
USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Daily Streamflow by Watershed, 1956 - present, https://doi.org/10.6073/pasta/38b11ee7531f6467bf59b6f7a4d9012b, 2016a.
USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Total Daily Precipitation by Watershed, 1956 - present, https://doi.org/10.6073/pasta/163e416fb108862dc6eb857360fa9c90, 2016b.
 The zip file "demonstration output files.zip" contains demonstration output files
These tab-delimited text files were generated by running EndSplit_demo_v1.0_20200516.R (which in turn calls EndSplit_v1.0_20200516.R) under R version 3.6.0, using the input files contained in "demonstration input data.zip"

en: tracer end-member-mixing end-member-splitting streamflow precipitation evapotranspiration isotopes

en: EndSplit

title_for_slug: endsplit

en:
Resource Title: demonstration input data
    Size: 387131 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b12fafab-7730-4648-a8bc-6afb1944bf3b/resource/00207c87-63c7-4c9d-86c9-4873bf699035/download/demonstration-input-data.zip
    Description: # The zip file "demonstration input data.zip" contains 8 demonstration data files (all tab-delimited text): #

"Hubbard Brook WS3 isotope data split by sampling date.txt" contains streamflow and precipitation isotope data from Hubbard Brook Watershed 3 isotope data (Campbell and Green, 2019).

"Hubbard Brook WS3 daily P and Q 1956-2014.txt" contains daily precipitation and streamflow totals for Hubbard Brook Watershed 3.  (USDA Forest Service Northern Research Station, 2016a and 2016b).

"Hubbard Brook WS3 isotope data WY2007.txt", "Hubbard Brook WS3 isotope data WY2008.txt", "Hubbard Brook WS3 isotope data WY2009.txt", "Hubbard Brook WS3 daily P and Q WY2007.txt", "Hubbard Brook WS3 daily P and Q WY2008.txt", and "Hubbard Brook WS3 daily P and Q WY2009.txt" contain subsets of these data for the designated water years.

As the work product of US federal employees, the data in these files are in the public domain.  However, any users of these data should cite the original sources:

Campbell, J. L., and Green, M. B.: Water isotope samples from Watershed 3 at Hubbard Brook Experimental Forest, 2006-2010, https://doi.org/10.6073/pasta/f5740876b68ec42b695c39d8ad790cee, 2019.

USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Daily Streamflow by Watershed, 1956 - present, https://doi.org/10.6073/pasta/38b11ee7531f6467bf59b6f7a4d9012b, 2016a.

USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Total Daily Precipitation by Watershed, 1956 - present, https://doi.org/10.6073/pasta/163e416fb108862dc6eb857360fa9c90, 2016b.

en:
Resource Title: EndSplit version 1.0 build 2020.05.16
    Size: 18979 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/b12fafab-7730-4648-a8bc-6afb1944bf3b/resource/bdc05c4b-dd74-4868-b1fb-08cb26107158/download/endsplit_v1.0_20200516.r
    Description: EndSplit_v1.0_20200516.R implements end-member mixing and splitting.

This script is copyright (C) 2020 ETH Zurich and James Kirchner.  Public use is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

But… READ THIS CAREFULLY:
ETH Zurich and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose.  Users are solely responsible for determining the suitability and reliability of this software for their own purposes.

This script implements end-member mixing and end-member splitting, as described in Kirchner and Allen, "Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis", Hydrology and Earth System Sciences, 24, 17-39, https://doi.org/10.5194/hess-24-17-2020, 2020.  Users publishing results based on these scripts should cite that paper.

en:
Resource Title: EndSplit_demo version 1.0 build 2020.05.16
    Size: 14802 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/b12fafab-7730-4648-a8bc-6afb1944bf3b/resource/19e05849-5bd7-4d0e-9c90-98f9f2d78435/download/endsplit_demo_v1.0_20200516.r
    Description: EndSplit_demo_v1.0_20200516.R demonstrates the application of EndSplit to the Hubbard Brook Watershed 3 isotope data set.  It calls EndSplit_v1.0_20200516.R.

Both of these scripts are copyright (C) 2020 ETH Zurich and James Kirchner.  Public use is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

But… READ THIS CAREFULLY:
ETH Zurich and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose.  Users are solely responsible for determining the suitability and reliability of this software for their own purposes.

These scripts implement end-member mixing and end-member splitting, as described in Kirchner and Allen, "Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis", Hydrology and Earth System Sciences, 24, 17-39, https://doi.org/10.5194/hess-24-17-2020, 2020.  Users publishing results based on these scripts should cite that paper.

en:
Resource Title: demonstration output files
    Size: 14152 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b12fafab-7730-4648-a8bc-6afb1944bf3b/resource/02360e0a-ceec-4bc8-968e-1b17a5a7ad1e/download/demonstration-output-files.zip
    Description: These tab-delimited text files were generated by running EndSplit_demo_v1.0_20200516.R (which in turn calls EndSplit_v1.0_20200516.R) under R version 3.6.0, using the input files contained in "demonstration input data.zip"

end-member-mixing

display_name: end-member-mixing

end-member-splitting

display_name: end-member-splitting

evapotranspiration

display_name: evapotranspiration

isotopes

display_name: isotopes