en: snow-density microstructure antarctica snowmicropen snow-density-profile

en: Manual snow pits and other snow density measurements

en: List of SnowMicroPen measurements

en: List of all SnowMicroPen measurements, including date and location, and processing status. This list has been generated by the python code (see other resources in this dataset).

en: Jupyter notebook for processing the SnowMicroPen files.

en: SnowMicroPen pts files and processed txt files

en: Zip archive containing the acquired *.pts files (binary files recorded by the SnowMicroPen), and the converted *.txt files using the SMP.ipynb jupyter notebook (see the python code in the other resources in this dataset)

en: Documentation SnowMicroPen measurements

en: This *.zip archive contains all available documentation of the SnowMicroPen measurements, including the mapping of the SMP measurements in the terrestrial laser scan field at PEA (see documentation for 20161220) and at the Hammarryggen Ice Rise (see documentation for 20190104 and 20190111).

en: Python code exported from the Jupyter notebook for processing the SnowMicroPen files

en: SnowMicroPen (SMP) measurements and manual snowpits from Dronning Maud Land, East Antarctica. Measurements were taken in the vicinity of the Belgium Princess Elisabeth Station (PEA), in a transect towards the coast, and on the Lokeryggen and Hammarryggen Ice Rises near the coast. Measurements were taken during 3 individual campaigns in the 2016-2017, 2018-2019 and 2019-2020 field seasons.

antarctica

display_name: antarctica

microstructure

display_name: microstructure

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: SnowMicroPen measurements and manual snowpits from Dronning Maud Land, East Antarctica


maintainer: EnviDat Support

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

state: active

type: dataset

en: CORE Dataset (80 GB)

format: SERVICE

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

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

media_type: text/html

format: HTML

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

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

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

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

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

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

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

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

CORE Specifications:

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

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

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

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

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

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

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

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

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

Major issues to be solved:

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

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

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

Example

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

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

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

core

display_name: core

geodata

display_name: geodata

gridded-data

display_name: gridded-data

raster

display_name: raster

research-data-management

display_name: research-data-management

time-series

display_name: time-series

video-encoding

display_name: video-encoding

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester


en: dispersal biodiversity reef-fish ecology traits

en: Tropical reef fish trait data

media_type: text/csv

en:
Species trait (i.e. adult body size, home range mobility behaviour, circadian activity and schooling) data gathered from the General Approach to Species Abundance Relationships (GASPAR) database (Kulbicki et al. 2013, Parravicini et al. 2013).

Kulbicki, M. et al. 2013. Global biogeography of reef shes: a hier- archical quantitative delineation of regions. – PLoS One 8: e81847.

Parravicini, V. et al. 2013. Global patterns and predictors of tropi- cal reef sh species richness. – Ecography 36: 1254–1262.

To complement the trait data with Pelagic Larval Duration information refer to the following publication:

Luiz, Osmar J., et al. "Adult and larval traits as determinants of geographic range size among tropical reef fishes." Proceedings of the National Academy of Sciences 110.41 (2013): 16498-16502.

In Donati et al. "A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography in August 2019, we used the PLD gathered from Luiz et al. 2013 as described in the methods - section "Species trait data".

format: CSV

mimetype: text/csv

en: Main text figures

en: R Script used for figures in main text in: Donati et al. 2019: "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019.

en: Global simulated distribution of species under a range of dispersal parameter values

media_type: text/plain

en:
Global distribution of simulated species richness under a range of dispersal parameter values d
d ∈ [1.75°: 5.45°].

format: TXT

mimetype: text/plain

en: Supplementary material Appendix 1 Figures illustrated in Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages". accepted in Ecography, August 2019

en: Donati et al. main script

en:
Main script used in : Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages"
accepted in Ecography, August 2019

en: Dispersal parameter value d per simulation starting with a minimum dispersal value, where each species is able to colonise each adjacent cell, and ending with the value of d for which all the extant habitats, including the most remote Pacific islands, have been colonised (d ∈ [1.75°: 5.45°]).

en: Simulated rates of speciation and extinction under a range of dispersal parameters. Net diversification rates are calculated from the difference between speciation and extinction rates. This data allows the creation of Figure A4 in Supporting information, Appendix 1.

en: Input for the Global distribution maps for proportions of species richness for (a) simulated dispersal parameter value (b) observed dispersal-related traits

en: Geographic distribution data (species richness and proportions of high/low dispersal-related traits) on predominantly low dispersing and high dispersing species expected based on the simulations, and those where low- and high- dispersal related traits values are observed for the five investigated traits.

en: Input for spatial congruence test overlap between proportions of low and high dispersal and observed low- and high- dispersal-related traits in assemblages (30-50% threshold). see Table 2.

media_type: application/zip

en: Global coast files from https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html used in the Figures

format: ZIP

mimetype: application/zip

en: Functions used in R scripts in: Donati et al. 2019 "A process-based-model supports an association between  # dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019

en: Functions for Figures illustrated in Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019.

en: Global species distribution of tropical reef fish species

en: Each species corresponds to a "Gaspar_code" gathered from the General Approach to Species Abundance Relationships (GASPAR) database (Kulbicki et al. 2013, Parravicini et al. 2013) and is linked to a specific Fishned ID corresponding to a specific Latitude and Longitude.

en: Coral reef grid cells (shape, dbf, etc. files)

en:
Grid cells of 5 × 5°, corresponding to circa 555 × 555 km at the Equator (see Parravicini et al. 2013, Leprieur et al. 2016)

Parravicini, V. et al. 2013. Global patterns and predictors of tropi- cal reef sh species richness. – Ecography 36: 1254–1262.

Leprieur, F. et al. 2016. Plate tectonics drive tropical reef biodiver- sity dynamics. – Nat. Commun. 7: 11461.

en:
This dataset contains all data and R codes (R Development Core Team, https://www.R-project.org) used in the following publication: Donati GFA, Parravicini V, Leprieur F, Hagen O, Gaboriau T, Heine C, Kulbicki M, Rolland J, Salamin N, Albouy C, Pellissier L. "A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted by Ecography in August 2019. When using this data and R scripts the above publication should be cited.

The interaction of habitat dynamics with species dispersal abilities could generate gradients
in species diversity and prevalence of life-history and ecological traits, when the latter are associated with dispersal potential. In this dataset, we use a spatial mechanistic model of speciation, extinction and dispersal, constrained by a dispersal parameter. This model allows to simulate the interplay between reef habitat dynamics over the past 140 million years and dispersal, shaping lineage diversification history and global assemblage composition of over 6000 tropical reef fish species. Global trait distribution data of tropical reef fish are used to evaluate the congruence between simulations and observations.

biodiversity

display_name: biodiversity

dispersal

display_name: dispersal

ecology

display_name: ecology

reef-fish

display_name: reef-fish

traits

display_name: traits

en: Simulated and observed prevalence of dispersal-related traits in tropical reef fish assemblages worldwide


maintainer: Franziska Gerber

en: topography landuse static-data wrf cosmo-wrf cryowrf

en: Static data for WRF - Soiltype bottom

en: Documentation

en: Documentation of the dataset

en: Example namelist.wps to use with the 1s static data

en: Static data for WRF - Topography

en: Static data for WRF - Soiltype top

en:
Example GEOGRID.TBL to use with the 1s static data;
Note about naming:
* 121 -> 1-2-1 topography smoothing applied to dataset
* 1 -> one smoothing cycle applied to the data (needs to be changed depending on the chosen resolution to avoid too steep slope angles)

en: Static data for WRF - Landuse

en: Static input data (topography, landuse and soiltype) for the WRF preprocessing system WPS is provided for Switzerland and its neighboring countries between 45-49 N and 4-12 E. The data is provided at a resolution of 1 s. Topography is based on the Aster dataset, while landuse is extracted from the Corine landuse dataset. Soil type is set to silty clay loam for the entire domain. This static input data is valid for WRF and CRYOWRF.

cosmo-wrf

display_name: cosmo-wrf

cryowrf

display_name: cryowrf

landuse

display_name: landuse

static-data

display_name: static-data

topography

display_name: topography

wrf

display_name: wrf

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: High resolution static data for WRF over Switzerland


maintainer: James W. Kirchner

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

en: Data Set Documentation

media_type: application/pdf

en: This file contains a documentation of the data sets, including references to other helpful documentations.

format: PDF

mimetype: application/pdf

en: 7-hourly stable water istopes

en: Data set of 7-hourly stable water isotope data measured at Carreg Wen (precipitation) and Upper Hafren (streamflow).

en: Weekly stable water isotopes

en: Data set of weekly stable water isotope data collected at Carreg Wen (precipitation) and Lower Hafren and Tanllwyth (streamflow).

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.

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

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


en: gemstock meteo-station shortwave-radiation longwave-radiation snow-height

en: DATA ACCESS

en:
Meteo data at Gemsstock [Level 2 dataset](http://models.slf.ch/p/dataset-processing/).

fields      = timestamp TSS HS VW DW VW_MAX OSWR ISWR ILWR RECORD AUX_VOLTAGE LOGGER_VOLTAGE LW_SENSOR_TEMP VENTILATION_FLAG OLWR

en:
Meteorological station at Gemstock (3021 m asl) in Canton Uri. The station includes in/out LW/SW and a snow height sensor.

Data from this station is managed by the permos.ch project. More information: https://www.permos.ch/permafrost-monitoring/field-sites

gemstock

display_name: gemstock

longwave-radiation

display_name: longwave-radiation

meteo-station

display_name: meteo-station

shortwave-radiation

display_name: shortwave-radiation

snow-height

display_name: snow-height

en: GEM2: Meteorological and snow station at Gemsstock (3021 m asl), Canton Uri, Switzerland


maintainer: Matthias Haeni

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

en: Data plot of the field station (NAF)

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

en: Site photo of the forest station (NAB)

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

mimetype: image/jpeg

en: Data plot of the forest station (NAB)

en: Field station data (NAF)

en: Data file.

en: Site aerial photo (Nationalpark)

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

en: Forest station data (NAB)

en: Site photo of the field station (NAF)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Nationalpark in Switzerland where one station is located within a natural coniferous forest stand (NAB) with mountain pine (_Pinus mugo_; 210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Nationalpark is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


maintainer: Benjamin Reuter

en: snowpack snow-cover-modeling avalanche-problem-type snow avalanche-activity snow-instability avalanche avalanche-danger crocus weissfluhjoch

en: 1999-2017 Avalanche problem types from SNOWPACK

en:
Avalanche problem types were derived from snow cover simulations with the model SNOWPACK at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are shown in Figure 8 in Reuter et al. (2021).

en: 2015-2016 Seismic avalanche acitivity catalogue

en:
Avalanches were indentified in seismic data recorded by two seismic arrays, one deployed at Wannengrat (2,500 m) and another one deployed in the Dischma valley (2,000 m).
The data cover the period from 01.01.2016 - 30.04.2016 and are presented in Figure 7 in Reuter et al. (2021).

en: 2015-2016 Avalanche problem types from Crocus

en:
Avalanche problem types were derived from a snow cover simulation with the model Crocus for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

en: 1999-2017 Avalanche problem types from Crocus

en:
Avalanche problem types were derived from snow cover simulations with the model Crocus at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are presented in Figure 8 in Reuter et al. (2021).

en: 2015-2016 Avalanche problem types from SNOWPACK

en:
Avalanche problem types were derived from a snow cover simulation with the model SNOWAPCK for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

en:
Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016.
Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

avalanche-danger

display_name: avalanche-danger

avalanche-problem-type

display_name: avalanche-problem-type

crocus

display_name: crocus

snow

display_name: snow

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

weissfluhjoch

display_name: weissfluhjoch

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: Simulated avalanche problem types and seismic avalanche activity around Weissfluhjoch


en: snow monitoring avalanche wind-drifted-snow low-cost lidar

en:
We developed a monitoring system using low-cost lidar (Livox Avia) and optical sensors, to measure snow depth variations in an avalanche release area at a high spatiotemporal scale (centimeter to low decimeter spatial resolution and hourly temporal resolution). A detailed description of the setup and a first data analysis is published in:
[Ruttner-Jansen, P., Voordendag, A., Hartmann, T., Glaus, J., Wieser, A., & Bühler, Y. (2024). Monitoring snow depth variations in an avalanche release area using low cost lidar and optical sensors. EGUsphere, 2024, 1-20.](https://egusphere.copernicus.org/preprints/2024/egusphere-2024-744/)
This dataset contains hourly 3D lidar data of the snow surface at the test site Braemabuel in Davos, Switzerland from the winter season 2023-2024 (23.11.2023 - 14.04.2024). Braema1 is installed at 2191 m a.s.l. in the slope, looking up into the avalache release area, and Braema2 is installed at 2255 m a.s.l at the ridge, looking down. Both scans together cover an area of about 15000 m².
The data is in format ".las" and contains the points in XYZ [m], in the scanner own coordinate system
- scalar fields: intensity, return number, timestamp, tag, label
-- tag: binary code from Livox (see [Livox Avia Manual](https://terra-1-g.djicdn.com/65c028cd298f4669a7f0e40e50ba1131/Download/Avia/Livox%20Avia%20User%20Manual%20202204.pdf))
-- label: marking outliers (filtered by distance and SOR to get snow particles in the air)

lidar

display_name: lidar

low-cost

display_name: low-cost

monitoring

display_name: monitoring

wind-drifted-snow

display_name: wind-drifted-snow

educ

en: Education, culture and sport

en: Snow depth mapping by lidar station Braemabuel


en: single-tree forest-inventory lidar tree-volume terrestiral-laserscanning biomass

en: Individual tree parameters and volume estimates based on TLS point clouds and allometric models

en:
"ASM" denotes volume estimates calculated by allometric scaling models (as used in Swiss NFI), based on tree height, DBH and D7.
"QSM" denotes volume estimates derived from TLS point clouds by quantitative structure models (QSM).
The remaining variables are geometrical features directly calculated from single tree TLS point clouds.
For more detailed variable descriptions see related publication.

en:
 Dataset
This dataset is based on terrestrial laser scanning (TLS) data acquired during winter 2020/2021 in leaf-off conditions, with a Leica BLK 360 instrument following a tree-centric scanning pattern.
The data was acquired on two sites (47.42°N 8.49°E and 47.504°N, 7.78°E), both of which were managed mixed temperate forest stands.
Individual trees were semi-automatically segmented from the co-registered TLS point clouds.

 Background
Accurate estimates of individual tree volume or biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data.
Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, with existing approaches ranging from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories.
In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM).
Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total tree, stem, coarse wood and fine branch volume.

biomass

display_name: biomass

forest-inventory

display_name: forest-inventory

single-tree

display_name: single-tree

terrestiral-laserscanning

display_name: terrestiral-laserscanning

tree-volume

display_name: tree-volume

envidat

en: EnviDat: the Environmental Data Portal

en: Individual tree TLS point clouds for tree volume estimation


latent-heat-flux-over-snow

title_for_slug: latent-heat-flux-over-snow

en: meteo-station snow meta central-asia swiss-alps meteo

en:
This file includes the Jupyter notebooks to reproduce the method and the figures of the paper. This consists in:
- 3 files for Alpine Valley
- 3 files for Antarctic Plateau
- 1 file for application at Pamir mountains

en:
This file includes the datasets used to compare the methods. It includes:
- EC Postprocessed heat flux data from sonic anemometer and gas analyzer. Files named: [SITE]_(NO)WatVap_eddypro_OUT_[type].csv
- MOST Postprocessed data from SNOWPACK model. Files named [SITE]_[year]_[METHOD].smet
- Configuration files for SNOWPACK (.ini files) and Eddypro (.eddypro files)

[SITE] correspond to DISHMA/DISH or Alpine Valley, PLATEAU/PLAT for Antarctic Plateau, TAJ for Pamir mountains (no EC)
[type] correspond to fluxnet, full_output or qc_details
[METHOD] indicates the stability model used and/or the roughness length

en:
This dataset contains three case studies where are compared the latent heat flux calculated using the Eddy Covariance (EC) method, the Monin-Obukhov Similarity Theory (MOST) and the modified Bowen-ratio method presented in the research paper "Using the sensible heat flux eddy covariance based exchange coefficient to calculate latent heat from moisture mean gradients overn snow". We include the datasets, the code and the ini files for reproduce the research.
The dataset contain two files in zip format:
 1. Sowftware-Heat_fluxes_on_snow: Jupyter notebooks to reproduce the method and the figures of the paper.
 2. data.zip: Datasets used to compare the methods

central-asia

display_name: central-asia

meta

display_name: meta

meteo

display_name: meteo

meteo-station

display_name: meteo-station

swiss-alps

display_name: swiss-alps

en: Latent Heat Flux over Snow


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

en: Community matrix (all beetles)

en: Abundances of all beetle species for each branch bundle

format: XLS

en: Forest and environmental variables

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

en: Variable description

en: Detailed description of the forest and environmental variables

en: Community matrix (saproxylic beetles)

en: Abundances of saproxylic beetle species for each branch bundle

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

biodiversity

display_name: biodiversity

colonisation

display_name: colonisation

conservation

display_name: conservation

dead-wood

display_name: dead-wood

forest-management

display_name: forest-management

habitat-availability

display_name: habitat-availability

habitat-heterogeneity

display_name: habitat-heterogeneity

saproxylic-beetles

display_name: saproxylic-beetles

sloss

display_name: sloss

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


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

en: Data plot of the forest station (BAB)

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

en: Data plot of the field station (BAF)

en: Site aerial photo (Beatenberg)

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

en: Site photo of the field station (BAF)

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

en: Forest station data (BAB)

en: Data file.

en: Site photo of the forest station (BAB)

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

en: Field station data (BAF)

en:
High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Beatenberg in Switzerland where one station is located within a natural coniferous forest (BAB) with Norway spruce (_Picea abies_; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Beatenberg is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the
Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

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


maintainer: Marcus Schaub

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

en: Site photo 2

en: Dataset

en: Swiss Forest Health Inventory

mimetype: text/html

en: Shapefile	of measurement location

en: Long-term Irrigation	Experiment Pfynwald homepage

en: Site photo 1

en: Site photo 3

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

crown-condition

display_name: crown-condition

dbh

display_name: dbh

diameter-at-breast-height

display_name: diameter-at-breast-height

drought

display_name: drought

forest

display_name: forest

irrigation

display_name: irrigation

lwf

display_name: lwf

scots-pine

display_name: scots-pine

tree

display_name: tree

tree-species

display_name: tree-species

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


maintainer: Thomas Wohlgemuth

en: pairwise-stand-comparisons soil-impacts forest-management biogeography biodiversity

en: Baseline data

en: headers

en: Description of headers for data_wohlgemuth_etal_Neobiota_2022

en: references

en: Bibliography of all references cited in data_wohlgemuth_etal_Neobiota_2022.

en: Compiled data on the impacts of seven important NNTs (Acacia dealbata, Ailanthus altissima, Eucalyptus globulus, Prunus serotina, Pseudotsuga menziesii, Quercus rubra, Robinia pseudoacacia) on physical and chemical soil and biodiversity in Europe, and summarise commonalities and differences. A total of 107 publications considered, studies referred to biodiversity attributes and soil properties: 2804 lines and 30 rows.

biodiversity

display_name: biodiversity

biogeography

display_name: biogeography

forest-management

display_name: forest-management

pairwise-stand-comparisons

display_name: pairwise-stand-comparisons

soil-impacts

display_name: soil-impacts

en: Impact of non-native tree species in Europe on soil properties and biodiversity: a review


maintainer: Rebecca Mott

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

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

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

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

en: Gletschboden setup

media_type: image/png

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

format: PNG

mimetype: image/png

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

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

en: Output explanation

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

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

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

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

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

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

dischmex

display_name: dischmex

eddy-covariance-measurements

display_name: eddy-covariance-measurements

patchy-snow-covers

display_name: patchy-snow-covers

snow-patch

display_name: snow-patch

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


disdrometer-data-laret

maintainer: Daniel Wolfensberger

title_for_slug: disdrometer-data-laret

en: precipitation hydrometeors raclets disdrometer visibility

en: Disdrometer Data Laret

en: The zip folder contains one netCDF file per day for Laret from February 7 to March 29 2019 with a temporal resolution of 30 seconds (time in UTC). The naming convention for the files is Laret_YYYYMMDD.nc and the files include the following: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

en:
A laser optical disdrometer (Parsivel² , OTT Hydromet) was used to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms.
The dataset contains information on precipitation amount and type for the period of February 7 to March 29 2019 in Laret.

disdrometer

display_name: disdrometer

hydrometeors

display_name: hydrometeors

precipitation

display_name: precipitation

raclets

display_name: raclets

visibility

display_name: visibility

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

en: Shapefile of 307 catchments in Switzerland

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

en: Soil moisture

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

en: Discharge

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

en: Precipitation

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

en: Low-flow storage

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

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

discharge

display_name: discharge

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

prevah

display_name: prevah

soil-moisture

display_name: soil-moisture

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


mountain-permafrost-hydrology

maintainer: Jeannette Noetzli

title_for_slug: mountain-permafrost-hydrology

en: permafrost-degradation mountain-permafrost-hydrology mass-movements climate-change-impacts monitoring-techniques ice-rich-permafrost

en: Synthesis report chapter of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN)

en: This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). In earlier reports such as the CH2014-IMPACTS report (CH-Impacts 2014), the topic of mountain permafrost hydrology was not addressed. Here, we provide a baseline of the available knowledge of mountain permafrost in the Swiss Alps for future reference. We compile an overview of the current understanding of mountain permafrost in the Swiss Alps, its distribution and characteristics, observed and projected changes, and expected impacts on slope stability, infrastructure and hydrological aspects. We also briefly describe the measurement techniques and modelling approaches applied. The chapter closes with a summary of the most important open research questions. The literature cited mainly includes studies on mountain permafrost published in scientific journals and assessments of long-term observation data. We focus on permafrost hydrology interactions wherever information is available. However, systematic studies on permafrost hydrology in mountain areas are still limited.

climate-change-impacts

display_name: climate-change-impacts

ice-rich-permafrost

display_name: ice-rich-permafrost

mass-movements

display_name: mass-movements

monitoring-techniques

display_name: monitoring-techniques

mountain-permafrost-hydrology

display_name: mountain-permafrost-hydrology

permafrost-degradation

display_name: permafrost-degradation

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Mountain Permafrost Hydrology


en: raster-processing qgis-plugin seilaplan cable-yarding geodata-processing swiss-geodata geoinformation-system digital-elevation-model open-source digital-planning

en: DHM-Kacheln zusammenfügen

en: View video on youtube!

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. In this tutorial video, we show how to merge multiple DTM raster tiles into one file, using the QGIS tool ‘Virtual Raster’. This simplifies the digital planning of a cable line using the QGIS plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to Seilaplan website: https://seilaplan.wsl.ch

***************************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. In diesem Tutorialvideo zeigen wir, wie man mit dem QGIS-Plugin Virtuelles Raster mehrere DHM-Kacheln zu einem einzigen Rasterfile zusammenfügen und abspeichern kann. Für die Seillinienplanung mit Seilaplan muss nun nur noch eine Datei, mein neues virtuelles Raster, ausgewählt werden.

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geodata-processing

display_name: geodata-processing

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

raster-processing

display_name: raster-processing

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

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: Seilaplan Tutorial: Merge DTM tiles


maintainer: Yaquan Chang

en: habitat-heterogeneity hypervolume model-residuals phytodiversity hengduan-mountains

en: Eurasia SR and heteorgeneity

en: This table is used for the Eurasia scale analyses, including both family level species richness map and different heterogeneity predictors

en: Hengduan plant family richness map

en: This is the family level plant richness map in the Hengduan mountain scale at 1km resoution

en: Eurasia plant family richness map

en: This is the plant family level richness map at the Eurasia scale calculated from https://www.nature.com/articles/s41597-021-00997-6

en: HDM heterogeneity scales

en: The Heterogeneity matrics in the Hengduan mountain scale

en: Plant range map at Hengduan scale

en: This is the plant range map at the Hengduan mountain scale based on species distribution model, elevation, and county level distribution information

en: This research data is used to re includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions. This research data includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions.

habitat-heterogeneity

display_name: habitat-heterogeneity

hengduan-mountains

display_name: hengduan-mountains

hypervolume

display_name: hypervolume

model-residuals

display_name: model-residuals

phytodiversity

display_name: phytodiversity

en: Phytodiversity is associated with habitat heterogeneity from Eurasia to Hengduan


en: snow-saltation wind-tunnel cold-lab drifting-snow water-isotopes experiments metamorphism blowing-snow

en: Snow samples - microCT measurements

en: The data of the snow samples that were analysed in a Scanco® CT-40 scanner using a sample holder of 15 mm diameter which allowed for a high-resolution analysis with voxel size of 8 μm. The timestamp (local time, Davos, Switzerland) of snow sampling is given as well as the type of snow sample (see Snow samples - stable water isotopic composition dataset), the corresponding wind tunnel experiment number ("ExpNo", Wahl et al. (2024), Table 1) and the time since the snow introduction to the wind tunnel. The measured SSA is given in units 1/mm. The column "hist_filename" lists the name of the corresponding .txt file which contains the sphere size statistics of the snow samples. The sphere size statistics files are compressed in a .zip file.

en:
The dataset contains the stable water isotopic composition (d18O, dD. d-excess) of the snow samples taken during the ring wind tunnel experiments in January and May 2023 at the SLF cold laboratory facilities in Davos. The snow samples were collected in air-tight WhirlPaks and kept frozen at -20°C until they were melted in the closed sampling bags, filtered and 1.8ml were transferred into a 2ml glass vial and shipped to the Paul Scherrer Institut in Villigen, Switzerland where they were analysed following established laboratory procedures. The combined uncertainty on the individual isotopic composition is <0.5‰ for dD and <0.1‰ for d18O which yields an uncertainty of 0.9‰ on d-excess.
Timestamps are given for local Davos time (LT) and synchronised to the CRDS instrument which is in UTC. The "ExpNo" column refers to the experiment numbers given in Wahl et al. (2024), Table 1. The "type" of the snow samples is: "ini" = initial snow before introduction to the wind tunnel, "airborne" = snow sampled out of the air, "acc_in" = inner part of accumulated snow, "acc_out" = outer surface layer of accumulated snow.

en: Meteorological variables measured inside the wind tunnel during the May 2023 experiments

en: The dataset contains the 1-minute averages of the calibrated meteorological variables: temperature (T, °C), relative humidity (RH, %), relative humidity referenced against saturation over ice (RHice, %), specific humidity (q, g/kg) and wind speed (WS, m/s) as measured in the observation section of the wind tunnel at different heights and the room temperature of the laboratory measured outside the wind tunnel (Lab_Ta, °C). The measurement heights correspond to: "Top"=42.5cm, "Mid"=30cm, "Bot"=15cm, "PT100_Ta"=3cm. "WS" was measured at 36.5cm height. The time was synchronised to the concurrent CRDS measurements (which are UTC) and adjusted for the time lag resulting from pumping vapour from inside the wind tunnel to the CRDS. The timestamps indicate the beginning of the averaging period.

en: Snow samples - sphere size statistics files

en: The individual measurement reports of the microCT scan for each snow sample as combined zip file. The file names and the sample number indicate which snow sample it is, see "Snow samples - microCT measurements". Each file contains the statistics and the count of fitted sphere size diameters and the respective share (in-%) in the frequency distribution. Immediately after snow sampling, a small portion of snow was transferred to the cylindrical μCT sample holder with a diameter of 15 mm and analysed at -20°C using a Scanco® CT-40 scanner. The analysis followed Hildebrand and Rüegsegger (1997).

en: Wind tunnel water vapour isotopic composition

en: The water vapour and its calibrated isotopic composition sampled inside the wind tunnel at 46cm height during the experiments. The vapour was pumped through a heated copper tube at a rate of 0.2 l/min to the CRDS instrument located in a temperate room outside the cold laboratory. The data has 1 second resolution with the timestamp in UTC (CRDS reference time). The measured variables are the calibrated d18O (‰), dD (‰) and d-excess (‰) signals and the uncalibrated ("H2Oraw", ppmv) and calibrated ("H2Ocali",ppmv) humidity readings of the CRDS and converted to specific humidity ("spechum", g/kg). The isotope data was corrected for its humidity-isotope dependency and referenced against the VSMOW-SLAP scale. Calibration runs were performed daily and details can be found in Wahl et al. (2024) including the Supplementary Materials.

en: Meteorological variables measured inside the wind tunnel during the January 2023 experiments

en: The dataset contains the 1-minute averages of the calibrated meteorological variables: temperature (T, °C), relative humidity (RH, %), relative humidity referenced against saturation over ice (RHice, %), specific humidity (q, g/kg) and wind speed (WS, m/s) as measured in the observation section of the wind tunnel at different heights. The measurement heights correspond to: "Top"=42.5cm, "Mid"=30cm, "Bot"=15cm, "PT100_Ta"=3cm. "WS" was measured at 36.5cm height. The timestamp is in local time (UTC+1) and indicates the start of the averaging period.

en: This dataset collection contains all datasets collected during the ring wind tunnel experiments in January and May 2023 at the SLF cold laboratory facilities in Davos. A full description of the experiment set-up can be found in Wahl et al. (2024). The collection contains data from 19 experiments. The dataset collection entails measurements of the stable water isotopic composition of snow samples and the water vapour inside the wind tunnel, measurements of the meteorological variables inside the wind tunnel and snow sample properties as measured with microCT measurements (sphere size distribution and specific surface area (SSA)).

blowing-snow

display_name: blowing-snow

cold-lab

display_name: cold-lab

drifting-snow

display_name: drifting-snow

experiments

display_name: experiments

metamorphism

display_name: metamorphism

snow-saltation

display_name: snow-saltation

water-isotopes

display_name: water-isotopes

wind-tunnel

display_name: wind-tunnel

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Ring wind tunnel experiments - airborne snow metamorphism and stable water isotopes


maintainer: Alexandre Buttler

en: climate-change soil transplantation-experiment silvopastoral-systems biogeochemisty

en: Dataset

en: Zip archive of all datasets detailed in study.

en:
Silvopastoral systems are highly productive and combine long-term wood production with annual provision of forage for livestock. In the Swiss Jura Mountains these systems are a key component of the landscape. As in other cold biomes, climate change can potentially accelerate landscape change within these historically sustainable systems.

In order to anticipate the evolution of subalpine wooded pasture ecosystems under future climate and land-use changes, this project focused on the interplay between soil, vegetation and climate. It was aimed at providing experimental evidence for chief ecosystem processes, with emphasis on the quality of the ecosystem services provided. The main interest was placed on vegetation turf resistance to climate change along an unwooded – sparsely wooded - densely wooded pasture gradient (land-use intensity), where plant productivity, diversity and succession along with rates of carbon cycling and microbial activity provided measures of ecosystem functioning at both plot and landscape level.

Experimental transplantation of monolith soil turfs to lower altitudes allowed to simulate soil warming and reduced annual precipitation. In order to simulate a year-round warmer and drier climate the natural climate variation along an altitudinal gradient was used as a proxy. The aim was to simulate realistic climate change scenarios for the second half of the 21st century predicted by the IPCC report and downscaled for Switzerland providing regionalized interpolated projections integrating therein trends for temperature increase and precipitation decrease. By using permanent meteorological stations within the network of the Federal Office of Meteorology and Climatology (MeteoSwiss), we obtained high resolution regional data on the variation of mean annual temperature (MAT) and mean annual precipitation (MAP) in relation to altitude in the Swiss Jura Mountains. We observed a general increase of +0.5 K in MAT and a decrease of -20 % MAP for each 100 m decrease in altitude along the SE slope of the Swiss Jura Mountains. These relationships served for the selection of the transplantation sites such that in comparison to a control site at 1350 m a.s.l. (Combe des Amburnex, N 46°54’, E 6°23’) a +2 K MAT and -20 % MAP was achieved at 1010 m a.s.l. (Saint-George, N 46°52’, E 6°26’), a +4 K MAT and -40 % MAP at 570 m a.s.l., (Arboretum d’Aubonne, N 46°51’, E 6°37’), and a +5 K MAT and -50 % MAP at 395 m a.s.l. (Les Bois Chamblard, N 46°47’, E 6°41’). The two stations at 1010 m a.s.l. and 570 m a.s.l. corresponded to the IPCC scenario A1B for a moderate increase in greenhouse gas emissions and to scenario A2 for a high increase in greenhouse gas emissions, respectively. The station at 395 m a.s.l. was chosen to represent an extreme scenario with climate variables lying at the positive tail distribution of model predictions under the A2 scenario.

Soil cores were assembled into rectangular PVC boxes of 60  80 cm2 size and of 35 cm height. All mesocosms were dug down to surface level into previously prepared trenches in the ground thus preventing lateral heat exchange with the atmosphere. Since at each site the mesocosms were placed in a common garden with no light interception, mesocosms with turfs from the two wooded pastures were shaded from direct sun light to simulate the natural light conditions in the corresponding habitats. Each mesocosm was equipped with a drainage system and was connected to a water tank thus representing a zero potential lysimeter collecting soil solution and precipitation/snowmelt runoff. ECH2O EC-TM sensor probes coupled to Em50 data-loggers (Decagon Devices, Inc., USA) recorded soil temperature and volumetric water content in each mesocosm at the top-soil (0 to -3 cm) every minute and data were averaged over one hour intervals. Climate parameters at each transplantation site were monitored continuously throughout the experiment by means of automated weather stations (Sensor Scope Sàrl, Switzerland), measuring rain precipitation (non-heated tipping bucket gauges) and air temperature and humidity 2 m above the ground surface at one minute intervals.

A list of above- and belowground variables were measured to assess the resilience of biogeochemical processes, plant productivity, tree regeneration, and carbon sequestration for each respective land-use practice. Furthermore, the experimental data were used to improve on (parameterization) the existing spatially explicit, dynamic model WoodPaM and refine the modelʼs climatic and land-use variables so that different scenarios of climate change and land use change could be simulated. Natural and management induced disturbance patterns were incorporated into the model.
The data have been made available within the project CCES Mounted. The climate stations Sensorscope are still in use within the project CLIMARBRE (Wald und Klimawandel, WSL/BAFU).

#References

1. Puissant, J., Cécillon, L., Mills, R.T.E., Robroek, B.J.M. Gavazov, K., De Danieli, S., Spiegelberger, T., Buttler, A., Brun, J.J. 2015. Seasonal influence of climate manipulation on microbial community structure and function in mountain soils. Soil Biology and Biochemistry 80: 296–305.
2. Mills, R., K. Gavazov, T. Spiegelberger, D. Johnson and A. Buttler 2014. Diminished soil functions occur under simulated climate change in a sup-alpine pasture, but heterotrophic temperature sensitivity indicates microbial resilience. Science of the Total Environment, vol. 473–474(0): 465-472.
3. Gavazov, K., Spiegelberger, T. and Buttler, A. 2014. Transplantation of subalpine wood-pasture turfs along a natural climatic gradient reveals lower resistance of unwooded pastures to climate change compared to wooded ones. Oecologia (174) : 1425-1435.
4. Peringer A., Siehoff S., Chételat J., Spiegelberger T., Buttler A. & Gillet F. 2013. Past and future landscape dynamics in pasture-woodlands of the Swiss Jura Mountains under climate change. Ecology and Society, 18, 3: 11. DOI: 10.5751/ES-05600-180311. [online] URL: http://www.ecologyandsociety.org/vol18/iss3/art11/
5. Gavazov, K. S., A. Peringer, A. Buttler, F. Gillet and T. Spiegelberger. 2013. Dynamics of Forage Production in Pasture-woodlands of the Swiss Jura Mountains under Projected Climate Change Scenarios. Ecology and Society 18 (1): 38. [online] URL: http://www.ecologyandsociety.org/vol18/iss1/art38/

biogeochemisty

display_name: biogeochemisty

climate-change

display_name: climate-change

silvopastoral-systems

display_name: silvopastoral-systems

soil

display_name: soil

transplantation-experiment

display_name: transplantation-experiment

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: Biogeochemical data from a transplantation experiment of monolith soil turfs along an altitudinal gradient to simulate climate change scenarios


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

en: Site photo of the field station (CIF)

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

en: Site photo of the forest station (CIB)

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

en: Site aerial photo (Chironico)

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

en: Data plot of the forest station (CIB)

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

en: Forest station data (CIB)

en: Data file.

en: Data plot of the field station (CIF)

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 Chironico in Switzerland where one station is located within a natural coniferous forest stand (CIB) with Norway spruce (_Picea abies_; 160-180 yrs) and European silver fir (_Abies alba_; 140-160 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CIF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Chironico is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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: Chironico, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 2000 onwards


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

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

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

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

bayesian-inference

display_name: bayesian-inference

biodiversity

display_name: biodiversity

cryptogams

display_name: cryptogams

detectability

display_name: detectability

detection-probability

display_name: detection-probability

inter-observer-difference

display_name: inter-observer-difference

lichens

display_name: lichens

monitoring

display_name: monitoring

observer-error

display_name: observer-error

occupancy-model

display_name: occupancy-model

sessile-species

display_name: sessile-species

swisslichens

display_name: swisslichens

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


en: geochemical-solutes stable-water-isotopes precipitation stream-water spatially-distributed-sampling

en: The dataset contains daily measurements of volumes, isotopes and solutes of stream water as well as precipitation measured several locations; meteorological variables are added. Date: 18.07.2019

en:
This dataset contain measurements of solute and stable water isotopes in stream water and precipitation in the Alp catchment and two of its tributaries (between 2015 -2018) . The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l. The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (precipitation rates, discharge) at daily intervals from several sampling locations.
An updated version of the isotope dataset is available here: https://www.doi.org/10.16904/envidat.242

geochemical-solutes

display_name: geochemical-solutes

precipitation

display_name: precipitation

spatially-distributed-sampling

display_name: spatially-distributed-sampling

stable-water-isotopes

display_name: stable-water-isotopes

stream-water

display_name: stream-water

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: Daily data of solute and stable water isotopes in stream water and precipitation in the Alp catchment, Central Switzerland


en: photogrammetry uav wingtra dsm drone-data

en: Data Access

en:
- DSM 10cm resolution
- Orthomosaic 3cm resolution
- Agisoft Metashape Processing Report
- original RGB images

All generated products are also available as a cloud optimized geotiff.

en: We conducted four drone flights in Davos Wolfgang Arelen, in 2020/21 and 2022 to obtain data for the generation of DSMs and orthomosaics at a high resolution. The data was processed with the Agisoft Metashape Professional Software.

drone-data

display_name: drone-data

dsm

display_name: dsm

photogrammetry

display_name: photogrammetry

uav

display_name: uav

wingtra

display_name: wingtra

en: Photogrammetric Drone Data Wolfgang Arelen


maintainer: Anita C Risch

en: nutrient-addition microbial-biomass-carbon microbial-respiration climate anthropogenic-management herbivore-exclusion soil-properties

en: Risch et al. GEB_data.xlsx

en:
Study sites and experimental design
We collected data from 23 sites that are part of the Nutrient Network Global Research Cooperative (NutNet, https://nutnet.umn.edu/). The mean annual air temperature (MAT) across these sites ranged from -4 to 22°C, mean annual precipitation (MAP) from 252 to 1592 mm, and elevations from 6 to 4261 m above sea level (Fig 1a, Supplementary Table S1), hence cover a wide range of climatic conditions under which grasslands occur (Fig 1b). Soil organic C concentrations ranged between 0.8 to 7.8%, soil total N concentrations between 0.1 and 0.6%, and the soil C:N ratio between 9.1 and 21.5. Soil clay content spanned from 3.0 to 35%, and soil pH from 3.4 to 7.6 (Supplementary Table S2).
At each site, the effects of nutrient addition and herbivore exclusion were tested via a randomized-block design (Borer et al., 2014). Three blocks with 10 treatment plots each were established at each site, except for the site at bldr.us (only two blocks). Each of these 10 plots was randomly assigned to a nutrient or fencing treatment. An individual plot was 5 x 5 m, divided into four 2.5 x 2.5 m subplots. Each subplot was further divided into four 1 x 1 m square sampling plots, one of which was set aside for soil sampling (Borer et al., 2014). Plots were separated by at least 1 m wide walkways. We collected soil samples from four different treatments for this study: (i) untreated control plots (Control), (ii) herbivore exclusion plots (Fence), (iii) plots fertilized with N, P, K, plus nine essential macro and micronutrients (NPK), and (iv) plots with simultaneous fertilizer addition and herbivore exclusion (NPK+Fence). The experiments were established at different times in the past, with years of treatment different among sites (2 – 9 years since start of treatment; Supplementary Table S1). For the nutrient additions, all sites applied 10 g N m-2 each year as time-release urea; 10 g P m-2 yr-1 as triple-super phosphate; 10 g K m-2 yr-1 as potassium sulfate. A micro-nutrient mix (Fe, S, Mg, Mn, Cu, Zn, B, Mo, Ca) was applied at 100 g m-2 together with K in the first year of treatments but not thereafter.
We excluded large vertebrate herbivores (Fence) by fencing two plots, one with and one without NPK additions, within each block. The fences excluded all aboveground mammalian herbivores with a body mass of over 50 g (Borer et al., 2014). At most sites, the fences were 180 cm high, and the fence contained a wire mesh (1 cm holes) for the bottom 90 cm with a 30 cm outward-facing flange stapled to the ground to exclude burrowing animals. Climbing and subterranean animals may potentially still access these plots (Borer et al., 2014). For slight modifications in fence design at a few sites see Supplementary Table S3. Most sites only had wild herbivores, although four sites were also grazed by domestic animals (Supplementary Table S1).

Collection of soil samples, soil microbial respiration, microbial biomass, and other soil properties
Each of the 23 sites received a package containing identical material from the Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland to be used for sampling (Risch et al., 2015; Risch et al., 2019). We collected two soil cores of 5 cm diameter and 12 cm depth in each sampling plot and composited them to measure MR, MBC, and soil chemical properties (see below). An additional sample (5 x 12 cm) was collected to assess soil physical properties. This sample remained within a steel sampling core after collection and both ends were tightly closed with plastic caps to avoid disturbance. All soils were shipped cooled to the laboratory at (Location will be disclosed after manuscript acceptance) within a few days after collection. Soils were sampled roughly 6 weeks prior to peak biomass at each site during 2015 and 2016.
To assess MR (CO2 production) in a laboratory incubation experiment we weighed duplicate soil samples (8 g dry soil equivalent) into 50-ml Falcon tubes. No additional substrate (glucose, sugar) was added to these samples. We adjusted the soil moisture of each sample to 60% field capacity. We then placed a 15 ml plastic test tube (Semadeni 1701A) containing 7.25 ml 0.05 M NaOH over each soil sample. The test tube was fixed with a plastic rod so that it was not in contact with the soil sample. The Falcon tubes were then sealed with a screw cap and placed in an incubator under completely dark conditions at 20°C. The CO2 produced by microbial respiration was absorbed by the 0.05 M NaOH. For five weeks we measured the decrease in conductivity within the 0.05 M NaOH solution on a weekly basis with a Multimeter WTW Multi 3410 (WTW GmbH, Germany) and replaced the 0.05 M NaOH with fresh solution. We included Falcon tubes without soil samples in each incubation run as blanks to test if tubes were tight and no CO2 could enter or escape. We calibrated the relationship between conductivity reduction and NaOH absorbed as follows: 400 ml 0.05 M NaOH was placed in a beaker and its conductivity was measured with the multimeter. While stirring, air containing CO2 was blown into the solution for approximately one minute, which reacts with NaOH to form Na2CO3. After this process, conductivity was measured again. We then transferred 7.25 ml of the solution into a smaller beaker and added 1 ml of 0.1 M BaCl2 to precipitate Na2CO3 and then titrated the solution with 0.05 M HCl to determine the remaining NaOH. We then repeated these steps with the remaining solution a total of nine times and plotted the conductivities (y-axis) against the NaOH consumed (x-axis, Supplementary Fig S1). This regression line was used to infer the consumption of NaOH from the conductivity reduction in the incubation experiments and to calculate CO2 evolution during incubation. In addition, we determined the optimum concentration for the NaOH solution in series of preliminary experiments, so that the concentration was not too high to become insensitive, but also not too low so that not all NaOH reacts during incubation. We then calculated MR (mg CO2-C kg dry soil-1 h-1) as total amount of CO2 released over the 5 weeks divided by the duration of the entire incubation in hrs.
Soil microbial biomass carbon (MBC; mg C kg soil-1 ) was measured at the beginning of the experiment by measuring the maximal respiratory response to the addition of glucose solution (4 mg glucose per g soil dry weight dissolved in distilled water; substrate-induced respiration method) on approximately 5.5 g of soil (J. P. E. Anderson & Domsch, 1978; Nico Eisenhauer et al., 2018; Scheu, 1992). For this purpose we used an O2-micro-compensation apparatus (Scheu, 1992). More specifically, substrate-induced respiration was calculated from the respiratory response to D-glucose for 10 hr at 20°C. Glucose was added according to preliminary studies to saturate the catabolic enzymes of microorganisms (4 mg g soil-1 dissolved in 400 ml deionized water). The mean of the lowest three readings within the first 10 hrs (between the initial peak caused by disturbing the soil and the peak caused by microbial growth) was taken as maximum initial respiratory response (MIRR; ml O2 kg soil-1 h-1) and microbial biomass (mg C kg soil-1) was calculated as 38 x MIRR (Beck et al., 1997; Cesarz et al., 2022; Thakur et al., 2015).
The rest of the composited sample was dried at 65°C for 48 h, ground and sieved (2 mm mesh) to assess the soil pH, mineral soil total C and N and C:N ratio, and mineral soil organic C (Risch et al., 2019). The undisturbed sample was used to assess water holding capacity (WHC), bulk density (BD), and soil texture [sand, silt, clay; methods in (Risch et al., 2019)]. We used the percentage of sand and clay as an indicator of soil texture in this study. MAT (°C), MAP (mm) and temperature of the wettest quarter (°C) were obtained from www.worldclim.com (Fick & Hijmans, 2017; Hijmans, Cameron, Parra, Jones, & Jarvis, 2005). These variables were selected as they were found to be drivers of soil nutrient processes across these sites in earlier studies (Risch et al., 2020; Risch et al., 2019). Mean annual soil temperatures (MAST; °C) for the 0 to 5 cm soil layer were obtained for each site from the SoilTemp maps (J. Lembrechts et al., 2021; J. J. Lembrechts et al., 2022), global gridded modelled products of soil bioclimatic variables for the 1979-2013 period at a 1-km² resolution, based on CHELSA, ERA5 and in-situ soil temperature measurements.
Numerical calculations and statistical analyses
We calculated MMQ as MR/MBC. We corrected this measure using the average soil temperature of each site (MMQsoil). This temperature correction is necessary as incubation temperatures are usually much higher than site mean annual soil temperatures (see Xu et al. 2017). MMQsoil = MMQ x Q10(MAST – 20)/10, where Q10 was assumed to be 2 (Xu et al. 2017). See Supplementary Fig S2 for comparison of air and soil temperatures across the 23 sites as well as the incubation temperature.
Some of the explanatory variables (clay, soil organic C, C:N ratio) were skewed and were thus log-transformed prior to analyses. All continuous explanatory variables were centred and scaled to have a mean of zero and variance of one. To avoid collinearity between them we filtered them using correlation analysis (Supplementary Fig S3). From the variables that were strongly correlated (Pearson’s |r| > 0.70) (Dormann et al., 2013), we selected the ones that allowed us to minimize the number of variables (Supplementary Fig S3). Specifically, soil total N concentration, soil total C concentration, soil sand content and soil bulk density were dropped from the dataset. We then assessed how these edaphoclimatic variables are related to MMQ across our global grasslands.
For this, we used linear mixed effects models (LMMs) fitted by maximum likelihood with the lme function in the nlme package (version 3.1-153) (Pinheiro, Bates, DebRoy, & Sarkar, 2021) in R version 3.6.3. (R Core Team, 2019). We used treatment as a fixed effect and plot nested in site as random effects to assess treatment differences in MMQsoil, as well as MR, and MBC. The number of years since the treatment started was included as a fixed effect in all the initial models but was not significant and therefore not retained in the models. To assess how differences in MMQsoil were affected by environmental factors (soil, climatic properties) we again used LMMs. Soil and climatic properties were included as fixed effects and plot nested in site as random effects. We did not include interactions between environmental variables. We then used the MuMin package (Barton, 2018) (version 1.42.1) to select the best models that explained the most variation based on Akaike’s information criterion (AIC; model.avg function). We used the corrected AIC (AICc) to account for our small sample size and selected the top models that fell within 4 AICc units (delta AICc < 4) (Burnham & Anderson, 2002; Johnson & Omland, 2004). We present all our top models rather than model averages. Conditional averages are provided in the Supplementary material.
Based on findings from analyses described above and the literature, we developed a conceptual model of direct and indirect relationships between both edaphoclimatic variables and experimental treatments (Supplement Figure S4) to obtain a more holistic approach in understanding how these properties affect MMQsoil. We had data from 23 sites with 272 observations. We tested this model using structural equation modelling based on a d-sep approach (Lefcheck, 2016; Shipley, 2009). We considered those environmental drivers that were included in our top LMMs, namely temperature of the wettest quarter (T.q.wet), soil pH, water holding capacity (WHC) and soil organic C (organic C; Supplementary Figure S4). These factors were allowed to directly affect MMQsoil, and via their interactions with treatments. In addition, treatments were allowed to directly affect MMQsoil. Treatments were included as dummy variables in the model. We tested our conceptual model (Supplementary Fig S4) using the piecewiseSEM package (version 2.0.2; Lefcheck, 2016) in R 3.4.0, in which a structured set of linear models are fitted individually. This approach allowed us to account for the nested experimental design, and overcome some of the limitations of standard structural equation models, such as small sample sizes (Lefcheck, 2016; Shipley, 2009). We used the lme function of the nlme package to model response variables, including site as a random factor. Good fit of the SEM was assumed when Fisher’s C values were non-significant (p > 0.05). For all significant interactions between soil or climate variables and treatments detected in the SEMs, we calculated treatment effect sizes, i.e., the differences in MMQsoil between Control and treatments as log response ratios (LRR) and plotted these values against the climate or soil factors. The LRR were defined as log(Control/Treatment), where treatment was either Fence, NPK or NPK+Fence. To assess which of the LRR-climate or soil property relationships were significant we again used LMMs, in which soil and climatic properties were included as fixed effects and plot nested in site as random effects.

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

Paper Citation:

Risch Anita C., Zimmermann, Stefan, Schütz, Martin, Borer, Elizabeth T., Broadbent, Arthur A.D., Caldeira, Maria C., Davies, Kendi F., Eisenhauer, Nico, Eskelinen, Anu, Fay, Philip A., Hagedorn, Frank, Knops, Johannes M.H., Lembrechts, Jonas, J., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Seabloom, Eric W., Silveira, Maria L., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Drivers of the microbial metabolic quotient across global grasslands. Global Ecology and Biogeography

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

The microbial metabolic quotient (MMQ; mg CO2-C mg MBC-1 h-1), defined as the amount of microbial CO2 respired (MR; mg CO2-C kg soil-1 h-1) per unit of microbial biomass C (MBC; mg C kg soil-1), is a key parameter for understanding the microbial regulation of the carbon (C) cycle, including soil C sequestration. Here, we experimentally tested hypotheses about the individual and interactive effects of multiple nutrient addition (NPK+micronutrients) and herbivore exclusion on MR, MBC, and MMQ across 23 sites (5 continents). Our sites encompassed a wide range of edaphoclimatic conditions, thus we assessed which edaphoclimatic variables affected MMQ the most and how they interacted with our treatments. Soils were collected in plots with established experimental treatments. MR was assessed in a five-week laboratory incubation without glucose addition, MBC via substrate-induced respiration. MMQ was calculated as MR/MBC and corrected for soil temperatures (MMQsoil). Using LMMs and SEMs, we analysed how edaphoclimatic characteristics and treatments interactively affected MMQsoil. MMQsoil was higher in locations with higher mean annual temperature, lower water holding capacity, and soil organic C concentration, but did not respond to our treatments across sites as neither MR nor MBC changed. We attributed this relative homeostasis to our treatments to the modulating influence of edaphoclimatic variables. For example, herbivore exclusion, regardless of fertilization, led to greater MMQsoil only at sites with lower soil organic C (<1.7%). Our results pinpoint the main variables related to MMQsoil across grasslands and emphasize the importance of the local edaphoclimatic conditions in controlling the response of the C cycle to anthropogenic stressors. By testing hypotheses about MMQsoil across global edaphoclimatic gradients, this work also helps to align the conflicting results of prior studies.

anthropogenic-management

display_name: anthropogenic-management

climate

display_name: climate

herbivore-exclusion

display_name: herbivore-exclusion

microbial-biomass-carbon

display_name: microbial-biomass-carbon

microbial-respiration

display_name: microbial-respiration

nutrient-addition

display_name: nutrient-addition

soil-properties

display_name: soil-properties


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

en: Data access

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

climate-change-impact

display_name: climate-change-impact

discharge

display_name: discharge

hydrology

display_name: hydrology

model

display_name: model

water-temperature

display_name: water-temperature

educ

en: Education, culture and sport

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


maintainer: Nander Wever

en: precipitation-amount shortwave-radiation air-temperature snow-ice-temperature humidity longwave-radiation wind-direction wind-speed snow-depth

en: SNOWPACK ini file (new syntax)

en: This ini-file can be used to run SNOWPACK simulations with the WFJ data set, using the new MeteoIO Filter, Generator and Interpolation syntax.

en: Version 4

en:
Deprecated version. Release notes:

Update: dataset extended to 2016-09-02T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: SNOWPACK ini file (old syntax)

en: This ini-file can be used to run SNOWPACK simulations with the WFJ data set, using version 3.4.0 or higher.

en: SNOWPACK sno file

en: This sno-file can be used to run SNOWPACK simulations with the WFJ data set, using version 3.4.0 or higher.

en: Version 6

en:
Deprecated version.

Release notes:

- Missing ISWR for 2009-09-30 and 2010-04-30 has been replaced by data from WFJ Gipfel

This dataset is the basis for the Weissfluhjoch dataset in ESM-SnowMIP (doi: http://dx.doi.org/10.16904/16).

en: Version 2

en:
Deprecated version. Release notes:

Bug fix: in v1, incoming radiation components were taken from WFJ_Gipfel station, instead of WFJ Versuchsfeld.

en: All versions

en: Undercatch correction

en: This document describes how the undercatch correction for the Weissfluhjoch measurement site was determined.

en: Version 3

en:
Deprecated version. Release notes:

Bug fix: in version 2, the undercatch correction was not applied correctly

Bug fix: in version 2, snow lysimeter data was missing for extended periods of time

Update: dataset extended to 2016-01-02T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 7 (current)

en:
Release notes:

Bug fix: in previous versions, snow lysimeter data from September 2010 onwards was erroneously reported for UTC+2, instead of UTC+1 (i.e., reported one hour later than should have been).

Update: dataset extended to 2017-07-11T00:30.

en: Version 5

en:
Deprecated version. Release notes:

Update: using the incoming and outgoing short- and longwave radiation data from the new processing chain from 2010 onwards, filling also data gaps in pre-2010 data.

Update: dataset extended to 2016-10-04T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 1

en:
Deprecated version. Release notes:

First release of dataset.

en:
Dataset of meteorological and snowpack measurements from the automatic weather station at Weissfluhjoch, Davos, Switzerland, suitable for driving snowpack models. The dataset contains standard meteorological measurements, and additionally snowpack runoff data from a snow lysimeter. Where possible, data is quality checked and missing data are replaced from backup sensors from the measurement site itself, or (in only a few cases) from the MeteoSwiss weather station at 470 m distance and 150 m above the measurement site.

Publication
Wever, N., Schmid, L., Heilig, A., Eisen, O., Fierz, C., and Lehning, M. Verification of the multi-layer SNOWPACK model with different water transport schemes. 2015. The Cryosphere. Volume 9. 2271-2293. http://dx.doi.org/10.5194/tc-9-2271-2015.

air-temperature

display_name: air-temperature

humidity

display_name: humidity

longwave-radiation

display_name: longwave-radiation

precipitation-amount

display_name: precipitation-amount

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

snow-ice-temperature

display_name: snow-ice-temperature

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

envidat

en: EnviDat: the Environmental Data Portal

en: WFJ_MOD: Meteorological and snowpack measurements from Weissfluhjoch, Davos, Switzerland