en:
Acknowledgements: 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).

DISCLAIMER: Basic scripts are provided for the Geomatics peer review (in 2021) and kept as additional information for the dataset. Nevertheless, please note that software dependencies and libraries, as well as cloud storage paths, may quickly become deprecated over time (after 2021). For compatibility, stable dependencies and libraries released around 2020 should be used.

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

en: CORE Dataset (80 GB)

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

en: Disclaimer: provided for the Geomatics peer review in 2021 and left as additional information for the dataset. Software dependencies and libraries, as well as cloud storage paths, may change over time (after 2021).

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


en:
This is the simulation dataset from _"Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland"_, M. Bavay, T. Grünewald, M. Lehning, Advances in Water Resources 55, 4-16, 2013

A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graubünden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models.

The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095.

Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation.

en: alpine3d-simulations climate-change graubunden snow switzerland

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

en: Alpine3D simulations of future climate scenarios for Graubunden

type: dataset

key: guid

key: uri

value: Envidat Harvester

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

fr: Éducation, culture et sport

it: Istruzione, cultura e sport

en: Configuration file

format: SERVICE

en: Reference data as well as the 2050 and 2099 scenarios for ARPEGE and ETHZ other RCMs available on demand)

en: Simulation data

climate-change

display_name: climate-change

graubunden

display_name: graubunden

snow

display_name: snow

switzerland

display_name: switzerland


en:
 Overview
We present GROUNDEYE, a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow.
Embedded in the surface process model Alpine3D, the new terrain radiation model GROUNDEYE receives interpolated real weather data with diffuse and direct broadband shortwave radiation for each pixel as well as a spatially variable plane albedo from the module SNOWPACK.

 Format
The GROUNDEYE model is written in c++, as is the entire environment of Alpine3d. The input and output data sets are .xlsx or .txt format, pre- and postprocessing including the generation of all figures is in .R format.

 Structure
In Data_Forward_Scattering.zip you will find all necessary data and model details to
reproduce the results of the JGR publication "How forward-scattering snow and terrain change the Alpine radiation balance with application to solar panels"

 - 	Model Input Data contains the meteorological and topographic input data sets, the BRDF, and preprocessing scripts.
 - Model Code contains the full model Alpine3d including the radiative transfer module GROUNDEYE.
 - Model Output Data contains the results of the simulation of terrain irradiance and irradiance of solar panels; hourly resolution, 1. Sptember 2017 - 31. August 2018.
 - Measurements Solar Testsite contains information and measurements of the solar testsite at the Totalp near Davos, Switzerland.
 - Postprocessing contains all R-Scripts used for the analysis and
plotting of the corresponding data.

In each of these folders you will find detailed information in
the file 'About this Folder.txt'.

en: albedo photovoltaic shortwave-radiation snow winter-energy-production

en: Data, Code Link and Metadata on Forward Scattering of Snow at Totalp

en: GROUNDEYE model for multiple forward scattering in snow-covered terrain. Meteorological and topographical input data set for the Meierhofertäli near Davos, Switzerland, September 2017 - August 2018. Output data for September 2017 - August 2018 at hourly resolution and every pixel. Post-processing scripts for plotting and analysis.

en: Data Forward Scattering

albedo

display_name: albedo

photovoltaic

display_name: photovoltaic

shortwave-radiation

display_name: shortwave-radiation

winter-energy-production

display_name: winter-energy-production


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).

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

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

en: Data access

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


en:
The data presented here corresponds to the publication "A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation" (Gerber et al., 2017), which investigates an eddy-like structure in the vicinity of the Sattelhorn in the Dischma valley (Davos Switzerland) and its influence on snow accumulation. The dataset contains:

* Alpine3D: Alpine3D snow depth grids (25 m resolution) for two simulations with and without snow redistribution.
* ARPS: 10 ARPS simulations (25 m horizontal resolution) with different model setups (wind direction, wind speed, stability).
* LiDAR: Processed LiDAR PPI (D2_PPI_1h) and RHI (D2_cross_1h) across the valley with a hourly resolution for the period 27 October 2015 01:00 - 29 October 2015c 21:00 (spatial resolution: 25 m).
* meteostations-dischma: Meteorological station data of two meteorological stations in the Dischma valley with 10 minute resolution for the period 28 October 2015 - 30 October 2015.
* TLS: Snow depth change data (m) between 28 October 2015 and 30 October 2015 based on terrestrial laser scans.

For more details about the simulation and observation data, see Gerber et al., 2017.

Publication: Gerber et al., 2017: A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation, submitted to JGR - Atmospheres.

en: alpine3d lee-side-flow-field lidar preferential-deposition tls

en: DISCHMEX - Observations and simulations of the close-ridge small-scale atmospheric flow field and snow accumulation at Sattelhorn, Dischma valley, Davos, Switzerland.

en: Dataset

format: ZIP

media_type: application/zip

lee-side-flow-field

display_name: lee-side-flow-field

lidar

display_name: lidar

preferential-deposition

display_name: preferential-deposition

tls

display_name: tls


en:
This dataset contains the data acquired during the expedition to Princess Elisabeth Antarctica Station in December 2016 and January 2017. The dataset consits of meterorological data, drifting snow mass flux data, SnowMicroPen data and Terrestrial Laser Scanning data.

Please refer to the README for more information about the data.

This dataset is the basis of the following publication:

Sommer, C. G., Wever, N., Fierz, C., and Lehning, M.: Wind-packing of snow in Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-36, in review, 2018.

en: antarctica drifting-snow mass-flux meteorological-data snowmicropen terrestrial-laser-scanning

envidat

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

en: EnviDat: the Environmental Data Portal

en: Expedition to Princess Elisabeth Antarctica Station, 2016/2017

educ

en: Education, culture and sport

en: Describes the dataset in detail

en: README

format: TXT

media_type: text/plain

antarctica

display_name: antarctica

drifting-snow

display_name: drifting-snow

mass-flux

display_name: mass-flux

meteorological-data

display_name: meteorological-data

snowmicropen

display_name: snowmicropen

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning


wind-crust-formation-snowmicropen-data

en:
This dataset contains the SnowMicroPen (SMP) data from 38 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winters 2015/16 and 2016/17 and include more than 1000 SMP measurements.

The SMPs are organized per experiment. Each experiment subfolder contains the processed SMP profiles and some additional files.

Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.

This dataset is the basis of the following publication:

Sommer C.G., Lehning M., & Fierz C. (2017). Wind tunnel experiments: Saltation is necessary for wind-packing. Journal of Glaciology, 63(242), 950-958. doi:10.1017/jog.2017.53

en: smp snow snowmicropen wind-crust wind-packing

en: Wind crust formation: SnowMicroPen data

title_for_slug: wind-crust-formation-snowmicropen-data

en: SMP data

en: Contains the R-scripts used to process the SMPs

en: Processing scripts

smp

display_name: smp

snow

display_name: snow

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing


en:
This data sets contains the Microsoft Kinect data from 15 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winter 2016/17. The Kinect measures distributed snow depth.

The Kinect data is organized per experiment. Each experiment subfolder contains the processed Kinect depth images and some additional files.

Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.

This dataset is the basis of the following publication:

Sommer C.G., Lehning M. & Fierz C. (2018). Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow. Front. Earth Sci. 6:4. doi: 10.3389/feart.2018.00004

en: kinect snow wind-crust wind-tunnel wind-packing

en: Wind crust formation: Microsoft Kinect data

en: Kinect data

en: Contains the Matlab- and R-scripts used to process the Kinect data

kinect

display_name: kinect

wind-tunnel

display_name: wind-tunnel


en:
This dataset contains measurement and simulation data. The measurements characterize the standard meteorology, turbulence, and snow transport at the S17 site near Syowa Station in East Antarctica during an expedition in austral summer 2018/2019. Large-eddy simulations with sublimating particles provide additional insight into the latent and sensible heat exchange between snow and air in two example situations observed at the S17 site.

A part of the measurement data was recorded by an automatic measurement station from 10th January 2019 to 26th January 2019. This measurement station was equipped with standard meteorological sensors, a three-dimensional ultrasonic anemometer, an open-path infrared gas analyzer, a snow particle counter, an infrared radiometer for measurements of the surface temperature, and a sonic ranging sensor measuring changes in snow surface elevation.

At a horizontal distance of approximately 500 m, a Micro Rain Radar (MRR) was installed in a tilted configuration with an elevation angle of 7° for remote sensing of blowing snow between 25th December 2018 and 24th January 2019. In addition, near-surface in-situ measurements of snow transport were performed at the location of the MRR by deploying a snow particle counter from 27th December 2018 to 24th January 2019.

The simulations cover a 18 x 18 x 6 m³ domain and reproduce the steady-state conditions during a 10-min interval with significant snow transport and another 10-min interval with negligible snow transport. We provide the model source code and the post-processed simulation data, i.e., horizontally averaged quantities as a function of height and time.

en: antarctica blowing-snow drifting-snow eddy-covariance-measurements large-eddy-simulations mass-flux meteorological-data micro-rain-radar turbulence

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: Meteorology and snow transport at S17 near Syowa, Antarctica, in austral summer 2018/2019

en: Meta data

en: Readme

en: Automatic in-situ measurements of micro-meteorology and snow transport

en: Measurement data from micro-meteorological station

en: Remote sensing data of blowing snow from a Micro Rain Radar (MRR).

en: Measurement data from Micro Rain Radar (MRR)

en: Near-surface observations of drifting snow from a Snow Particle Counter (SPC) installed at the location of the Micro Rain Radar.

en: Measurement data from snow particle counter at MRR site

en: Source code of the large-eddy simulations coupled with a Lagrangian stochastic model (LES-LSM), available on GitLab.

en: Data from two LES-LSM simulations called Drift_1 and Drift_2, which reproduce the conditions observed at the S17 site on 11th January 2019, 00:40 to 00:50 UTC.

en: Simulation data for a situation with significant snow transport

en: Results from two LES-LSM simulations called NoDrift_1 and NoDrift_2, which reproduce the conditions observed at the S17 site on 12th January 2019, 05:40 to 05:50 UTC.

en: Simulation data for a situation with negligible snow transport

blowing-snow

display_name: blowing-snow

eddy-covariance-measurements

display_name: eddy-covariance-measurements

large-eddy-simulations

display_name: large-eddy-simulations

micro-rain-radar

display_name: micro-rain-radar

turbulence

display_name: turbulence


en:
 Overview

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

 Experimental Setup

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

 Data

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

envidat

en: EnviDat: the Environmental Data Portal

en: Alpine3D simulations of future climate scenarios CH2014

educ

en: Education, culture and sport

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

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

en: CH2014 REPORT

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

en: Dataset

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

en: Graubunden input

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

en: Aare input

climate-change

display_name: climate-change

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent


snow-drift-station-micro-rain-radar

en: The instrument (MRR, Metek) was mounted at Gotschnagrat (LON: 46.859 LAT: 9.849) at a height of 1 m above snow surface (at the beginning of the campaign) with an orientation of 22° with respect to North and a horizontal viewing direction. The sampling time was either 5 s or 10 s, depending on the settings at the specific period. The MRR produces standard outputs like radar reflectivity, doppler velocity, etc., and additional information can be found [here](https://metek.de/de/product/mrr-2/).

en: mountain precipitation raclets radar rain snow

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: Snow Drift Station - Micro Rain Radar

title_for_slug: snow-drift-station-micro-rain-radar

en: The PDF contains additional information on parameters which were set for the certain time periods (time in UTC).

en: Parameter MRR

format: PDF

media_type: application/pdf

en: The dataset contains measurements between March 1 and March 5, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: MMR Data Period 1

en: The dataset contains measurements between March 5 and March 8, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: MRR data Period 2

en: The dataset contains measurements between March 8 and March 13, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: MRR data of period 3

en: The dataset contains measurements between March 13 and March 22, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: MRR data of period 4

mountain

display_name: mountain

precipitation

display_name: precipitation

raclets

display_name: raclets

radar

display_name: radar

rain

display_name: rain


en:
This dataset contains long-term snow water equivalent and corresponding snow depth data 11 observer sites in Switzerland between 1200 and 2500 m a.s.l. compiled for the Global Climate Observing System (GCOS) and supported by MeteoSwiss. Snow depth (cm) and snow water equivalent (mm) are manually recorded every 2 weeks since the 1947 (depending on station). The attached metadata file gives details for each station.
The measurement series agree with GCOS objectives according to the GCOS Implementation Plan: This inlcudes:
• Raw data are archived in the snow and avalanche database at SLF.
• Measuring techniques are traceable and documented as snow depth and snow water equivalent have in general remained the same since beginning up to now. When planning new systems or changes of existing systems in the future, their impact will be assessed prior to implementation.
• Historical data of these 11 stations have been digitized and all data have been quality controlled.
• Detailed metadata (location of measurements) are available.
• Public availability of the data has been ensured by publishing the data on the Envidat portal (https://www.envidat.ch/dataset/gcos-swe-data).

en: gcos snow snow-height snow-profile snow-water-equivalent

envidat

en: EnviDat: the Environmental Data Portal

en: GCOS SWE data from 11 stations in Switzerland

educ

en: Education, culture and sport

en: STATION METADATA

en: DATA ACCESS

format: CSV

media_type: text/csv

gcos

display_name: gcos

snow

display_name: snow

snow-height

display_name: snow-height

snow-profile

display_name: snow-profile


irkis-soil-moisture-measurements-davos

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

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

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

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

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

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

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

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

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

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

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: IRKIS Soil moisture measurements Davos

title_for_slug: irkis-soil-moisture-measurements-davos

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

en: IRKIS soil moisture data (zip file)

davos

display_name: davos

dischma-catchment

display_name: dischma-catchment

meteo-station

display_name: meteo-station

meteorological-measurements

display_name: meteorological-measurements

soil-moisture

display_name: soil-moisture


bats-in-beaver-systems

en:
This dataset contains processed bat acoustic data collected to evaluate the ecological impacts of beaver-engineered ponds on bat species richness, activity, and feeding activity. It includes nightly activity and feeding call counts identified to species or species group level, site and treatment information (beaver pond vs. control areas along the same stream), and associated environmental covariates such as deadwood volume, arthropod abundance, and vegetation structure.

Methods: Bats were recorded using passive acoustic detectors placed at paired beaver-influenced and control sites. The resulting data was processed in the software Batscope. Arthropod abundance was estimated using flight-interception traps. The amount of deadwood and vegetation cover was measured in the field. Canopy cover and canopy heterogeneity were modelled from GIS models.

Temporal and Spatial Coverage:
Data were collected from eight sites in Switzerland between June and July 2022, each site sampled for 16 nights.

en: beaver castor chiroptera deadwood ecosystem-engineer flying-arthropods habitat-heterogeneity nature-conservation wetland

en: Bats in beaver systems

title_for_slug: bats-in-beaver-systems

en: README text file providing context for the data. Please do not hesitate to contact us if you have any questions

en:
For the time information about the research data, created (2022-09-23 to 2022-09-23) refers to the GIS data, collected (2022-06-13 to 2022-07-31) to the field sampling. Details can be found in the csv “sampling_dates”.

sampling_dates with columns
"site" (sampling site),
"bats_1" (first period of bat survey in June),
"bats_2" (second period of bat survey in June),
"bats_3" (first period of bat survey in July),
"bats_4" (second period of bat survey in July),
"flying_arthropods_1" (arthropod sampling in June),
"flying_arthropods_2" (arthropod sampling in July),
"deadwood survey" (date of deadwood survey),
"vegetation survey" (date of vegetation survey)

en: sampling dates.xlsx

format: XLS

en:
vegetation_characteristics data on vegetation_cover and cumulative_cover with columns "laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"Vegetation_cover" (estimated vegetation cover over 5x1 m plot in %),
"Cumulative cover" (summed up cover over tree, shrub and herb layer in %),
"Cumulative cover trees" (tree cover level, higher than 3 metres in %),

en:
gewichtungsfaktor with columns
"Project" (sampling site),
"Treatment" (sampling location within site, with levels ctrl for Control and pool for Pool),
"date" (sampling date),
"gew" (weighting factor to standardize night length)

en:
dead_wood for deadwood with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"type" (deadwood type),
"height_length" (the height or lenghth of a piece of deadwood in m),
"DBH_diamlarge" (diameter at breast height or diameter of larger end if lying in m),
"diamsmall (diameter of smaller end if lying in m",
"volume" (calculated volume in m3)

en:
bats_biodiv.day.guilds with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"guild" (which feeding guild information),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en:
bats_biodiv.day with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en:
bats.environment with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"canopy" (modelled tree canopy cover, averaged across radius of 100 m, unitless),
"structure_ruggedness" (mean ruggedness of the total vertical structure > 3 m, averaged across radius of 100 m, unitless), "vegetation_ruggedness" (mean ruggedness of the canopy > 3 m, later called canopy_heterogenneity, averaged across radius of 100 m, unitless),
"pool_size" (maximum width of beaver pool in m),
"area_deadwood_monitoring" (total area of deadwood moonitoring in m3),
"age" (beaver presence in years)

en:
bat_guilds with columns
"SP" (identification level),
"EndGuilde" (information on feeding guild)

en:
arthropods_flying the arthropod abundance with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"month" (sampling month),
"abundance" (number of flying arthropods)

en:
site_info contains columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"location" (sampling location row within site),
"sample_nr" (sampling number within location)

en:
Raw data data_bats with columns:
“Categories” (empty or "feeding", indicating that call sequence contains feeding buzz)
“Sequence” (sequence number)
“Timestamp” (recording time)
“Temperature” (temperature recorded by batlogger in degree celcius)
“Serial” (serial number of bat logger)
“Duration” (duration of call sequence in seconds)
“Call.Count” (call count number, how many calls could be detected)
“Verification.1” (bat sequence verification)
“Notes” (remarks for identification)
“treatment” (treatment ctrl = Control and Pool)
“slot” (slot number, 4 slots, two in June, two in July)
“Laufnummer” (unique identifier per sampling point)

en: R script bats_age to test the beaver pool age effect on bat richness, activity and feeding activity

format: OCTET

en: R script bats_data_cleaning to clean the raw data

en: R script bats_data_exploration_Figure_nighly to explore the data and display nightly species richness, activity and feeding activity

en: R script bats_glmm to test the influence of beavers on bat richness, activity and feeding activity

en: R script bats_pool_size to test the beaver pool size on bat richness, activity and feeding activity

en: bats_raw_values_overview to produce raw value summaries and tables

en: R script bats_SEM to test the influence of the beaver on bat richness, activity and feeding activity through feeding and habitat heterogeneity

beaver

display_name: beaver

castor

display_name: castor

chiroptera

display_name: chiroptera

deadwood

display_name: deadwood

ecosystem-engineer

display_name: ecosystem-engineer

flying-arthropods

display_name: flying-arthropods

habitat-heterogeneity

display_name: habitat-heterogeneity

nature-conservation

display_name: nature-conservation

wetland

display_name: wetland


en:
This dataset contains the population evolution of a pest and its biocontrol agent in terms of presence proportion at gall level and absolute number of insects.

The study area extends from the Cuneo region (Piedmont, Italy) to southern Switzerland. In order to provide a complete range of data covering the entire process from the pest arrival to complete biological control by its natural enemy T. sinensis, a space-for-time substitution approach has been adopted so as to create a temporal gradient of the epidemic stages over the whole study area.

The southernmost Swiss sites roughly represent the arrival and establishment of the pest without the presence of the natural enemy, the central ones the early epidemic stage and the epidemic peak, whereas the northern ones the end of the epidemic with the beginning of the biocontrol. The Italian ones represent the beginning of the equilibrium between the two population as well as the situation with stable T. sinensis populations on the long term.

These data are used in the paper entitled: Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions

en: biological-control castanea-sativa dryocosmus-kuriphilus pest-invasion population-evolution southern-switzerland torymus-sinensis

envidat

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

en: EnviDat: the Environmental Data Portal

educ

en: Education, culture and sport

en:
Data has been collected from summer 2011 to 2016.

Main reasons for NA values:

-	D. kuriphilus still not arrived in the site.
-	No galls were found during the field sampling.
-	Site has not been sampled in the correspondent year because of time, weather or money constrains.
-    T. sinensis not found in the galls

Variable description:

Year = sampling year.

Coordinate, elevation, slope, aspect = average site characteristics.

ts_arrival = T. sinensis arrival year.

dk_arrival = D. kuriphilus arrival year.

dk_parasitism_by_ts = D. kuriphilus parasitism rate by T. sinensis. Formula: (no. of T. sinensis/ no. of D. kuriphilus chambers) * 100 at gall level.

dk_infestation = Percentage of attacked buds. Formula: no. of attacked buds / no. of total developed buds*100 (as presented in Kotobuki et al. 1985) at shoot level.

ts_per_bud = number of alive T. sinensis individual calculated at bud level.

dk_per_bud = number of alive D. kuriphilus individual calculated at bud level.

dk_pot_per_bud = overall number (dead and alive) D. kuriphilus individual calculated at bud level.

total_gall_nr_per_bud = average gall number per bud.

chamber_per_galls = average D. kuriphilus chamber number per gall.

For details see the submitted publication with the same name

References
Kotobuki, K., Mori, K. and Sato, Y. (1985) ‘2 methods to estimate the tree damage by chestnut gall wasp Dryocosmus-kuriphilus’, Bulletin of the fruit tree research station A (Yatabe), 2(12), pp. 29–36.

biological-control

display_name: biological-control

castanea-sativa

display_name: castanea-sativa

dryocosmus-kuriphilus

display_name: dryocosmus-kuriphilus

pest-invasion

display_name: pest-invasion

population-evolution

display_name: population-evolution

southern-switzerland

display_name: southern-switzerland

torymus-sinensis

display_name: torymus-sinensis


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). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.
Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0° Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels.
As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.

en: climate-change-impacts hydrology snow snowfall water-cycle

en: Snow and the water cycle in a changing climate

en: Synthesis report chapter

en: DORA Persistent URL

format: HTML

media_type: text/html

climate-change-impacts

display_name: climate-change-impacts

hydrology

display_name: hydrology

snow

display_name: snow

snowfall

display_name: snowfall

water-cycle

display_name: water-cycle


en:
 Important
This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry "Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.." The entry can be found under this link <https://www.envidat.ch/#/metadata/meteo-stillberg> and with this DOI <https://doi.org/10.16904/envidat.396>.

en: atmospheric-conditions precipitation radiation temperature-profiles wind

en: Long-term meteorological and snow station at 2090 m a.s.l., Stillberg, Davos, Switzerland (1975 - present)

en: This data access is outdated and has been replaced with an updated version (see description).

en: DATA ACCESS

en: This description is outdated and has been replaced with an updated version (see description).

en: README

en: This overview plot is outdated and has been replaced with an updated version (see description).

en: Overview plot

format: PNG

media_type: image/png

en: This site photo is outdated and has been replaced with an updated version (see description).

en: Site photo 1

en: Site photo 2

en: Site photo 3

en: This dataset is outdated and has been replaced with an updated version (see description).

en: Latest data from Stillberg climate station

en: STB2 CSV

atmospheric-conditions

display_name: atmospheric-conditions

precipitation

display_name: precipitation

radiation

display_name: radiation

temperature-profiles

display_name: temperature-profiles

wind

display_name: wind


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

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

The datasets consist of two folders organized in zip format:

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

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

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

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

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

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

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

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

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

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

envidat

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

en: EnviDat: the Environmental Data Portal

en: Reproducibility Dataset for CRYOWRF v1.0

educ

en: Education, culture and sport

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

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

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

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

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

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

alps

display_name: alps

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

mesoscale-modelling

display_name: mesoscale-modelling

snow-atmosphere-interaction

display_name: snow-atmosphere-interaction


en:
This dataset contains the fire events, drivers and auxillery datasets analyzed in the study "Compounding preconditions of wildfires vary in time and space within Europe" of Miller et al. (2025) (DOI: 10.1038/s43247-025-02955-1).
A Jupyter-Notebook explaining the fire event datasets and code to reproduce the figures of Miller et al. (2025) are available on gitlab (see https://gitlab.com/jumi26/wildfire-drivers-in-europe).

en: driver europe event-analysis forest-fire mountains observations

en: Wildfire events and drivers across Europe

en:
01_fire_events_and_drivers: contains the fire event metrics files and in the grid subdirectory drivers and burned area information
02_utest_results: contains the results for different u-test experiments, i. e. fire vs. no-fire for (a) entire region (*both*-files), (b) fire vs. no-fire for mountain regions (*1*-files), (c) fire vs. no-fire for no-mountain regions (*0*-files) and (d) mountain vs. non-mountain on fire days (*mr_nonmr_new*-files)
03_random_forest_results: contains for each region (first two letters) and season (second three letters), the variable importance and accuracy metrics.
04_timelags_datasets: contains seasonal averages for the timelags of fire drivers on fire days and on non-fire days.
figures: contains figures of the publication, which can be derived from the code available in the git-repository https://gitlab.com/jumi26/wildfire-drivers-in-europe.git
mountain_regions_mask: contains file to reproduce mountain regions

driver

display_name: driver

europe

display_name: europe

event-analysis

display_name: event-analysis

forest-fire

display_name: forest-fire

mountains

display_name: mountains

observations

display_name: observations


forest-canopy-layer

en: This dataset provides a LiDAR-based classification of the forest canopy layer for selected Swiss cantons. Combining an area-based and tree-centred approach, forest areas are categorised into four canopy layering classes that differentiate between single-layered and multi-layered canopies.

en: forest-canopy forest-management forest-structure lidar mountain-forest nfi

en: Forest Canopy Layer

title_for_slug: forest-canopy-layer

en: Forest Canopy Layer (class 1: single-layered; class 2: probably single-layered; class 3: probably multi-layered; class 4: multi-layered).

format: TIFF

media_type: image/tiff

en: QGIS QML file with stored layer style definitions for the ForestCanopyLayer.tif.

en: Validation data from the Independent Forester Assessment (IFA). x- /y-coordinate (CH1903+ LV03) and layer (1: single-layered; 2: multi-layered; -999: NA).

forest-canopy

display_name: forest-canopy

forest-management

display_name: forest-management

forest-structure

display_name: forest-structure

lidar

display_name: lidar

mountain-forest

display_name: mountain-forest

nfi

display_name: nfi


forest-canopy-layer-code

en:
R script for large-scale mapping of forest canopy layering. The approach combines tree-based and area-based methods, using airborne laser scanning point clouds together with individual tree detection (ITD) information (tree height and position) to distinguish between single- and multi-layered forests. The output is a raster layer at 10m x 10m resolution, encoded as follows:
1: single-layered (class 1): Both point cloud and ITD approaches classify the forest as single-layered.
2: probably single-layered (class 2): ITD classifies the forest as single-layered, while the point cloud suggests multi-layered.
3: probably multi-layered (class 3): ITD classifies the forest as multi-layered, while the point cloud suggests single-layered.
4: multi-layered (class 4): Both point cloud and ITD approaches classify the forest as multi-layered.
Next to the R script, we also provide an example data set. The LAZ files in the example dataset are from the nationwide ALS data and are provided by the Federal Office of Topography swisstopo in accordance with its open data policy (swisstopo, 2022a, 2019). The detected individual trees were identified using Dalponte and Coomes' (2016) algorithm on a spike-free vegetation height model (cell size 0.5m). The provided forest mask corresponds to the NFI forest layer definition by Waser et al. (2015). For details and mentioned references, we refer to the related publication by Bast et al. (under revision; JAG).

en: forest forest-management itd lidar mountain-forest protective-forest vhm

en: Forest Canopy Layer Code

title_for_slug: forest-canopy-layer-code

forest

display_name: forest

itd

display_name: itd

protective-forest

display_name: protective-forest

vhm

display_name: vhm


distributed-acoustic-sensing-brienz

en:
This dataset contains the Distributed Acoustic Sensing (DAS), radar detection data used for training and result analysis in the GRL paper titled `Automatic Monitoring of Rock-Slope Failures Using Distributed Acoustic Sensing and Semi-Supervised Learning`.

The DAS dataset (both waveform and cross-spectral density matrices), extracted features, labeled dataset, two trained models (feature extraction model and xgboost classification model), scripts to reproduce the whole training and classification processes, and a notebook to replicate the result analysis part are provided under the MIT license. To provide a reasonable data size, we chunked the raw data to a few hundred channels which we used in our project.

Abstract:
Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi-supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million cubic meters on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground-truth labeling. The proposed algorithm is capable of distinguishing between rock-slope failures and background noise, including road and train traffic, with a detection precision of over 95%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal-to-noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

en: early-warning landslide machine-learning natural-hazard rockfall seismic

envidat

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

en: EnviDat: the Environmental Data Portal

en: Distributed Acoustic Sensing Brienz

title_for_slug: distributed-acoustic-sensing-brienz

en: Data access

early-warning

display_name: early-warning

landslide

display_name: landslide

machine-learning

display_name: machine-learning

natural-hazard

display_name: natural-hazard

rockfall

display_name: rockfall

seismic

display_name: seismic


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

Project overview: https://www.wsl.ch/en/projects/life-at-chilly-temperatures-a-collection-of-microorganisms-from-extreme-habitats/

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

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

educ

en: Education, culture and sport

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

active-layer

display_name: active-layer

arctic

display_name: arctic

bacteria

display_name: bacteria

biobank

display_name: biobank

biodiversity

display_name: biodiversity

collections

display_name: collections

conservation

display_name: conservation

extreme-habitat

display_name: extreme-habitat

fungi

display_name: fungi

glaciers

display_name: glaciers

permafrost

display_name: permafrost

plastisphere

display_name: plastisphere

swiss-alps

display_name: swiss-alps


en: This dataset consists of data related to capillary rise experiments performed with neutron radiography. There are 4 videos of capillary rise experiments as well as the files used to perform the inverse fitting with Hydrus. The videos show the upward flow of water in glass columns filled with sand and snow or sand, gravel, and snow. The videos show the 2D evolution of the unitless optical density with time. The Hydrus files were used to fit the parameter values of the Mualem-van Genuchten model. The experiments were performed at the Paul Scherrer Institute (PSI) in Villigen, Switzerland.

en: measurements pore-water snow soil transport water

en: Capillary rise rise experiments in snow using neutron radiography

en: Videos of the capillary rise experiments

en: Description of the videos

en: The Hydrus files used for the inverse fitting of the hydraulic conductivity.

en: Descriptions of the nomenclature, Hydrus files, and videos.

measurements

display_name: measurements

pore-water

display_name: pore-water

snow

display_name: snow

soil

display_name: soil

transport

display_name: transport

water

display_name: water


en: The dataset contains measured values of soil liquid water content, matric potential, and soil texture of 40 soil samples in Davos, Switzerland. The measurements were used for determining van Genuchten parameter values through fitting of water retention curves, pedotransfer functions, and inverse fitting with Hydrus-1D.

en: alpine glide-snow-avalanche lwc matric-potential measurements soil soil-classification soil-texture

en: Soil measurements of Seewer Berg and Davos

en: Detailed information of each file in the dataset

en: Coordinates of each soil location, location of the snow profiles, and location of the snow liquid water content measurements

en: Hydrus files used for the inverse fitting

en: Daily averaged values used for the direct fitting

en: Texture data of the soil locations

en: Complete list of the van Genuchten parameter values for each soil and method

alpine

display_name: alpine

glide-snow-avalanche

display_name: glide-snow-avalanche

lwc

display_name: lwc

matric-potential

display_name: matric-potential

soil-classification

display_name: soil-classification

soil-texture

display_name: soil-texture


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

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

educ

en: Education, culture and sport

en: A condensed instruction for running the presented simulations.

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

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

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

bioclimatic-variables

display_name: bioclimatic-variables

climate-change

display_name: climate-change

example-applications

display_name: example-applications

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-management

display_name: forest-management

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-migration

display_name: species-migration

treemig

display_name: treemig


c-n-p-release-treeline

en:
1. Chemical characteristics of litter layer sampled along elevation gradient across treeline ecotones in the Khibiny mountains (birch) and South Urals (spruce)
2. Release of C-N-P during decomposition at 5°C and 15° studied in an incubation experiment with microlysimeters
3. Data on microbial biomass C-N-P, potential activity of C-N-P requiring enzymes in the litter layer
4. Mineralization of 13C labeled glucose-6 phosphate, microbial 13C uptake, and net P immobilization of released P to identify microbial processing of labile organic P

en: biogeochemical-cycling carbon-cycle climate-change enzymatic-activities litter-decomposition microbial-biomass nutrient-fluxes treeline-ecosystems

en: C-N-P-Release Treeline

title_for_slug: c-n-p-release-treeline

en: Litter layer characteristics, C-N-P-release during decomposition, microbial C-N-P, enzyme activity, mineralization of 13C-labeled glucose-6-phosphate from samples collected across two treelines

biogeochemical-cycling

display_name: biogeochemical-cycling

carbon-cycle

display_name: carbon-cycle

enzymatic-activities

display_name: enzymatic-activities

litter-decomposition

display_name: litter-decomposition

microbial-biomass

display_name: microbial-biomass

nutrient-fluxes

display_name: nutrient-fluxes

treeline-ecosystems

display_name: treeline-ecosystems


en:
The data supports the paper "Local and landscape factors shape alpha and beta trophic interaction diversity in urban gardens". It provides:
(1) the host-natural enemy interaction networks and network metrics between cavity-nesting bees and wasps and their natural enemies,
 (2) the explanatory variables reflecting urban intensity, temperature, habitat amount and management intensity.
(3) the questionnaires for assessing management intensity.
(4) a thesaurus for the variable names.
(5) a readme file.
(6) the R script used for the analyses.
The data is part of the BetterGardens project. It was sampled in 85 allotment and home gardens in the ctiy of Zurich in 2015 using standardized trapnests.

en: bees cavity-nesting food-webs host-enemy-interactions hymenoptera management-intensity trap-nest urban-ecology urban-ecosystems urbanization wasps zurich

en: Data on interaction diversity in urban gardens

en: Not used: data of the study

en: Variable names and their description.

en: Thesaurus

en: Host-enemy interaction data, used to compute the interaction diversity metrics

en: Raw data nests

en: Read me file

en: Not used: readme

en: Cleaned script to reproduce the analyses

en: Script for the analyses v1.0

en: Not used: raw questionnaires

en: Results from the management intensity questionnaires anonymised. IDs are randomized and don't match those in the study data csv

en: Questionnaires management intensity anonymised

en: The csv. file contains the response variable "interaction diversity" (idiv), and the values of the predictors used in the paper. The "id" column indicates the garden ID, each row in the dataset correspond to a garden and each column to the response (idiv), an explanatory variable or a descriptor (name, Lat, Long). See the thesaurus and the readme file for extended information on the variables.

en: Data of the study

en: Script for the analyses v2.0

bees

display_name: bees

cavity-nesting

display_name: cavity-nesting

food-webs

display_name: food-webs

host-enemy-interactions

display_name: host-enemy-interactions

hymenoptera

display_name: hymenoptera

management-intensity

display_name: management-intensity

trap-nest

display_name: trap-nest

urban-ecology

display_name: urban-ecology

urban-ecosystems

display_name: urban-ecosystems

urbanization

display_name: urbanization

wasps

display_name: wasps


chelsacerra-daily

en:
CHELSA is a mechanistic downscaling model that links large-scale atmospheric conditions with local topographic factors to produce very high-resolution climate data. It includes commonly used climate variables for impact modeling, such as air temperature, precipitation, humidity, solar radiation, wind speed, and derived variables.
CHELSAcerra daily is a high resolution climate dataset for air-temperatures generated with the CHELSA downscaling model using the Copernicus regional reanalysis for Europe (CERRA).
Daily summaries are provided for: Daily Mean Near-Surface Air Temperature

en: air-temperature climate europe maximum-temperature minimum-temperature reanalysis

envidat

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

en: EnviDat: the Environmental Data Portal

en: CHELSAcerra-daily

title_for_slug: chelsacerra-daily

educ

en: Education, culture and sport

en: Daily Mean Near-Surface Air Temperature

en: Daily Mean Near-Surface Air Temperature 1985

en: Daily Mean Near-Surface Air Temperature 1986

en: Daily Mean Near-Surface Air Temperature 1987

en: Daily Mean Near-Surface Air Temperature 1988

en: Daily Mean Near-Surface Air Temperature 1989

en: Daily Mean Near-Surface Air Temperature 1990

en: Daily Mean Near-Surface Air Temperature 1991

en: Daily Mean Near-Surface Air Temperature 1992

en: Daily Mean Near-Surface Air Temperature 1993

en: Daily Mean Near-Surface Air Temperature 1994

en: Daily Mean Near-Surface Air Temperature 1995

en: Daily Mean Near-Surface Air Temperature 1996

en: Daily Mean Near-Surface Air Temperature 1997

en: Daily Mean Near-Surface Air Temperature 1998

en: Daily Mean Near-Surface Air Temperature 1999

en: Daily Mean Near-Surface Air Temperature 2000

en: Daily Mean Near-Surface Air Temperature 2001

en: Daily Mean Near-Surface Air Temperature 2002

en: Daily Mean Near-Surface Air Temperature 2003

en: Daily Mean Near-Surface Air Temperature 2004

en: Daily Mean Near-Surface Air Temperature 2005

en: Daily Mean Near-Surface Air Temperature 2006

en: Daily Mean Near-Surface Air Temperature 2007

en: Daily Mean Near-Surface Air Temperature 2008

en: Daily Mean Near-Surface Air Temperature 2009

en: Daily Mean Near-Surface Air Temperature 2010

en: Daily Mean Near-Surface Air Temperature 2011

en: Daily Mean Near-Surface Air Temperature 2012

en: Daily Mean Near-Surface Air Temperature 2013

en: Daily Mean Near-Surface Air Temperature 2014

en: Daily Mean Near-Surface Air Temperature 2015


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.

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

educ

en: Education, culture and sport

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

en: Plant range map at Hengduan scale

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

en: Hengduan 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: Eurasia plant family richness map

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

en: Eurasia SR and heteorgeneity

en: The Heterogeneity matrics in the Hengduan mountain scale

en: HDM heterogeneity scales

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:
List of key debris flow variables from the WSL Illgraben monitoring station (2023) such as occurrence date and time, peak flow depth, peak flow velocity, total volume and bulk density.
This table contains values based on our current analysis methods. The list will be updated annually after each debris flow season, and as our methods continue to improve, individual values may change slightly in the future.

en: debris-flow force-plate illgraben magnitude monitoring-station

en: Illgraben debris-flow characteristics 2023

debris-flow

display_name: debris-flow

force-plate

display_name: force-plate

illgraben

display_name: illgraben

magnitude

display_name: magnitude

monitoring-station

display_name: monitoring-station


climarctic-seasons

en: This dataset includes measurements of potential microbial functions (potential extracellular enzyme activities and microbial functional genes abundance) and soil physico-chemical properties (pH, nutrients, texture, etc) in biocrusts and underlying mineral soils in High-Arctic tundra near Ny-Ålesund (Svalbard) during each season along a toposequence.

en: arctic greenhouse-gases microbial-biodiversity microbial-functions soil

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: Climarctic seasons

title_for_slug: climarctic-seasons

en:
Samples description
•	sample number: sample list number (S1 to S126)
•	season: season during which the samples were collected
•	location: one of the two location where the samples were collected in the vicinity of Ny Alesund on Svalbard
•	slope site: sites where the samples were collected along the toposequence (upslope = top of the slope, midslope = middle of the slope, downslope = bottom of the slope)
•	soil layer: depth layer where the sample were collected (biocrust = 0-2 cm and mineral soil = 5-10 cm)
•	replicate: replicated sampled (3 replicates per site)
Environmental variables
•	pH = pH value of the soil
•	sand = proportion of sand of the soil [%]
•	silt = proportion of silt of the soil [%]
•	clay = proportion of clay of the soil [%]
•	GWC = Gravimetric Water content in the soil [%]
•	OM = Organic Matter content in the soil [g]
•	N = Total Nitrogen content in soil [mg/g]
•	C = Total Carbon content in soil [mg/g]
•	C/N = ration of C content over N content [ratio]
•	NO3 = Nitrate content in soil [mg/kg]
•	NO2 = Nitrite content in soil [mg/kg]
•	NH4 = Ammonium content in soil [mg/kg]
•	Ptot = Total Phosphorus content in soil [mg/kg]
•	Pavail = Available Phosphorus content in soil [mg/kg]
•	PO4 = Phsphate content in soil [mg/kg]
•	CO2 = CO2 fluxes from soil [mmol CO2 m-2 d-1]
•	CH4 = CH4 fluxes from soil [umol CH4 m-2 d-1]
•	N2O = N2O fluxes from soil [umol CH4 m-2 d-1]
•	GPP = Gross Primary Production [mmol CO2 m-2 d-1]
•	ER = Ecosystem respiration [mmol CO2 m-2 d-1]
•	NEE = Net Ecosystem Exchange [mmol CO2 m-2 d-1]
•	glucoOM = glucosidase enzyme activity [mmol h-1 gOM-1]
•	23 xyloOM = xylosidase enzyme activity [mmol h-1 gOM-1]
•	chitOM = N-acetyl-glucosaminidase (chitinase) enzyme activity [mmol h-1 gOM-1]
•	phosOM = phosphatase enzyme activity [mmol h-1 gOM-1]
•	leuOM = leucine-aminopeptidase enzyme activity [mmol h-1 gOM-1]
•	PPOM = Polyphenol Peroxidase enzyme activity [mmol h-1 gOM-1]
•	POOM = Polyphenol Oxidase enzyme activity [mmol h-1 gOM-1]
•	30. Bac16SOM = Bacterial 16S gene abundance [copies gOM-1]
•	31. BacamoAOM = Bacterial amoA gene abundance [copies gOM-1]
•	32. ArchamoAOM = Archaeal 16S gene abundance [copies gOM-1]
•	33. nifHOM = nifH gene abundance [copies gOM-1]
•	34. nirSOM = nirS gene abundance [copies gOM-1]
•	35. nosZOM = nosZ gene abundance [copies gOM-1]
•	36. nxrBOM = nxrB gene abundance [copies gOM-1]
•	37. mcrAOM = mcrA gene abundance [copies gOM-1]
•	38. pmoAOM = pmoA gene abundance [copies gOM-1]

en: Climarctic seasons dataset Envidat.xlsx

arctic

display_name: arctic

greenhouse-gases

display_name: greenhouse-gases

microbial-biodiversity

display_name: microbial-biodiversity

microbial-functions

display_name: microbial-functions

soil

display_name: soil


en:
The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.

Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height > 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0.
The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.html#links). High vegetation is also derived from the classification and the calculated object height from the point cloud.
Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.

An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.

The work is published in:

en: avalanches davos dischma photogrammetry snow-depth-distribution snow-hydrology

envidat

en: EnviDat: the Environmental Data Portal

en: Snow depth mapping by airplane photogrammetry (2017 -  ongoing)

educ

en: Education, culture and sport

en: Snowdepth maps

format: GeoTIFF

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology


en: LiDAR and PD radar data as well as camera videos of the 30 June 2022 debris flow at the Illgraben (Switzerland).

en: 3d-lidar analysis debris-flow illgraben pulse-doppler radar

en: Dataset for "Sorting and Surging 3D LiDAR and Pulse-Doppler Radar Analysis of a Natural Debris Flow"

analysis

display_name: analysis

pulse-doppler

display_name: pulse-doppler

radar

display_name: radar


en:
This repository hosts the experimental data accompanying our publication, “On the compressive strength of weak snow layers of depth hoar,” featured in the Journal of Glaciology.

-Compressive Strength Data:
Measurements of the compressive strength for 92 artificially grown weak snow samples.
-Microstructural CT Data:
CT-derived microstructural information for each sample, including:
Density
Specific surface area
Connectivity density
Correlation lengths
Anisotropy
-Additional CT Data:
Parent Sample Variability: CT data used to assess the variability of the parent samples.
Temporal Evolution: CT data capturing the evolution within the artificial weak layers.
Reference Data: Information on the reference CT data sourced from the RHOSSA and MOSAiC campaigns.

en: depth-hoar microstructure slab-avalanche snow snow-mechanics strength

en:
CT Reference Data: Contains sample names, positions, evaluated regions and microstructural parameters based on data from the RHOSSA and MOSAiC campaigns.
MOSAiC:
https://mosaic-portal.org/data/open-mcs/vessel/polarstern/sn_microct/sn_microct/exdata/
https://doi.org/10.1594/PANGAEA.952794
RHOSSA:
https://doi.org/10.16904/envidat.151

en: Experimental Data: Contains results of the compression experiments and microstructural parameters.

en: Parent sample variability: Contains the CT data used to estimate the parent sample variability.

en: Time series data: Contains the CT data of upper slab, weak layer and lower slab used to evaluate the temporal evolution within the artificial samples.

depth-hoar

display_name: depth-hoar

microstructure

display_name: microstructure

slab-avalanche

display_name: slab-avalanche

snow

display_name: snow

snow-mechanics

display_name: snow-mechanics

strength

display_name: strength


en: These datasets and code support research on the community assemblage of aquatic macroinvertebrates in ephemeral microhabitats, specifically water-filled tree holes (WTH), in three biogeographically and climatically distinct old-growth forests: Temperate-Mediterranean (France), Neotropical (Brazil) and Palaeotropical (India). By integrating standardised biological sampling, detailed environmental measurements and spatial analysis, the dataset allows for an in-depth examination of how local habitat conditions and spatial configuration influence taxonomic and functional beta-diversity patterns. The data capture the variation in species composition and ecological traits in 35 WTH per forest site and are particularly important for understanding how environmental filtering and spatial processes shape beta-diversity in natural forest ecosystems.

en: beta-diversity community-assembly environmental-factors functional-diversity metacommunity spatial-factors taxonomic-diversity water-filled-tree-holes

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: Spatial factors shape taxonomic and functional beta-diversity in water-filled tree holes in different biogeographical regions

en: This Excel file contains metadata that provides detailed information about all other files included here. It describes the ten accompanying datasets, outlining the type of data each contains, the variables used, their descriptions, and the units of measurement (where applicable).

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the Brazilian forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the French forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the Indian forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This dataset contains values of taxonomic and functional β-diversity for each study site, along with their respective components: species richness difference vs. turnover (taxonomic), and functional richness difference vs. functional turnover (functional). Used to generate Fig. 1.

en: This dataset includes physical and environmental characteristics of water-filled tree holes (WTHs), such as water depth, height, opening size, hole origin and orientation, circumference at breast height, WTH type, and water volume. It also contains physico-chemical parameters, nutrient concentrations (ammonium, nitrogen, phosphate, sulfate, chloride), CDOM, Chlorophyll-A, turbidity, and dry organic matter (FPOM, CPOM, total).

en: This dataset describes the animal community composition sampled from 35 water-filled tree holes (WTHs) per site. It includes abundance and taxonomic identification (to morphospecies level) of aquatic macroinvertebrates and Chordata.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the Brazilian forest plot.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the French forest plot.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the Indian forest plot.

en: This dataset includes results from variation partitioning analysis used to assess the relative contributions of environmental and spatial factors to taxonomic and functional β-diversity and their components across biogeographic regions. It contains site-level values for pure and shared effects of predictors. Used to generate Fig_2.

en: Code to do the analysis and figures of the paper.

beta-diversity

display_name: beta-diversity

community-assembly

display_name: community-assembly

environmental-factors

display_name: environmental-factors

functional-diversity

display_name: functional-diversity

metacommunity

display_name: metacommunity

spatial-factors

display_name: spatial-factors

taxonomic-diversity

display_name: taxonomic-diversity

water-filled-tree-holes

display_name: water-filled-tree-holes


individual-detected-trees

en: This dataset provides individual detected trees for selected Swiss Cantons (Appenzell Outer-Rhodes - AR, Grisons - GR, Nidwald - NW, Obwald - OW and St. Gall - SG), including the derived diameter at breast height and the derived NFI development stage. Data show the Individual detected trees (ITD) with the x- and y-positions of each detected tree based on a spike-free vegetation height model based on nationwide LiDAR data. The shapefile contains the following attributes: tree height [m] (Height), estimated diameter at breast height [cm] (BHD), and the NFI development stage (Ent; 1: young growth/thicket (< 12 cm); 2: pole timber (12-30 cm); 3: young timber (31-40 cm); 4: medium timber (41-50 cm); 5: old timber (> 50 cm).

en: development-stage forest-management forest-structure itd mountain-forest nfi

envidat

en: EnviDat: the Environmental Data Portal

en: Individual Detected Trees

title_for_slug: individual-detected-trees

educ

en: Education, culture and sport

development-stage

display_name: development-stage

forest-management

display_name: forest-management

forest-structure

display_name: forest-structure

itd

display_name: itd

mountain-forest

display_name: mountain-forest

nfi

display_name: nfi


en:
This repository hosts the experimental data accompanying our publication "The influence of snow microstructure on the compressive mechanical properties of weak snowpack layers" feautured in Acta Materialia.
- Experimental Data and microstructural descriptors:
Full dataset used for the main analysis of the paper. Contains the measured mechanical properties, µCT parameters, further analysis, etc. The column "ID" (e.g. 363) links this data with the 3D µCT dataset (e.g. e0000363).
- 3D Data of the segmented µCT scans:
Full dataset containing the raw segmented µCT scans in the MetaIO format. They can be opened using the SimpleITK package (`pip install SimpleITK`).
Note: the Files can be idenfied by their scan number in the filename, (e.g, e0000363)
 `import SimpleITK as sitk `
 `img = sitk.ReadImage("e0000363_crop_seg.mhd") `
 `array = sitk.GetArrayFromImage(img) ` shape: [z, y, x]
`array_xyz = np.transpose(array, (2, 1, 0))` (Optional) for array axes in [x, y, z]
 `spacing = img.GetSpacing() `         voxel size (x,y,z)

en: lab-experiment snow snow-avalanche snow-mechanics snow-microstructure

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: Dataset containing both the experimental results and microstructural parameters.

lab-experiment

display_name: lab-experiment

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-microstructure

display_name: snow-microstructure


en: Countrywide vegetation height models (VHM) were generated for Armenia based on Copernicus Sentinel-2 imagery within the framework of the FORACCA project. “The Forest Restoration and Climate Change in Armenia” (FORACCA) project is funded by the Swiss Agency for Development and Cooperation (SDC). The project is implemented by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL) in collaboration with Zoï Environment Network as well as the Forest Alliance of Armenia. A Convolutional Neural Network (CNN) model was trained in Switzerland to estimate the maximum vegetation height at the spatial resolution of the Sentinel-2 pixel of 10 m. Vegetation heights from the spatially higher-resolved VHM Lidar NFI were used as reference data for the CNN training. Then, the model was spatially transferred and VHMs for Armenia were generated annually based on available Sentinel-2 imagery from May – September of the respective year. Further details on the dataset and model to create the Sentinel-2 VHMs can be found in the paper Jiang et al. (2023, https://doi.org/10.1016/j.srs.2023.100099). Contains modified Copernicus Sentinel data.

en: armenia cnn foracca forest remote-sensing sentinel-2 vegetation-height

en: Sentinel-2 Vegetation Height Model Armenia

armenia

display_name: armenia

cnn

display_name: cnn

foracca

display_name: foracca

forest

display_name: forest

remote-sensing

display_name: remote-sensing

vegetation-height

display_name: vegetation-height


en:
This dataset contains several useful components for **Swiss Electricity system modeling**:
1. **High-voltage Swiss grid**: 2019 snapshot of the high-voltage (>150kV) grid composed of :
- Nodes: names, voltage, position.
- Lines: ends nodes, resistance, power capacity.
- Transformers: ends nodes, power capacity, resistance, tap ratio.

2. **Hydropower topology** : Representative description of Swiss reservoirs and plants (2019) composed of:
- Plants: Lower reservoir, Upper reservoir, pump/turbine, debit, power capacity.
- Reservoirs: Volume, altitude, position, canton.

3. **Inflows** into all the reservoirs: hourly time series post-processed from PREVAH model outputs from 2009 to 2019.

4. **Wind production per node**: COSMO-1 data processed and scaled to match SFOE annual production.

5. **Solar irradiance maps projected onto tilted surfaces**: hourly time series for 35° and 65° tilts, azimuth 180°. (same method as [Frischholz, Y. (2025)](https://www.doi.org/10.16904/envidat.632))

6. **Processed ERA5 (historical), ECMWF SEAS5 (seasonal forecast) and ECMWF TIGGE (short-term forecast)**:
- ERA5 Land: For inflow forecast model: historical runoff, snow depth, temperature and total precipitation per catchment (hourly, 2009-2019). For residual demand forecast model: historical temperature, u-v wind, ssrd per country (CH, AT, DE, FR, IT, hourly 2015-2019)
- ECMWF SEAS5: For inflow forecast model: same variables as ERA5 (yearly forecast starting from every 1st day of every month in 2019). For residual demand forecast model: same variables as ERA5 (yearly forecast starting from every 1st day of every month in 2019).
- ECMWF TIGGE: For inflow forecast model: same variables as ERA5 (two weeks forecast starting from every 1st day of every week in 2019). For residual demand forecast model: same variables as ERA5 (two weeks forecast starting from every 1st day of every week in 2019).

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More details can be found on *README.txt*.

en: electricity-grid forecast-models hydropower meteorological-data renewable-energy reservoir-inflow solar-irradiance

en: Swiss electricity system modeling -- HV grid - hydropower topology and inflows - renewable productions - past and forecast meteorological data

en: Plants and reservoirs description, with associated hourly inflows (2009 - 2019).

en: Solar irradiance maps projected onto tilted surfaces: hourly time series for 35° and 65° tilts, azimuth 180°. (same method as [Frischholz, Y. (2025)](https://www.doi.org/10.16904/envidat.632))

en: Description of Swissgrid high-voltage (>150kV) grid, including lines, transformers and nodes (snapshot of the grid in 2019) .

en: ERA5 Land, Seasonal forecast and short-term forecast meteorological data to train inflow and residual demand forecast models.

en: Data structure and description.

electricity-grid

display_name: electricity-grid

forecast-models

display_name: forecast-models

hydropower

display_name: hydropower

meteorological-data

display_name: meteorological-data

renewable-energy

display_name: renewable-energy

reservoir-inflow

display_name: reservoir-inflow

solar-irradiance

display_name: solar-irradiance