en: hicar dischma fsm2trans fsm2 snow-hydrology snowpack

en: Input data March 2017 HICAR 100m resolution

en: Input data May 2017 HICAR 250m resolution

en: Input data December 2016 HICAR 100m resolution

en: input data november 2016 COSD

en: Input data June 2017 COSD

en: Input data May 2017 COSD

en: input data october 2016 COSD

en: Input data July 2017 HICAR 100m resolution

en: Input data April 2017 HICAR 50m resolution

en: Input data July 2017 HICAR 250m resolution

en: Input data January 2017 COSD

en: Input data June 2017 HICAR 100m resolution

en: Input data May 2017 HICAR 100m resolution

en: Input data February 2017 HICAR 250m resolution

en: Input data January 2017 HICAR 100m resolution

en: Input data January 2017 HICAR 250m resolution

en: Input data July 2017 HICAR 50m resolution

en: Available input files for running with FSM2trans

en:
snowheight output of validation days (LiDAR days) for all simulation runs
- 20.03.2027
- 31.03.2017
- 17.05.2017

en: Input data December 2016 HICAR 50m resolution

en: Input data June 2017 HICAR 250m resolution

en: Input data April 2017 COSD

en: Input data December 2016 HICAR 250m resolution

en: Input data February 2017 COSD

en: Input data November 2016 HICAR 250m resolution

en: Input data April 2017 HICAR 250m resolution

en: Input data January 2017 HICAR 50m resolution

en: Input data March 2017 HICAR 50m resolution

en: Input data February 2017 HICAR 100m resolution

en: Input data April 2017 HICAR 100m resolution

en: Input data october 2016 HICAR 50m resolution

en: Input data May 2017 HICAR 50m resolution

en: Input data June 2017 HICAR 50m resolution

en: Input data November 2016 HICAR 50m resolution

en: Input data March 2017 HICAR 250m resolution

en: Input data november 2016 HICAR 100m resolution

en: Input data February 2017 HICAR 50m resolution

en: Input data october 2016 HICAR 100m resolution

en:
time series of daily averages of the whole domain for each simulation run
- rainfall
- snowfall
- runoff
- snowmelt
- temperature

en: Input data October 2016 HICAR 250m resolution

en:
 DESCRIPTION
The dataset is used to run snowpack simulations with various forcing data at different resolutions with the Flexible Snow Model (FSM2oshd). A dynamical downscaling model (HICAR) and a semi-statistical downscaling approach (COSD) are used to downscale COSMO data to resolutions of 250 m, 100 m and 50 m. Simulations are run with the operational snow cover model and a model extension including wind- and gravity-induced snow redistribution (FSM2trans).

 Input Data
This paragraph describes the input data used to run simulations with FSM2trans

 HICAR
The HICAR input data is created by the High-resolution Intermediate Complexity Atmospheric Research model. The input data is available for different resolutions (250m, 100m and 50m) for a domain covering complex terrain in the Swiss Alps (Dischma)

 COSD
The COSD data was statistically downscaled within the OSHD framework to different resolutions (250m, 100m, 50m) for a domain covering complex terrain in the Swiss Alps (Dischma)

 Simulation files
For the Simulations, a landuse file and a namelist file with the parametrizations need to be supplied to the model

 simulation output
This simulation output that is used for the analysis of the study is provided.

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: FSM2trans snowpack simulations with HICAR input


maintainer: Merin Reji Chacko

en: invertebarates trophic-networks metaweb vascular-plants vertebrates switzerland food-webs

state: active

type: dataset

en: trophich 0.1

media_type: application/zip

en: Deprecated version from before the first round of review at Scientific Data

format: ZIP

en: trophiCH 1.0

en: Most current version of the dataset, after revision round 1 at Scientific Data.

en:
Environmental pressures on species can cascade within food webs and even extend beyond individual ecosystems to interconnected systems at large spatial scales. To facilitate the exploration of these dynamics, we constructed trophiCH: a data-based national trophic meta-food web (henceforth the metaweb), that includes vertebrates, invertebrates, and vascular plants within Switzerland's national boundaries, and drawing from literature published between 1862 and 2024. Our comprehensive dataset catalogues 1,112,073 trophic interactions involving 23,151 species and 125 feeding guilds (e.g., detritivores, fungivores, etc). Interactions were primarily documented at the species level from the literature. Additional species-level interactions were inferred by resolving coarser taxonomic records (e.g., using “species A feeds on genus B” to infer interactions between species A and species within genus B) using taxonomic and habitat co-occurrence information.
We provide seven datasets: 1) the metaweb, 2) the taxa checklist, 3) the data source meta-dataset, 4) the list of generalist basal and predator families and polylectic species with citations and 5) a dataset with citations for the inferences of missing predators, 6) a dataset with citations for the parallel inference of diets from similar species and 7) a list of other existing metawebs. The empirical dataset is 00_empirical_metaweb.csv. To reproduce the final metaweb (01_final_metaweb.csv), start the R Project file: trophich.Rproj. We provide five scripts, accompanying functions, and the raw data required to run these scripts to reproduce the taxonomic expansion and validation of the datasets. In the first script (01_inferring_interactions.R), we infer interactions using genus and family level interactions and for basal feeding groups (see Methods: Taxonomic expansion). In the second script (02_inferring_interactions_special_cases.R), we infer further interactions for a few special cases with detailed explanations. In the third script (03_metaweb_comparisons.Rmd), we provide the statistical comparisons between our metaweb and other empirical metawebs as an R Markdown document. We additionally provide a a Python Jupyter Notebook document, outlining the error validation of the data extraction process (04_error_validation.ipynb and an accompanying .html file). Finally, we provide a script to reproduce figure 1 from the associated data paper (05_metaweb_summary_figure_1.R).
This is a first step towards a comprehensive food web for Switzerland, but data gaps remain (see Reji Chacko et al 2024, Fig.1C). We encourage addition of new datasets to this metaweb and welcome any collaborations to contruibute to future versions of this dataset. Please contact: merin.rejichacko@gmail.com.

food-webs

display_name: food-webs

invertebarates

display_name: invertebarates

metaweb

display_name: metaweb

switzerland

display_name: switzerland

trophic-networks

display_name: trophic-networks

vascular-plants

display_name: vascular-plants

vertebrates

display_name: vertebrates

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: trophiCH v1 - a food web for Switzerland


maintainer: Milojevic

en: return-periods reservoir-inflow extreme-values alpine-precipitation low-flow

en: R codes for Extreme Values Analysis

en: Zip file containing R codes for running extreme value analyses for high precipitation and inflow events as well as code for determining return levels of low inflow and low precipitation events

format: SERVICE

en: Daily Luzzone Reservoir Inflow 01-01-2000 to 31-12-2019

en: Daily total water inflow to Luzzone reservoir. Natural inflow (LUZ_inflow_raw) is back-calculated based on daily water level in reservoir (removing hydropower inputs and spills). Linear interpolation between daily values is used to bring all inflow values into positive space (LUZ_inflow_intrp).

en:
 Summary

* Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland

* R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis

 Data
The dataset included here is the "natural" reservoir inflow for the Luzzone reservoir.
Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html).

 Codes
R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels:

* GEV (Generalized Extreme Value)

* GPD and GPDd (Generalized Pareto Distribution including declustered version)

* eGPD (extended Generalized Pareto Distribution)

* MEV (Metastatistical Extreme Value)

alpine-precipitation

display_name: alpine-precipitation

extreme-values

display_name: extreme-values

low-flow

display_name: low-flow

reservoir-inflow

display_name: reservoir-inflow

return-periods

display_name: return-periods

en: Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs


maintainer: EnviDat Support

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

en: Data plot of the field station (SCF)

media_type: application/pdf

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

format: PDF

en: Forest station data (SCB)

media_type: text/csv

en: Data file.

format: CSV

en: Further details on the data repository

en: Meta data documentation.

en: Site photo of the forest station (SCB)

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

en: Field station data (SCF)

mimetype: text/csv

en: Data plot of the forest station (SCB)

en: Site aerial photo (Schänis)

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

en: Site photo of the field station (SCF)

en: Site photo of the field station from above (viewing downwards the slope).

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Schänis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Schänis is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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: Schänis, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1998 onwards


wind-lidar-davos-wolfgang

title_for_slug: wind-lidar-davos-wolfgang

en: wolfgangpass wind profiles lidar raclets

en: Wind LIDAR data Wolfgang

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

mimetype: application/zip

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

lidar

display_name: lidar

profiles

display_name: profiles

raclets

display_name: raclets

wind

display_name: wind

wolfgangpass

display_name: wolfgangpass

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Wind LIDAR Davos Wolfgang


maintainer: Christoph Marty

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

en: DORA Persistent URL

media_type: text/html

format: HTML

en: Synthesis report chapter

mimetype: application/pdf

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.

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: Snow and the water cycle in a changing climate


en: acoustic-emissions snow-avalanche sintering viscous-deformation snow snow-mechanics snow-failure fiber-bundle-model

en: fiber-bundle-model-for-snow-failure

en: Data underlying Figures in publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted to "Geophysical Research Letters"

en:
This dataset contains modeled and experimental results for laboratory snow failure experiments and the concurrent acoustic emissions signatures for different loading rates. For modelling the snow failure we used a fiber bundle model that includes sintering and viscous deformation.

The data underlay the figures in the publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted for publication to "Geophysical Research Letters".

acoustic-emissions

display_name: acoustic-emissions

fiber-bundle-model

display_name: fiber-bundle-model

sintering

display_name: sintering

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-mechanics

display_name: snow-mechanics

viscous-deformation

display_name: viscous-deformation

en: Fiber Bundle Model for snow failure and concurrent Acoustic Emissions


en: topsoil-interflow preferential-flow overland-flow celerity runoff-plot tracers velocity rainfall-simulation alptal studibach

format: XLS

en:
Description:
This dataset comprises the time series of overland flow (OF) and topsoil interflow (TIF) discharge, and tracer concentrations during artificial rainfall simulation experiments at two large (>80 m2) trenched runoff plots in the Studibach catchment in the Alptal, a typical pre-Alpine headwater catchment in Switzerland.
One plot is located in a natural clearing in an open mixed forest and the other in a grassland. Together, they represent the dominant land cover types in the region.
We applied streamwater to the surface of the plots using sprinklers and added tracers after OF and TIF had reached steady state. Deuterium enriched water was applied to the surface of the plots via the sprinklers, while Uranine and NaCl were applied as a line tracer at multiple distances from the trench. NaBr was injected into the topsoil at ~20 cm depth. Samples of overland flow and topsoil interflow were collected for several hours after tracer application, while the sprinklers continued to apply water to the surface. The runoff was collected into self-made "Upwelling Bernoulli Tubes" and the water level inside these tubes was measured using pressure sensors (DCX-22-CTD, Keller Druck, Switzerland) at a 1-min resolution during the day and 5-min resolution during the night. Uranine concentrations and electrical conductivity (EC) were recorded at a 1-minute interval. Samples for deuterium and Bromide were analysed in the lab.
The celerity of overland flow and topsoil interflow was determined during another experiment by temporarily adding more water to the surface of the plots at different distances from the trench after steady state conditions had been reached. The overland flow and topsoil interflow discharge were again measured using the Upwelling Bernoulli Tubes and pressure transducers.

alptal

display_name: alptal

celerity

display_name: celerity

overland-flow

display_name: overland-flow

preferential-flow

display_name: preferential-flow

rainfall-simulation

display_name: rainfall-simulation

runoff-plot

display_name: runoff-plot

studibach

display_name: studibach

topsoil-interflow

display_name: topsoil-interflow

tracers

display_name: tracers

velocity

display_name: velocity

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: Flow and tracer data for overland flow and topsoil interflow during rainfall simulation experiments in the Studibach catchment - Alptal - Switzerland


en: fish senegal teleo edna metabarcoding

en: Data access

en: Please use cyberduck to download for the moment

en:
Monitoring fish diversity through eDNA metabarcoding along the Casamance river

In 2018, water samples were collected in 13 locations along the Casamance in Senegal. Samples were taken either from a boat or by filtering from the side of the river. At each station, two filtration replicates were performed using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 30 min, during which approximately 30 liters of water were filtered through each filtration capsule. After filtration, the water inside the capsules was removed, and the capsules were filled with 50 ml of conservation buffer for preservation at room temperature. We followed strict contamination control protocols throughout both the fieldwork and laboratory processes, adhering to the guidelines of Valentini et al. (2016). To prevent contamination, each sample was processed using disposable gloves and single-use filtration equipment. We use primers for eukaryote and fish. The MiSeq Reagent Kit v3 (2x75 bp) (Illumina, San Diego, CA, USA) was used for paired-end sequencing at a theoretical sequencing depth of 200,000 reads per sample.

Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

edna

display_name: edna

fish

display_name: fish

metabarcoding

display_name: metabarcoding

senegal

display_name: senegal

teleo

display_name: teleo

educ

en: Education, culture and sport

en: Environmental DNA Freshwater Senegal Casamance 2018


en: climate-change snow-avalanche ramms climada avalanche risk

en: By using hazard scenarios based on CH2018 climate projections and the RAMMS avalanche model, we generated large-scale hazard indication maps for future avalanche hazards. By employing the open-source probabilistic risk assessment platform CLIMADA, along with building data and vulnerability functions, we estimated risks for the present time and two future time frames: mid-century (2060) and the end of the century (2085). An uncertainty and sensitivity analysis complemented the study to account for potential fluctuations in model assumptions. The data contains the hazard information and data used for the Climada risk analysis.

avalanche

display_name: avalanche

climada

display_name: climada

climate-change

display_name: climate-change

ramms

display_name: ramms

risk

display_name: risk

snow-avalanche

display_name: snow-avalanche

envidat

en: EnviDat: the Environmental Data Portal

en: Assessing the impacts of climate change on snow avalanche-induced risk in alpine regions


en: edna crispr-dx dna-metabarcoding catchment mammals metabarcoding national-park

en:
Folder containing a folder for eDNA and species_inventory (traditional monitoring), respectively.
eDNA folder:
CRISPR_classifications.csv: CRISPR-Dx classifications per extract (and PCR pool).
metabarcoding_seqs_all_cleaned.csv: all DNA sequences from metabarcoding after removing all sequences found in the negative controls.
species_inventory: species per catchment reported by the Swiss National Park, based on traditional monitoring including. visual surveys and other opportunistic records. 2: known presence in 2022, 1: maybe present in 2022, 0: not recorded in 2022

media_type: text/plain

en: Read me file with information about files within this repository

format: TXT

en:
 General description

Data associated with the publication "CRISPR-Dx and metabarcoding perform similarly for monitoring mammals with eDNA on the catchment level in high-alpine ecosystems". The data set contains the cleaned metabarcoding reads per site/catchment, the CRISPR-Dx detection per species and site, the site coordinates and a species checklist obtained from the Swiss National Park with the species known to occur at the sampling time point in the respective catchments.

 Authors
Flurin Leugger, Martina Lüthi, Michel Schmidlin, Sarah Marie-Antoine Turnheer, Zacharias Kontarakis and Loïc Pellissier

 Contact
flurinleugger@gmail.com
loic.pellissier@usys.ethz.ch

 Project description
We collected eDNA samples in the Swiss National Park in September 2022 using VigiDNA filters. We extracted the samples in the clean lab at ETH using a modified extraction protocol of Pont et al. (2018). We used the Mamm01 primer (Tabarlet et al. 2018) for metabarcoding and for CRISPR analysis. We applied CRISPR-Dx for eight species occurring in the Swiss National Park. Additionally, we compared the eDNA detections to species inventories per catchment obtained from the Swiss National Park. See publication for more details.

 Folders
 catchments
Shape file containing the catchments sampled in the Swiss National Park.
 metadata
Folder containing files with overview of site names and extract number. Second file with metadata per site (e..g., sampling date). Column called "EVS" (Environmental sample) is the key between the two files.
 data
Folder containing a folder for eDNA and species_inventory (traditional monitoring), respectively.
 eDNA folder
CRISPR_classifications.csv: CRISPR-Dx classifications per extract (and PCR pool).
metabarcoding_seqs_all_cleaned.csv: all DNA sequences from metabarcoding after removing all sequences found in the negative controls.
 species_inventory
Species per catchment reported by the Swiss National Park, based on traditional monitoring including. visual surveys and other opportunistic records. 2: known presence in 2022, 1: maybe present in 2022, 0: not recorded in 2022

catchment

display_name: catchment

crispr-dx

display_name: crispr-dx

dna-metabarcoding

display_name: dna-metabarcoding

mammals

display_name: mammals

national-park

display_name: national-park

en: Application of CRISPR-Dx and metabarcoding to catchment-level eDNA for terrestrial mammal detection


en: wind radiation precipitation atmospheric-conditions temperature-profiles

en: Site photo 2

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

en: Overview plot

media_type: image/png

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

format: PNG

en: Site photo 1

en: Site photo 3

en: Latest data from Stillberg climate station

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

en: DATA ACCESS

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

en: STB2 CSV

en: README

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

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>.

atmospheric-conditions

display_name: atmospheric-conditions

precipitation

display_name: precipitation

radiation

display_name: radiation

temperature-profiles

display_name: temperature-profiles

wind

display_name: wind

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


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

en: Forest station data (NOB)

en: Data file.

en: Site photo of the forest station (NOB)

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

mimetype: image/jpeg

en: Site photo of the field station (NOF)

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

en: Data plot of the forest station (NOB)

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

en: Data plot of the field station (NOF)

en: Site aerial photo (Novaggio)

en: Site aerial photo from both Novaggio stations. Source: Google. 2016. Site aerial photo from both Novaggio stations. Retrieved November 15, 2016, from https://www.google.ch/maps

en: Field station data (NOF)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Novaggio in Switzerland where one station is located within a natural deciduous forest stand (NOB) with Turkey oak (_Quercus cerris_; 70 yrs), sweet chestnut (_Castanea sativa_; 90 yrs) and silver birch (_Betula pendula_; 70 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Novaggio is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

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


maintainer: Zhao

en: austin urban-land-use planning driving-factors land-dynamics

en: Potential driving factors of urban transformations of Austin over 25 years

en: In this study, the Austin metropolitan area, Texas, U.S., one of the fastest urban transformations and transformations regions, is selected to test the hypothesis that spatial planning and policies are important factors of urban transformations. Despite ample previous work in understanding the interactions between human and urban form transformation at specific areas, the actual interventions and outcomes of planning and policies (e.g., ‘smart growth’) on urban forms have been poorly measured. In this study, the potential influencing factors of urban transformations of Austin over 25 years were selected and collected.

austin

display_name: austin

driving-factors

display_name: driving-factors

land-dynamics

display_name: land-dynamics

planning

display_name: planning

urban-land-use

display_name: urban-land-use

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.


maintainer: Markus Didion

en: growing-stock section-wise-diameter branchwood allometry stemwood tree-volume

en: Revised version of the initial submission StemBranchwood_Didionetal_initialsubmission.pdf after review.

en: dataset with section-wise stem diameter and branchwood volume measurements. For column definitions see Column_definitions.pdf.

en: definitions of data in columns contained in the dataset Stem&Branchchwood_2024-02-29.txt

en: Updated dataset of outdated_Stem&Branchchwood_2024-02-29.txt, including corrections to the assignment of tree species to NFI main species and location information for two additional sites.

en: manuscript submitted for publication.

en: additional background information on this work

en:
The data presented here were prepared for publication in form of a data paper. The dataset presents an update of the information in Didion M, Herold A, Vulovic Z, Nitzsche J, Stillhard J (2022) Datasets for deriving functions for the stem- and branchwood volume in the Swiss National Forest Inventory. EnviDat. doi:https://www.doi.org/10.16904/envidat.358. The new dataset is based on an alternative, more accurate and comprehensive dataset, that was identified as the source of the dataset in Didion et al. 2022. It includes additional data, including newly digitized information.
The dataset includes section-wise stem measurements on 40’349 felled individual trees and empirically derived coarse (diameter >= 7 cm) and fine branch volume of 27’297 and 18’980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range. The dataset has undergone quality controls and due to its origin from 768 plots of the Experimental Forest Management project long-term consistency is assured.

allometry

display_name: allometry

branchwood

display_name: branchwood

growing-stock

display_name: growing-stock

section-wise-diameter

display_name: section-wise-diameter

stemwood

display_name: stemwood

tree-volume

display_name: tree-volume

en: A dataset of 40000 trees with section-wise measured stem diameter and length and volume of branches from across Switzerland


en: prokaryotes fungi soil-microbiome microbial-biodiversity latitudinal-gradient soil-mulitfunctionality

en: 16S_Han et al_EUgradient.xlsx

en: Diversity of prokaryotes (16S gene sequencing) containing: 1) ASV (Amplicon Sequence Variants) table: abundance of ASV in each sample and 2) Taxonomy table: taxonomic classification of each ASV.

en: ITS_Han et al_EUgradient.xlsx

en: Diversity of fungi (ITS region sequencing) containing: 1) ASV (Amplicon Sequence Variants) table: abundance of ASV in each sample and 2) Taxonomy table: taxonomic classification of each ASV.

en: Metadata, soil properties and soil microbial functions for each sites across the European gradient.

en: The diversity and network complexity of prokaryotic and fungal communities and their relationships with soil multifunctionality (SMF) – an integrative index for C-, N- and P-cycling functions – was investigated along a 3,000-km latitudinal transect across Europe (37° to 62°N), spanning biomes from Mediterranean drylands, temperate to boreal forests.

fungi

display_name: fungi

latitudinal-gradient

display_name: latitudinal-gradient

microbial-biodiversity

display_name: microbial-biodiversity

prokaryotes

display_name: prokaryotes

soil-microbiome

display_name: soil-microbiome

soil-mulitfunctionality

display_name: soil-mulitfunctionality

en: Soil microbial diversity and multifunctionality across European biomes


maintainer: Frank Techel

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

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

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

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

en:
The data set contains the re-analyzed (or quality-checked) regional avalanche danger levels (D_QC) for Switzerland.

D_QC relates to dry-snow avalanche conditions only. Measuring the avalanche danger level D is not possible; forecast, nowcast, and hindcast assessments of D are judgments by humans interpreting data. However, combining several pieces of information indicating the same D, it can be expected that it is more likely that D_QC represents the avalanche conditions well.

For the **forecasting seasons 2001/2002 until 2019/2020**, the approach to obtain D_QC is described in detail in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).

For the **forecasting seasons 2020/2021 and later**, D_QC is derived using the following approach:
1. *Combination of forecast (D_forecast) and nowcast (D_nowcast)*: If there was only one assessment available by an observer after a day in the field for a region, and if D_forecast = D_nowcast --> D_QC = D_forecast.
2. *Combination of several nowcast assessments (D_nowcast)*: If two (or more) observers agreed (or majority opinion) in their (independent) assessments of D_nowcast after a day in the field in the same warning region. --> D_QC = D_nowcast.
3. *Hindcast analysis (D_hindcast)*: In Switzerland, avalanche forecasters re-evaluate all situations when D = 4 (high) or D = 5 (very high) were either forecast, should have been forecast, or when forecasters discussed given one of these two levels but had not given them. Generally, two forecasters assess each situation. In these cases, D_QC = D_hindcast. The hindcast analysis, only available since the forecasting season 2020/2021, replaces what was step (2) in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).

All other cases - ties in case of (1) or (2), no new information from the warning region in question, or if no D_hindcast was available - are not considered quality-checked, and are, thus, not contained in the data set.

In addition to D_QC, the file contains information on the elevation and aspect, where D_QC likely prevails.
- The indicated elevation is the mean of the respective elevations in (1), (2), or (3). At danger level 1 (low), when no elevation is indicated in the Swiss forecast, a value of 1500 m is set.
- For the four cardinal aspects N, E, S, and W, a value of 1 means that there was agreement that D was reached in this aspect and a value of 0 means that there was agreement that D was not reached in this aspect. Intermediate values correspondingly mark disagreements in the assessments.

avalanche-bulletin

display_name: avalanche-bulletin

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forecast

display_name: avalanche-forecast

avalanche-forecasting

display_name: avalanche-forecasting

verification

display_name: verification

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Re-analyzed regional avalanche danger levels in Switzerland


en: fen biodiversity-monitoring liverworts sphagnum vegetation-survey mosses raised-bog cover observer-bias

en: Releve data

en: The CSV file contains all releves considered in the mentioned study. The columns include plot identification, survey period, survey year, habitat category of the surveyed object, vegetation type of the plot, anonymized identification of the observer, observer expertise category, the observers survey experience, scaled mean indicator values (i.e., light, nutrients, reaction, temperature and wetness), counts for the four groups (all species, liverworts, mosses exkl. Sphagnum spp. and Sphagnum spp.), the size categories (small, medium, large) and the modified cover classes (NA, r, +, 1, 2, 3-5).

en:
This dataset contains bryophyte species count data and information about the observers bryophyte expertise for 2950 relevés conducted from 2011 to 2023 on 10-m2 plots in a long-term monitoring program in Switzerland. Plots were situated in raised bogs and fens of national importance, which were distributed across the whole country. The majority of the plots is represented by two relevés as sites are revisited every six years. The dataset was used in the paper mentioned below to test if species richness estimates differed among categories of observer expertise.

Moser T, Boch S, Bedolla A, Ecker KT, Graf UH, Holderegger R, Küchler H, Pichon
NA, Bergamini A (2025) Greater observer expertise leads to higher liverwort species richness in bryophyte surveys. _Journal of Vegetation Science_.

biodiversity-monitoring

display_name: biodiversity-monitoring

cover

display_name: cover

fen

display_name: fen

liverworts

display_name: liverworts

mosses

display_name: mosses

observer-bias

display_name: observer-bias

raised-bog

display_name: raised-bog

sphagnum

display_name: sphagnum

vegetation-survey

display_name: vegetation-survey


en: crown-condition forest-vitality forest-heath defoliation icp-forests

en: Country level forest cabon dataset.csv

en: Country-level forest carbon data extracted from the common reporting format (CRF) tables (Table 4a therein, relating to forests) based on the National Inventory Reports provided by all countries signatory to the Conference of Parties (https://unfccc.int/ghg-inventories-annex-i-parties/2024).

en: Climate and air pollution data.csv

en:
Linked plot level climate and air pollution data:
Climate data were obtained from two primary sources: SPEIbase v2.9, provided by the Spanish National Research Council (CSIC) ( https://spei.csic.es/) and the ERA5-Land dataset, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (https://cds.climate.copernicus.eu/dataset).
Air pollution data were sourced from the European Monitoring and Evaluation Programme (EMEP)(https://emep.int/).

en: Data Visulaization of the ICP Forest Defoliation 1990-2022

en:
This data set has been obtained after processing original data from the ICP Forests data infrastructure. It includes "clean" defoliation and symptoms data from 19 countries over the period 1990-2022 after removing dubious cases of e.g. species attribution, dubious coding, long-standing dead trees. It is based on ICP Forests Level I plots.
Defoliation is the relative loss (shed or not formed) of tree needles / leaves in relation to a hypothetical fully foliated optimum and is visually assessed using a sliding scale recorded in 5% steps (from 0%= no defoliation to 100%=standing dead tree).
Occurrence of s^Symptoms attributable to damaging agents (e.g., insects, fungi, drought, hail, fire, direct action of men…) on each tree and associated to defoliation are also included in this data set.
This data set includes a total of 2’688’512 observations from 219’854 trees in 12’104 plots.
In addition there are two associated datasets included here relating to Forest Carbon data at the national level and Plot level Climate and Air Pollution variables which were used in publications based on the primary dataset. The Forest Cabon data was extracted from the common reporting format (CRF) tables provided by all countries signatory to the Conference of Parties (https://unfccc.int/ghg-inventories-annex-i-parties/2024). Climate data were obtained from two primary sources: SPEIbase v2.9, provided by the Spanish National Research Council (CSIC) ( https://spei.csic.es/) and the ERA5-Land dataset, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (https://cds.climate.copernicus.eu/dataset). Air pollution data were sourced from the European Monitoring and Evaluation Programme (EMEP)(https://emep.int/).

crown-condition

display_name: crown-condition

defoliation

display_name: defoliation

forest-heath

display_name: forest-heath

forest-vitality

display_name: forest-vitality

icp-forests

display_name: icp-forests

en: ICP Forests Defoliation and Symptoms Data Set


maintainer: Tobias Wechsler

en: hydropower gis integral-water-management decision-making ecosystem-protection

en: Indicators

en: The indicators capture supporting ecosystem functions.

format: SHP

en:
 A spatial dataset and tool to simultaneously assess hydropower potential and ecological potential of the Swiss river network (Version 2016)

 Introduction
The steadily growing demand for energy and the simultaneous pursuit of decarbonisation are increasing interest in the expansion of renewable energies worldwide. In Switzerland, various funding projects have been launched to promote technologies in the field of renewable energies and their application as quickly as possible. With the introduction of a funding instrument in 2009, the number of projects submitted to produce renewable energies increased rapidly. The applications for small hydropower plants (≤ 10 MW) were correspondingly numerous. However, the assessment of the environmental impact and its comparison with hydropower importance is still not standardized.
To provide a basis for decision-making, a methodology was developed to determine the overall hydropower potential of a region. A detailed assessment of each river reach, and the systematic and holistic assessment of small hydropower projects at a regional scale are combined here. The assessment of a river reach is conducted at the river space (i.e., the river and adjacent areas) and at the surrounding landscape level. The HYDROpot_integral methodology was developed as part of Carol Hemund's dissertation (2012) at the University of Bern. It allows the evaluation of river reaches holistically, regarding ecological, social, economic and cultural criteria. As a second part of the overall project, the theoretical hydropower (or hydraulic) potential was calculated for the entire river network, which complemnets the spatial assessment. In particular, it is possible to classify river reaches into those that are more suitable for hydropower production (=”use”) and those that are more suitable for protection.

 Material and method
The HYDROpot_Integral method was developed and tested on the basis of cantonal and national data (Hirschi et al. 2013). The method relies on 73 geodata sets. This holistic assessment is the key element of the entire assessment procedure. Its aim is to quantify the importance of the ecosystem functions in terms of services. The river network (GWN07) is divided into reaches of about 450m and for each reach two study units are defined. The river space (RS) records the ecosystem functions of the water body and the nearby riparian area. The length of the RS is 315 m on average in Switzerland and a maximum of 450 m, whereas the width is based on the FOEN definition (BWG 2001: 18f) and varies between 7-107 m. The surrounding landscape (SLS) is the second survey unit that records the ecosystem functions of the surrounding area over a range of 21 m to 321 m. The SLS is calculated over three times the RS width. The length of the SLS is identical to the length of the RS. The ecosystem functions are divided into three types: regulating (service A), cultural (service B) and provisioning (service C) functions. Accordingly, the assessment of the functions is divided into three parts and three values are assigned to each river reach. The more functions there are and the greater their performance, the higher these values are and the more important the corresponding functions are. Hence, these values quantify the importance of the ecosystem functions and the ecological, cultural and economic ecosystem services of each river reach. The concatenation of ecosystem services results in a value (ABC) that can occur in 125 different versions due to the chosen five-level value scale; i.e. each digit of the three-digit number sequence can be assigned a value between 1 and 5. Each of the 125 combinations, and thus each river reach, has its own characteristics determined by the assessments of the three function types. To record the suitability, the combinations are ranked according to their ecological, cultural and economic ecosystem services. These rules mean that the combination that is most suitable for hydropower production at minimum cost in terms of ecological and cultural ecosystem services and has a high economic potential is ranked first; rank 125 indicates the highest ecological and cultural ecosystem services and the lowest economic services and is therefore most suitable for protection. A river reach that is excluded from hydropower use due to legislation, a so-called priority reach, is given rank 126 from the outset and specially marked. A more detailed description of the methods can be found in Hirschi et al. 2013 [Link]. The dataset presented here presents the latest state of the HYDROpot_integral methodology applied at the national level. Only national data that is easily accessible was used in the preparation of the dataset. The cantonal data, such as renaturation and revitalization, would have to be requested by each canton individually and was excluded here. The nationwide value synthesis was made with R.
A list of data sources can be found here [link to text file]
A list of all parameters can be downloaded here [link to PDF and text files]

 Dataset description
Data is presented as a single shapefile. It contains the river network and all assessment results obtained with HYDROpot_Integral.

 Changes in the methodology compared to the original method (Hirschi et. al 2013)

 * RS_A11 Ecomorphology: recorded for the whole of Switzerland and zero values equated with NA;
individual cantons such as Zug and St. Gallen have no mapped values according to the modular
concept of the federal government, Valais and Graubünden only the main valleys, Ticino and Fribourg not
completely (BAFU 2009).
 * RS_A14 Renaturation and revitalization data: not centrally available at the time of data collection.
centrally available, therefore values in GR were equated with NA.
 * RS_A15 Dilution ratio at wastewater treatment plants (WWTPs) for discharges: Zero values
equal to NA.
 * RS_A20 Water flow: use WASTA (2013) with hydroelectric power plants (> 300 kW) under
Federal control and dams serving hydroelectricity (Dam, as of 2013).
 * RS_C05 Synoptic hazard maps: are cantonally managed at the time of data collection,
Values in GR are marked with a 5 so that the systematics in the decision tree is not affected.
is affected.
 * Water quality (RS_A15, RS_A16, RS_A17, RS_A18, RS_A19): for the evaluation of the function type.
A Nature, it is important whether the median of the five values is less than or equal to 3 in total. This
evaluation is based on the decision tree for evaluating GR (Hirschi et al. 2013:22).
Therefore, an evaluation of the station data is made where critical and possible river segments
with poor quality (median less than 3) exist. Only two longer and one short
sections in Switzerland receive a lower median than 3 for water quality.
 * SLS_B06 Visibility: For 99 percent of the river segments (30,733 of 31,062) in the canton of
Bern (2015 reduced version), the landscape area is considered to be visible. Due to this high number
of sections, a large number of viewpoints in the layer of Swisstopo and
the computation time and computability in ArcGIS, the landscape area is classified as generally viewable
(equal to 1).
16 Method
Additional indicators were added (see Appendix B.2):
 * SLS_A21 Dissection
 * SLS_A22 Forest areas
 * SLS_B03 Hiking trails
 * SLS_B10 Residential and vacation homes
 * SLS_B11 Tourist infrastructure
 * SLS_C01 Landfill
 * SLS_C03 Infrastructure
 * SLS_C05 Industry
 * SLS_C06 Agricultural land
Not to be added, although present to some extent:
 * SLS_B06 Cultural assets of national importance: here, too, the calculability of the visibility analysis is
for the whole of Switzerland is limited
 * SLS_A15 Legally binding protection and land use planning: the individual river sections are not
clearly designated, i.e. no geodata exist
The following data are also not supplemented, as they are cantonal data:
 * SLS_A10 Cantonal nature reserves
 * SLS_A16 Forest reserves
 * SLS_A17 Cantonal inventories and contractually protected areas

decision-making

display_name: decision-making

ecosystem-protection

display_name: ecosystem-protection

gis

display_name: gis

hydropower

display_name: hydropower

integral-water-management

display_name: integral-water-management


forest-canopy-layer

title_for_slug: forest-canopy-layer

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

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

format: OCTET

media_type: image/tiff

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

format: TIFF

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

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.

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

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Forest Canopy Layer


en: soil-moisture snow-profile avalanche glide-snow-avalanche avalanche-activity

en: This file explains all provided datasets, variables and their units.

en: This file contains all soil measruements (liquid water content and matric potential) recorded at the reference location.

en: This file contains the aggregated data from manually recorded snow profiles in the reference location, 100m northwest of the Seewer Berg slope and in release areas of glide-snow avalanches in the Seewer Berg slope.

en: This file contains the coordinates of the reference location and the sensors in the Seewer Berg slope as a shapefile.

en: This file contains SNOWPACK simulations (snow height, air temperature, rain) at a virtual station in the reference location. The virtual station was interpolated between the Weissfluhjoch (WFJ2) and Klosters Madrisa (KLO2) IMIS Stations.

en: This file provides an overview of the dataset, the detailed measurement setup, and points out limitations.

en: This file contains the release areas (polygon) of glide-snow avalanches as a shapefile.

en: This file contains all sensor recordings in the Seewer Berg slope (time interval: 15 minutes).

en: This file contains the daily avalanche activity on Dorfberg as extracted from time-lapse photographs.

en:
This dataset consists of spatio-temporal soil measurements and local snow measurements which were recorded to investigate glide-snow avalanche release in the Seewer Berg slope (Davos, Switzerland) from Nov 2021 to June 2024. Investigations based on this dataset were published in the research article:
Fees A., Lombardo M., van Herwijnen A., Lehmann P., Schweizer J. (2025). The source, quantity, and spatial distribution of interfacial water during glide-snow avalanche release: experimental evidence from field monitoring. The Cryosphere
The dataset consists of:
- Soil measurements (temperature and liquid water content) at soil depths of -5cm, -10cm, and -20cm
- Matric potential at depths of -5cm, -10cm, and -20cm
- Manual snow profiles (Snow height, temperature, liquid water content, bulk density)
- Metadata and manual snow profiles on glide-snow avalanches that released in the Seewer Berg slope
- Snow height, air temperature and rain simulated with SNOWPACK.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

glide-snow-avalanche

display_name: glide-snow-avalanche

snow-profile

display_name: snow-profile

soil-moisture

display_name: soil-moisture

en: Spatio-temporal soil and local snow monitoring in a glide-snow avalanche prone slope above Davos Switzerland


en: drone-data antarctica spatial-variability polar sea-ice snow

en:
Data of snow and sea ice in the McMurdo Sound, October-December 2022. The data was collected as part of the New Zealand Marsden Fund Research Grant 21-VUW-103 "Can Snow Change the Fate of Antarctic Sea Ice?"

The dataset includes raw data of the manual snow and sea ice measurements from snow pits and ice cores (temperature, density, salinity, dO18), measurements of snow water equivalent (SWE), spatial information of snow height (MagnaProbe) and sea ice thickness (EM-31), AWS (air temperature, wind speed, wind direction, relative humidity, pressure), radiations stations (shortwave, longwave, thermal IR, spectral shortwave), differential GPS data (3 fixed stations on different sea ice thicknesses, + 1 rover station for georeferencing UAV measurements), SIMBA buoy temperature (+heated temperature) data (3 buoys during November, 1 buoy for 15 months), UAV data: RGB, thermal IR, broadband albedo, spectral albedo, Chlorophyll-a from ice cores (bottom 10 cm), NIR reflectivity data of snow at 850 nm, and 940 nm (snow surface, profile, ice surface), photographs (1. overview of field sites, 2. for Structure from Motion for surface roughness, 3. macrophotos of snow) surface impurity concentrations, microCT data of snow microstructure, Denoth probe (density) and InfraSnow (specific surface area - SSA).

See the README file in each dataset for detailed information.

antarctica

display_name: antarctica

drone-data

display_name: drone-data

polar

display_name: polar

sea-ice

display_name: sea-ice

snow

display_name: snow

spatial-variability

display_name: spatial-variability

en: Snow on Antarctic Sea Ice - McMurdo Sound 2022


en: field-data well-being urban-landscape artificial-lighting stress

en: 01_Information sheet.pdf

en: This file includes the information sheet provided to participants before they consented to take part in the study.

en: 05_Anonymised dataset.xlsx

en: This file contains data for the manuscript "Randomised trial reveals a mismatch between preferences for and hormonal responses to anthropogenic light colour temperatures".

en: This document includes the questionnaire administered at the second measurement time point (after exposure).

en: 02_Particiants consent form.pdf

en: This file contains the informed consent document that participants signed to participate in the study.

en: This document includes the questionnaire administered at the first measurement time point (before exposure).

en:
This repository contains data related to the field experiment of the WP3 (social impacts) of the ALANeX project. This project is a collaboration between the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and Elektrizitätswerk des Kantons Zürich (EKZ). It has received funding from the WSL programme EXtremes. The overall objective of the ALANeX project was to assess the ecological and social impact of anthropogenic light at night.

We conducted a randomized field experiment to compare the well-being benefits of exposure to streetlights of different correlated colour temperatures (CCT) during a 20-min stay in the centre of Richterswil, Swizerland, to explore:
 - the impact of exposure to streetlamps of different CCT on affect, self-reported stress and cortisol levels
- the differences in impression of artificial light and of the feeling of safety provided by streetlamps based on its CCT.

artificial-lighting

display_name: artificial-lighting

field-data

display_name: field-data

stress

display_name: stress

urban-landscape

display_name: urban-landscape

well-being

display_name: well-being

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


maintainer: Christian Rixen

en: nitrogen-isotope fertilization dwarf-shrubs stillberg treeline tree-rings

en: Metadata Nutrient Addition Stillberg Experimental Design

en: Nutrient addition map of the study area with the investigated plots. Each circle (study block) contains one control plot and two plots that received NPK fertilizer corresponding to 15 (NPK15) or 30 (NPK30) kg N ha-1 a-1. Contour lines specify the elevation (m a.s.l.) in steps of 10 m.

en: Data Nutrient Addition Stillberg

en: This link leads to the figshare repository, which contains all nutrient addition data.

en:
 Background information

The availability of nitrogen (N) and phosphorus (P) is considered to be a major factor limiting growth and productivity in terrestrial ecosystems globally. This project aimed to determine whether the growth stimulation documented in previous short‐term fertilisation trials persisted in a longer‐term study (12 years) in the treeline ecotone, and whether possible negative effects of nutrient addition offset the benefits of any growth stimulation.
Over the course of the 12 study years, NPK fertiliser corresponding to 15 or 30 kg N ha−1 a−1 was added annually to plots containing 30‐year‐old *Larix decidua* or 32‐year-old *Pinus uncinata* individuals with an understorey of mainly ericaceous dwarf shrubs. To quantify growth, annual shoot increments of trees and dwarf shrubs as well as radial growth increments of trees were measured. Nutrient concentrations in the soil were also measured and the foliar nutritional status of trees and dwarf shrubs was assessed.

 Experimental design

Over an elevation gradient of 140 m across the treeline afforestation site Stillberg, 22 locations were chosen that covered the whole range of microenvironmental conditions (*see* Nutrient addition experimental design.png). Half of the blocks included European larch (*L. decidua*) and the other half included mountain pine (*P. uncinata*). Within each block, three plantation quadrats were randomly selected as experimental plots and each plot was assigned to a control (no fertilisation) or to one of two fertiliser dose treatments (15 kg and 30 kg N ha−1 a−1). Treatments were assigned randomly but confined so that the location of fertilised plots within a block was not directly above control plots to avoid nutrient input from drainage. For details about the experiment, *see* Möhl et al (2019).

 Data description

The available datasets contain climate variables (2004-2016), nutrient isotope measurements (2010 & 2016), shrub growth measurements (2004-2016), soil parameter measurements and annual ring and shoot measurements (2004-2016). All data can be found here:
<https://figshare.com/articles/dataset/Twelve_years_of_low_nutrient_input_stimulates_growth_of_trees_and_dwarf_shrubs_in_the_treeline_ecotone/7025858>

dwarf-shrubs

display_name: dwarf-shrubs

fertilization

display_name: fertilization

nitrogen-isotope

display_name: nitrogen-isotope

stillberg

display_name: stillberg

tree-rings

display_name: tree-rings

treeline

display_name: treeline

en: Nutrient addition experiment at the Alpine treeline site Stillberg, Switzerland


maintainer: Chunhong Zhao

en: land-dynamics urban-morphology land-use austin landscape

en: Land use dynamics in Austin since 1992

en: The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006–2011 and 2011–2016. The study also aims to understand to what extent the changes in the built environment (in terms of ‘efficient growth’ versus ‘inefficient growth’) from the 1990s to 2016 in the Austin metropolitan area corresponded with ‘compact and efficient growth’ planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 × 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package.

austin

display_name: austin

land-dynamics

display_name: land-dynamics

land-use

display_name: land-use

landscape

display_name: landscape

urban-morphology

display_name: urban-morphology

en: Land use/cover dynamics in Austin metropolitan area since 1992


en: insects diversity urban-ecology snails spiders urbanization birds arthropods species-abundance-distributions species-richness-models sdms

en: contains the raw data to perform the species richness models of the rare species

en: contains the raw data to perform species distribution models, only for common species

en: contains the raw data to perform the species richness models of all species

en: contains the raw data to perform the species richness models of the hypercommon species

en: abundance and occurrence data of the 1446 species studied, used to investigate species-abundance distributions and species-occurrence distributions

en:
Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.
Data are provided on request to contact person against bilateral agreement.

arthropods

display_name: arthropods

birds

display_name: birds

diversity

display_name: diversity

insects

display_name: insects

sdms

display_name: sdms

snails

display_name: snails

species-abundance-distributions

display_name: species-abundance-distributions

species-richness-models

display_name: species-richness-models

spiders

display_name: spiders

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

en: Ecological properties of urban ecosystems. Biodiversity dataset of Zurich


forest-canopy-layer-code

title_for_slug: forest-canopy-layer-code

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

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).

forest

display_name: forest

forest-management

display_name: forest-management

itd

display_name: itd

lidar

display_name: lidar

mountain-forest

display_name: mountain-forest

protective-forest

display_name: protective-forest

vhm

display_name: vhm

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Forest Canopy Layer Code


en: aerodynamic-entrainment large-eddy-simulation snow-saltation drifting-snow splash saltation-parameterizations

en: Code and input files to reproduce the simulations in Melo et al. (2024), as well as the post-processing scripts used in the analysis.

en:
This dataset includes the necessary files to reproduce the results presented in Melo et al. (2024):
- code version used in the simulations (also available at the institutional [GitLab repository](https://gitlabext.wsl.ch/atmospheric-models/les-lsm) - tag 'r2024')
- input files to run the simulations
- post-processing scripts (MATLAB and FORTRAN)
- selected raw output files
- post-processed results (MATLAB data files).
 	**Note:** Direct model outputs are not provided because of their size, but they can be shared upon request. They can be reproduced by running the code. The post-processed results allow you to reproduce all figures in Melo et al. (2024).

aerodynamic-entrainment

display_name: aerodynamic-entrainment

drifting-snow

display_name: drifting-snow

large-eddy-simulation

display_name: large-eddy-simulation

saltation-parameterizations

display_name: saltation-parameterizations

snow-saltation

display_name: snow-saltation

splash

display_name: splash

en: Understanding snow saltation parameterizations - Reproducibility


greenland-climate-network-gc-net-data

title_for_slug: greenland-climate-network-gc-net-data

en: air-temperature net-radiation greenland climate aws automatic-weather-station

en: GC Net Chart Parameters

media_type: application/json

en: Stations parameters from static bucket

format: JSON

en: Access the latest L1 data

en:
The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial version of L1 data has been archived.

en: 22-Petermann ELA

en: GC Net Config for Stations

en: Stations Config from the static bucket

en: Archived GC-Net Data (deprecated)

en:
The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial versions of L0 and L1 data have been archived.

en:
In Memory of Dr. Konrad (Koni) Steffen

Update October 2022: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).
Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS. Future data versions will be available at: [https://doi.org/10.22008/FK2/VVXGUT](https://doi.org/10.22008/FK2/VVXGUT)
 Background
Starting with a single station in 1991, the Greenland Climate Network (commonly known as GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen, and spanning the Greenland Ice Sheet (GrIS). This first station was "Swiss Camp" or the "ETH-CU" camp (GC-Net station 01) which was used as a field science and education site by Koni for years. The GC-Net was expanded with multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data (see "C-file" below) were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado.

 Overview
Provided in this dataset are the 16 longest running stations in the network, which are spread over a significant area of the GrIS and the majority of the unique climatic zones. From the South Dome high point in the South, to the Western Jakobshavn ablation region in the west, to the Petermann glacier in the North across east of the Northeast Greenland Ice Stream to the east, GC-Net is the longest running climatological record of Greenland.

 The standard GC-Net station consists of:
* Air temperature measurements at 2 heights above the surface
* Temperature and humidity measurements at 2 heights above the surface
* Wind speed and direction measured at 2 heights above the surface
* Sonic distance sounder measurements for 2 snow height and distance of instruments to surface
* Incoming shortwave radiation measurement
* Reflected shortwave radiation measurement
* Net broadband radiation (long- and short-wave) measurement
* Air pressure measurement
Data have often been repatriated in near-real time using one of either the GOES geostationary satellite or the ARGOS polar orbiting satellite transmission system. The stations were visited typically every 1-2 years for maintenance and service, and to download full uncorrupted data directly from the dataloggers. GC-Net stations were visited by Twin Otter equipped with snow skids to land directly on the open-ice at the AWS locations, or by helicopter near the west coast. The AWSs operate on solar and battery power and occasionally lost power during the dark and cold winter months, particularly when the batteries were aging.

 Dataset
This dataset consists of 2 main data levels; Level 0 and Level 1.
Level 0 is the raw data from the dataloggers, historical processing codes, satellite transmissions, and Koni’s personal data archive. Level 0 data (.zip) directories contain subdirectories:
* “C file” - contains the historical processed datafile for each station.
* “Campbell logger files” - contains the raw csv datafiles from the stations’ Campbell Scientific dataloggers since the CR1000 era (~2007-2008 for most stations).
* “Photos” - contains photographs of the station when available marked by year.
Level 1 is the appended, calibrated, cleaned, and quality flagged data. The full processing scheme is open-source and publicly available on the following GitHub repository (please also check GitHub for the latest L1 data):
[GC-Net L1 data on GitHub](https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing "GC-Net-level-1-data-processing")
Level 1 data is provided in the newly described csv-compatible [NEAD format](https://www.envidat.ch/#/metadata/nead "NEAD format").

 Additional Details
Dataset description publication will be forthcoming.
The Geological Survey of Denmark and Greenland (GEUS) has been imperative in the reprocessing and continuity mission of GC-Net. Multiple GC-Net stations have been replaced with updated and upgraded AWS hardware at the same coordinates by GEUS. This effort will ensure continuity of the GC-Net dataset into the future.

air-temperature

display_name: air-temperature

automatic-weather-station

display_name: automatic-weather-station

aws

display_name: aws

climate

display_name: climate

greenland

display_name: greenland

net-radiation

display_name: net-radiation

en: Greenland Climate Network (GC-Net) Data


en: drought-2018 fagus-sylvatica growth-reaction-to-drought-stress leaf-senescence forests european-summer-drought

en: Drought and Beech: 1000 beeches data set

en: The data set comprises data from the Drought and Beech 1000 beeches project. The actual data file with monitoring data of 963 beech trees in northern Switzerland over four years (Drought_and_Beech_1000beeches_Data_v1.0.csv), a metadata file with column descriptions (Drought_and_Beech_1000beeches_Legend_v1.0.csv), and a file defining the different survey periods (Drought_and_Beech_1000beeches_Survey_periods_v1.0.csv).

en: This study investigated multi-year drought impacts on beech forests through a unique large-scale monitoring of 963 individual beech trees, which showed either premature leaf discoloration during the drought in summer 2018 or no visible damage. We conducted the study in two highly drought-affected regions in northern Switzerland and one less drought-affected region located further south. We quantified the development of crown dieback and tree mortality as well as secondary drought damage, i.e. the presence of bleeding cankers and bark beetle infestations, in these trees for three consecutive years. We also determined the impact of several potential climate- and stand-related (predisposing) factors on mortality and drought legacy processes.

european-summer-drought

display_name: european-summer-drought

fagus-sylvatica

display_name: fagus-sylvatica

forests

display_name: forests

growth-reaction-to-drought-stress

display_name: growth-reaction-to-drought-stress

leaf-senescence

display_name: leaf-senescence

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Data on multi-year drought impacts on European beech in northern Switzerland


maintainer: Tobias Jonas

en: sky-view-fraction terrain solar-irradiance forests shortwave-radiation light-availability solar-energy lidar radiation-transfer transmissivity

en: Data Download

en:
The SwissRad10 dataset contains light availability maps at 10 m and hourly resolution across an entire annual cycle with wall-to-wall coverage across the whole of Switzerland.
Areas below and within 25 m of forests include light availability calculated for both leaf-on and leaf-off canopy structure, and terrain only data is available across the full domain.
The variables sky-view factor and direct-beam transmissivity are both included, allowing estimation of both direct and diffuse light at the ground surface.
The dataset was calculated using the detailed synthetic hemispheric image-based ray-tracing Canopy Radiation Model (CanRad) with the latest airborne lidar and terrain surface data from the swissSURFACE3D dataset.

lidar

display_name: lidar

light-availability

display_name: light-availability

radiation-transfer

display_name: radiation-transfer

shortwave-radiation

display_name: shortwave-radiation

sky-view-fraction

display_name: sky-view-fraction

solar-energy

display_name: solar-energy

solar-irradiance

display_name: solar-irradiance

terrain

display_name: terrain

transmissivity

display_name: transmissivity

en: SwissRad10 Hourly light availability maps at 10 m resolution over Switzerland


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

en: Data access

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

climate-change-impact

display_name: climate-change-impact

discharge

display_name: discharge

hydrology

display_name: hydrology

model

display_name: model

water-temperature

display_name: water-temperature

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


maintainer: Dirk Nikolaus Karger

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

en: Reptiles

en: Species distribution models based on different climate predictor groups

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

en: Mammals

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

en: Amphibians

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

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

amphibians

display_name: amphibians

chelsa

display_name: chelsa

climate-variablity

display_name: climate-variablity

generalized-additive-models

display_name: generalized-additive-models

generalized-linear-models

display_name: generalized-linear-models

interannual-climate-variability

display_name: interannual-climate-variability

iucn

display_name: iucn

mammals

display_name: mammals

niche-models

display_name: niche-models

random-forests

display_name: random-forests

range-maps

display_name: range-maps

reptiles

display_name: reptiles

sdm

display_name: sdm

species-distribution-models

display_name: species-distribution-models

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


dataset-on-new-snow-water-equivalent

title_for_slug: dataset-on-new-snow-water-equivalent

en: new-snow precipitation data-assimilation snowfall snow-modelling

en: The .zip file contains all data as described in this repository and associated publication. The .zip file contains a readme file describing its content in detail.

en:
This dataset includes quality-controlled measurements of new snow depth (HN), new snow water equivalent (HNW), snow depth (HS), and snow water equivalent (SWE) from 41 stations located in Switzerland for the period from 2016-09-01 to 2022-08-31.
These data are the basis of the following publication: Magnusson J., Cluzet B., Quéno L., Mott R., Oberrauch M., Mazzotti G., Marty C., Jonas T., 2025, Evaluating methods to estimate the water equivalent of new snow from daily snow depth recordings, Cold Regions Science and Technology, https://doi.org/10.1016/j.coldregions.2025.104435.
Abstract
The water equivalent of new snow (HNW) plays a crucial role in various fields, including hydrological modeling, avalanche forecasting, and assessing snow loads on structures. However, in contrast to snow depth (HS), obtaining HNW measurements is challenging as well as time-consuming and is hence rarely measured. Therefore, we assess the reliability of two semi-empirical methods, HS2SWE and ΔSNOW, for estimating HNW. These methods are designed to simulate continuous water equivalent of the snowpack (SWE) from daily HS only, with changes in SWE yielding daily HNW estimates. We compare both parametric methods against HNW predictions from a physics-based snow model (FSM2oshd) that integrates daily HS recordings using data assimilation. Our findings reveal that all methods exhibit similar performance, with relative biases of less than ~3 % in replicating SWE observations commonly used for model evaluations. However, the ΔSNOW model tends to underestimate daily HNW by ~17 %, whereas HS2SWE and FSM2oshd combined with a particle filter data assimilation scheme provide nearly unbiased estimates, with relative biases below ~5 %. In contrast to the parsimonious parametric methods, we show that the physics-based approach can yield information about unobserved variables, such as total solid precipitation amounts, that may differ from HNW due to concurrent melt. Overall, our results underscore the potential of utilizing commonly available daily HS data in conjunction with appropriate modeling techniques to provide valuable insights into snow accumulation processes. Our study demonstrates that daily SWE observations or supplementary measurements like HNW are important for validating the day-to-day accuracy of simulations and should ideally already be incorporated during the calibration and development of models.
Acknowlegements
These data were recorded by SLF observers and staff members. Their contribution is gratefully acknowledged.

data-assimilation

display_name: data-assimilation

new-snow

display_name: new-snow

precipitation

display_name: precipitation

snow-modelling

display_name: snow-modelling

snowfall

display_name: snowfall

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Dataset on new snow water equivalent


en: radar rainfall illgraben surge-waves debris-flow

en: Radar-based flow height measurement of debris flow at CD28 at Illgraben. Flow height is derived relative to pre-event channel-bed elevation. Sensor location: 46.309677N, 7.633304E

en: Cumulative rainfall measured at CD1 in the Illgraben catchment. Sensor location: 46.283287N / 7.619689E

en:
This dataset contains measurements from two debris flows recorded in 2024 at the Illgraben debris-flow observatory in Switzerland. The first debris flow was recorded on 15 June 2024 and the second debris flow was recorden on 21 June 2024.

This dataset contains the following reseach data for both:
a) Cummulative rainfall measured at CD1 in the catchment.
b) Radar-based flow height of debris flows measured at CD28.

Abstract
In recent years, the destructive impact of debris flows in alpine regions has become increasingly evident. Surge waves within debris flows increase peak discharge and magnify the hazard potential. Hence, understanding the dynamic complexity of debris flows is crucial to mitigate their risk. In order to capture the dynamic processes involved in the formation and interaction of surge waves, it is necessary to obtain distributed observations in the spatiotemporal domain. In this study, we present near-torrent distributed seismic measurements to monitor the Illgraben channel located in the Swiss Alps. With 33 nodal sensors, we detected and tracked surge waves along a 2-kilometer torrent section across the Illgraben fan, which allowed us to gain valuable new insights into the spatial scales relevant for surge wave formation and their long-distance propagation characteristics. We observed erosion-deposition waves that emerged out of the muddy flow tail and propagated with constant velocity along the torrent, reaching meter-scale flow heights within only 100s of meters of flow distance which significantly impact the hazard potential of debris flows. Our results provide valuable observations of surge waves from their formation to their annihilation. We can differentiate flow regimes based on their seismic signature and track them along the torrent, thus mapping the debris-flow evolution in time and space. The observations elucidate large-scale debris flow dynamics thus improving our understanding of hazard potential and the effectiveness of structural countermeasures.

debris-flow

display_name: debris-flow

illgraben

display_name: illgraben

radar

display_name: radar

rainfall

display_name: rainfall

surge-waves

display_name: surge-waves

en: Debris flow observation at Illgraben 2024: Event Data 15 June / 21 June


en: satellite hazard-mapping avalanche snow natural-hazard

en: SPOT6 Avalanche outlines 24 January 2018

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset.

en:
Outlines of 18'737 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 24 January 2018. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping.pdf)

The generation of the data is described in:

Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.

Abstract.
Accurate and timely information on avalanche occurrence are key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability considering accuracy, completeness and reliability of the reported avalanche events is limited. In this study we present the spatial continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12’500 km2).

We tested different satellite sensors available for rapid mapping during a first avalanche period. Based on these experiences, we tasked SPOT6/7 data for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18’737 individual avalanche events, applying image enhancement techniques to analyze regions in cast shadow as well as brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having a unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period.

This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data is of great value to validate avalanche bulletins, complete existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.

Koordinate System: CH1903+ LV95 LN02

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow