en:
**Schweizweite Hinweiskarten des lichtbedingten Verjüngungspotentials (Stand LFI VHM 2024)**
This is a dataset for Swiss forestry, for a description in English please see below.
Il s'agit d'un ensemble de données relatives à la sylviculture suisse. Pour une description en français, voir ci-dessous.
Si tratta di una serie di dati relativi alla silvicoltura svizzera. Per una descrizione in francese, vedere sotto.
Die Karte zeigt, wo die Lichtbedingungen für genügend Höhenzuwachs der Verjüngung (-= 300 cm) der jeweiligen Baumartengruppe ausreichen. Die Karte basiert auf gutachtlich erhobenen Feldaufnahmen in 39 Forstrevieren, verteilt über die ganze Schweiz und verschiedene Höhenlagen, sowie auf dem Kronenöffnungsgrad, der aus LiDAR Daten abgeleitet wurde. Die berücksichtigten Baumarten wurden anhand ihrer Lichtansprüche in die unten aufgeführten Klassen eingeteilt. Indikativ kann die Karte auch für andere Baumarten der jeweiligen Klasse verwendet werden:
- Lärche, Birke, Stiel- und Traubeneiche, Waldföhre und Kirschbaum: Lichtbaumarten (Klasse 1)
- Berg- und Spitzahorn: Halblichtbaumarten (Klasse 2)
- Fichte, Sommer- und Winterlinde: Halbschattenbaumarten (Klasse 3):
- Buche und Tanne: Schattenbaumarten (Klasse 4)
- Zu dunkel für das Aufwachsen sämtlicher Baumarten (Klasse 5)
Zweck: Die Karte zeigt, ob die lokal vorhandenen Lichtverhältnisse ausreichen, damit Jungbäume ohne zusätzlichen Eingriff vital ins Stangenholz aufwachsen können, und macht damit potenziell nötige waldbauliche Massnahmen frühzeitig erkennbar.
Hinweise zur Verwendung: Unterstützung von Försterinnen und Waldbewirtschaftern bei der Planung von Verjüngungseingriffen, der Jungwaldpflege, der Baumartenwahl und beim zeitlichen und räumlichen Monitoring, da die Karte bei neuen LiDAR-Befliegungen regelmässig aktualisiert wird.
Bemerkungen: Das Verjüngungspotential wurde ausschliesslich aufgrund des Kronenöffnungsgrads berechnet, der die Abschattung durch die umliegenden Baumkronen und dem Gelände berücksichtigt. Die direkte Sonneneinstrahlung wurde bisher nicht berücksichtigt, da damit statistisch weniger gute Ergebnisse erreicht werden. Somit kann auch die Hangexposition oder die Ausrichtung der Lücken bisher nicht berücksichtig werden. Ebenso können bisher keine Höhenstufen-spezifischen Unterteilungen erfolgen, da das verwendete nationale Modell in den statistischen Tests am besten abschneidet. In Beständen mit Bäumen und Sträuchern von ca. 3-7 Höhe werden die Lichtbedingungen in der Unterschicht überschätzt, d.h. die Karte zeigt in solchen Fällen ein zu hohes Verjüngungspotential. Die Karte wurde auf die Waldfläche gemäss LFI reduziert und Waldstrassen wurden so gut wie möglich ausmaskiert.
**Cartes nationales indiquant le potentiel de régénération lié à la lumière (état IFN VHM 2024)**
La carte montre les endroits où les conditions de luminosité sont suffisantes pour permettre une croissance en hauteur suffisante (-= 300 cm) de la régénération pour chaque groupe d'essences. La carte est basée sur des relevés effectués sur le terrain dans 39 districts forestiers répartis sur toute la Suisse et à différentes altitudes, ainsi que sur le degré d'ouverture de la canopée, dérivé des données LiDAR. Les essences prises en compte ont été classées dans les catégories ci-dessous en fonction de leurs besoins en lumière. La carte peut également être utilisée à titre indicatif pour d'autres essences de la catégorie correspondante :
Mélèze, bouleau, chêne pédonculé et chêne rouvre, pin sylvestre et cerisier : essences photophiles (classe 1)
Érable sycomore et érable plane : essences semi-photophiles (classe 2)
Épicéa, tilleul à petites feuilles et tilleul à grandes feuilles : essences semi-ombelibles (classe 3)
Hêtre et sapin : essences ombéliques (classe 4)
Trop sombre pour la croissance de toutes les essences (classe 5)
Objectif : la carte indique si les conditions de luminosité locales sont suffisantes pour que les jeunes arbres puissent atteindre le stade de perchis sans intervention supplémentaire et permet ainsi d'identifier à un stade précoce les mesures sylvicoles potentiellement nécessaires.
Conseils d'utilisation : aide les forestiers et les gestionnaires forestiers à planifier les mesures de rajeunissement, l'entretien des jeunes forêts, le choix des essences et le suivi temporel et spatial, car la carte est régulièrement mise à jour lors de nouveaux survols LiDAR.
Remarques : le potentiel de régénération a été calculé exclusivement sur la base du degré d'ouverture de la canopée, qui tient compte de l'ombrage causé par les cimes des arbres environnants et le terrain. L'ensoleillement direct n'a pas été pris en compte jusqu'à présent, car il donne des résultats statistiquement moins bons. De même, l'exposition des pentes ou l'orientation des clairières ne peuvent pas être prises en compte pour l'instant. De même, aucune subdivision spécifique à l'altitude ne peut être effectuée jusqu'à présent, car le modèle national utilisé obtient les meilleurs résultats dans les tests statistiques. Dans les peuplements composés d'arbres et d'arbustes d'une hauteur d'environ 3 à 7 mètres, les conditions d'ensoleillement dans la strate inférieure sont surestimées, c'est-à-dire que la carte indique dans ces cas un potentiel de régénération trop élevé. La carte a été réduite à la surface forestière selon l'IFN et les routes forestières ont été masquées autant que possible.
**Mappe indicative a livello nazionale del potenziale di ringiovanimento legato alla luce (stato LFI VHM 2024)**
La mappa mostra dove le condizioni di luce sono sufficienti per garantire un'adeguata crescita in altezza del rimboschimento (-= 300 cm) del rispettivo gruppo di specie arboree. La mappa si basa su rilevamenti sul campo effettuati da esperti in 39 distretti forestali distribuiti su tutto il territorio svizzero e a diverse altitudini, nonché sul grado di apertura della chioma ricavato dai dati LiDAR. Le specie arboree prese in considerazione sono state classificate in base alle loro esigenze di luce nelle classi elencate di seguito. La mappa può essere utilizzata in modo indicativo anche per altre specie arboree della rispettiva classe:
- Larice, betulla, farnia e faggio, pino silvestre e ciliegio: specie arboree che necessitano di luce (classe 1)
- Acero di montagna e acero riccio: specie arboree che necessitano di luce moderata (classe 2)
- Abete rosso, tiglio estivo e tiglio invernale: specie arboree di mezz'ombra (classe 3):
- Faggio e abete: specie arboree di ombra (classe 4)
- Troppo buio per la crescita di tutte le specie arboree (classe 5)
Scopo: la mappa mostra se le condizioni di luce locali sono sufficienti affinché i giovani alberi possano crescere vitalmente senza ulteriori interventi e rende così riconoscibili tempestivamente le misure selvicolturali potenzialmente necessarie.
Indicazioni per l'uso: supporto ai forestali e ai gestori forestali nella pianificazione degli interventi di ringiovanimento, nella cura dei boschi giovani, nella scelta delle specie arboree e nel monitoraggio temporale e spaziale, poiché la mappa viene aggiornata regolarmente con nuove rilevazioni LiDAR.
Osservazioni: il potenziale di rigenerazione è stato calcolato esclusivamente sulla base del grado di apertura della chioma, che tiene conto dell'ombreggiamento causato dalle chiome degli alberi circostanti e dal terreno. Finora non è stata presa in considerazione l'esposizione diretta al sole, poiché ciò comporta risultati statisticamente meno attendibili. Pertanto, non è stato possibile tenere conto dell'esposizione del pendio o dell'orientamento delle radure. Allo stesso modo, non è ancora possibile effettuare suddivisioni specifiche per altitudine, poiché il modello nazionale utilizzato ottiene i migliori risultati nei test statistici. Nei popolamenti con alberi e arbusti di circa 3-7 metri di altezza, le condizioni di luce nello strato inferiore sono sovrastimate, ovvero in questi casi la mappa mostra un potenziale di rigenerazione troppo elevato. La mappa è stata ridotta alla superficie forestale secondo l'IFN e le strade forestali sono state mascherate il più possibile.
**Switzerland-wide maps showing light-induced regeneration potential (as of NFI VHM 2024)**
The map shows where light conditions are sufficient for sufficient height growth of regeneration (-= 300 cm) for each tree species group. The map is based on expert field surveys in 39 forest districts spread across Switzerland and at various altitudes, as well as on the degree of canopy opening derived from LiDAR data. The tree species considered were divided into the classes listed below based on their light requirements. The map can also be used as a guide for other tree species in the respective class:
- Larch, birch, English oak, sessile oak, Scots pine, and cherry: light-demanding tree species (class 1)
- Sycamore maple and Norway maple: semi-light-demanding tree species (class 2)
- Spruce, small-leaved and large-leaved lime: semi-shade tree species (class 3):
- Beech and fir: shade tree species (class 4)
- Too dark for all tree species to grow (class 5)
Purpose: The map shows whether the local light conditions are sufficient for young trees to grow vigorously into pole wood without additional intervention, thus identifying potentially necessary silvicultural measures at an early stage.
Notes on use: Support for foresters and forest managers in planning regeneration measures, young forest maintenance, tree species selection, and temporal and spatial monitoring, as the map is regularly updated with new LiDAR flights.
Comments: The regeneration potential was calculated solely on the basis of the degree of canopy opening, which takes into account the shading caused by the surrounding tree crowns and the terrain. Direct sunlight has not been taken into account so far, as this leads to statistically less reliable results. This means that slope exposure or the orientation of gaps cannot be taken into account at this stage. Similarly, no altitude-specific subdivisions can be made at present, as the national model used performs best in statistical tests. In stands with trees and shrubs of approx. 3-7 m in height, the light conditions in the understorey are overestimated, i.e. in such cases the map shows an excessively high regeneration potential. The map has been reduced to the forest area according to the NFI, and forest roads have been masked out as much as possible.

en: forest-management rejuvenation light-availability lidar protection-forest

envidat

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

en: EnviDat: the Environmental Data Portal

en: Switzerland-wide maps of light-induced regeneration potential (as of NFI VHM 2024)

de:
Resource Title: LiDAR_Befliegungsjahre_VHM_LFI_2024.png
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    Format: image/png
    URL: https://www.envidat.ch/dataset/c9b1019d-78cf-49b5-b065-25d8e2dd2700/resource/75048199-3e98-4715-9d2e-8e27cb265b12/download/lidar_befliegungsjahre_18122025.png
    Description: Karte mit den Befliegungsjahren der LiDAR Daten, welche für die Berechnung des Verjüngungspotential verwendet wurden.

format: PNG

media_type: image/png

de:
Resource Title: Verjüngungspotential_LFI_2024.tif
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    URL: https://www.envidat.ch/dataset/c9b1019d-78cf-49b5-b065-25d8e2dd2700/resource/2a3adf57-5d17-4c04-897b-13c2c94149b9/download/verjpot_ch_18122025.tif
    Description:

format: TIFF

media_type: image/tiff

de:
Resource Title: Verjüngungspotentialkarte_Kurzbeschrieb.pdf
    Size: 364954 bytes
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    URL: https://www.envidat.ch/dataset/c9b1019d-78cf-49b5-b065-25d8e2dd2700/resource/1f901079-7a5a-46a5-8246-a685d71e0c8f/download/verjungungspotentialkarte_kurzbeschrieb_17022026.pdf
    Description: Kurzbeschrieb inkl. Legende und Limitierungen

forest-management

display_name: forest-management

lidar

display_name: lidar

light-availability

display_name: light-availability

protection-forest

display_name: protection-forest

rejuvenation

display_name: rejuvenation


$oid: OBSOLETE

en: This dataset comprises long-term daily 1 km gridded data of snow water equivalent and snow depth for Switzerland spanning the hydrological years 1962 until 2023. The SWE data were generated using the OSHD temperature index model and subsequent quantile mapping (Michel et al. 2024). The snow depth data were derived from the SWE data after applying the SWE2HS conversion algorithm. Details on theses datasets can be found in doi.org/10.5194/tc-19-4391-2025. The products were developed as part of the SPAtial Snow climatology for Switzerland (SPASS) project, a collaboration between MeteoSwiss and SLF.

en: swe 1km-resolution gridded-data snow-depth switzerland snow-climatology

envidat

approval_status: approved

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

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

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

de: EnviDat: das Umwelt-Datenportal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: SPASS - new gridded climatological snow datasets for Switzerland

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

de:
Resource Title: SWECLQMD_ch01h.swiss.lv95_WY_1962_2023.zip
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    URL: https://www.envidat.ch/dataset/da0c9ee4-ff02-4848-bc65-cbcaae343636/resource/a5db7556-7a91-413a-8d90-4c02aa2e9e8c/download/sweclqmd_ch01h.swiss.lv95_wy_1962_2023.zip
    Description: Long-term daily 1 km gridded data of snow water equivalent for Switzerland. The SWE data were generated using the OSHD temperature index model and subsequent quantile mapping. Details can be found in Michel et al. (2024), see doi.org/10.5194/gmd-17-8969-2024. A first version of this SWE data was publised at https://zenodo.org/records/7886773

format: ZIP

de:
Resource Title: HSCLQMD_ch01h.swiss.lv95_WY_1962_2023.zip
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    URL: https://www.envidat.ch/dataset/da0c9ee4-ff02-4848-bc65-cbcaae343636/resource/688d1caf-ed63-4cb8-8579-8c017bb7458c/download/hsclqmd_ch01h.swiss.lv95_wy_1962_2023.zip
    Description: Long-term daily 1 km gridded data of snow depth for Switzerland. The data were produced by applying the SWE2HS algorithm of Aschauer et al. (2023) to the SWE dataset, which can also be found in this directory. Details can be found in (doi.org/10.5194/gmd-16-4063-2023).

gridded-data

display_name: gridded-data

snow-climatology

display_name: snow-climatology

snow-depth

display_name: snow-depth

swe

display_name: swe

switzerland

display_name: switzerland


en:
This dataset contains several useful components for **Swiss Electricity system modeling**:
1. **High-voltage Swiss grid**: 2019 snapshot of the high-voltage (-150kV) grid composed of :
- Nodes: names, voltage, position.
- Lines: ends nodes, resistance, power capacity.
- Transformers: ends nodes, power capacity, resistance, tap ratio.
2. **Hydropower topology** : Representative description of Swiss reservoirs and plants (2019) composed of:
- Plants: Lower reservoir, Upper reservoir, pump/turbine, debit, power capacity.
- Reservoirs: Volume, altitude, position, canton.
3. **Inflows** into all the reservoirs: hourly time series post-processed from PREVAH model outputs from 2009 to 2019.
4. **Wind production per node**: COSMO-1 data processed and scaled to match SFOE annual production.
5. **Solar irradiance maps projected onto tilted surfaces**: hourly time series for 35° and 65° tilts, azimuth 180°. (same method as [Frischholz, Y. (2025)](https://www.doi.org/10.16904/envidat.632))
6. **Processed ERA5 (historical), ECMWF SEAS5 (seasonal forecast) and ECMWF TIGGE (short-term forecast)**:
- ERA5 Land: For inflow forecast model: historical runoff, snow depth, temperature and total precipitation per catchment (hourly, 2009-2019). For residual demand forecast model: historical temperature, u-v wind, ssrd per country (CH, AT, DE, FR, IT, hourly 2015-2019)
- ECMWF SEAS5: For inflow forecast model: same variables as ERA5 (yearly forecast starting from every 1st day of every month in 2019). For residual demand forecast model: same variables as ERA5 (yearly forecast starting from every 1st day of every month in 2019).
- ECMWF TIGGE: For inflow forecast model: same variables as ERA5 (two weeks forecast starting from every 1st day of every week in 2019). For residual demand forecast model: same variables as ERA5 (two weeks forecast starting from every 1st day of every week in 2019).
7. **Run-of-river production per electricity node**: 15-min resolution run-of-river production time-series (15min, 2009-2019). Raw data and documentation can be found on [https://data.sccer-jasm.ch/runofriver_production/2017-10-16/].
\
More details can be found on *README.txt*.

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

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

de:
Resource Title: data_IFM_RDFM.zip
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/7f514513-cb54-44a9-8ea4-8f8087048247/download/data_ifm_rdfm.zip
    Description: ERA5 Land, Seasonal forecast and short-term forecast meteorological data to train inflow and residual demand forecast models.

media_type: application/zip

de:
Resource Title: node_RunOfRiver_15min.ftr
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    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/9923c621-7238-4b16-b20d-6736da165a5a/download/node_runofriver_15min.ftr
    Description: Run-of-river production on 15min resolution from 2009 to 2019 in Switzerland, aggregated by electrical nodes.

format: OCTET

de:
Resource Title: data_IFM_RDFM.zip
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/965d0b4b-a87f-4023-8bb8-a33dd2d86c5a/download/data_ifm_rdfm.zip
    Description:

de:
Resource Title: SwissGrid_data.zip
    Size: 17664 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/a1224789-8bb8-46cd-a36d-4cb81d6d47ec/download/swissgrid_data.zip
    Description: Description of Swissgrid high-voltage (>150kV) grid, including lines, transformers and nodes (snapshot of the grid in 2019) .

de:
Resource Title: PV_data.zip
    Size: 2072321720 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/e0e501b9-1041-4ff9-a3d9-485cce4aadac/download/pv_data.zip
    Description: Solar irradiance maps projected onto tilted surfaces: hourly time series for 35° and 65° tilts, azimuth 180°. (same method as [Frischholz, Y. (2025)](https://www.doi.org/10.16904/envidat.632))

de:
Resource Title: hydropower.zip
    Size: 21349007 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/66f009b8-0cef-4f4e-bc12-c3a7d684f229/download/hydropower.zip
    Description: Plants and reservoirs description, with associated hourly inflows (2009 - 2019).

de:
Resource Title: README.txt
    Size: 6216 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/0dc23d95-7563-4f21-a416-ca46643d9b69/download/readme.txt
    Description: Data structure and description.

format: TXT

media_type: text/plain

electricity-grid

display_name: electricity-grid

forecast-models

display_name: forecast-models

hydropower

display_name: hydropower

meteorological-data

display_name: meteorological-data

renewable-energy

display_name: renewable-energy

reservoir-inflow

display_name: reservoir-inflow

solar-irradiance

display_name: solar-irradiance


en: This dataset contains code and data to reproduce the figures and analyses in the article 'Environmental DNA metabarcoding reveals biodiversity recovery in tropical forest restoration across multiple landscapes' in Ecosphere journal. In this study, we analysed environmental DNA data from water and soil samples collected across gradients of forest regrowth at six sites in Colombia to assess the recovery of mammal and soil biodiversity associated with forest restoration.

en: soil-biodiversity colombia environmental-dna mammals forest-restoration biodiversity-assessment

en: Data and code underlying 'Environmental DNA metabarcoding reveals biodiversity recovery in tropical forest restoration across multiple landscapes'

de:
Resource Title: Raw Data.zip
    Size: 1610612736 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/18ce28d1-0fa3-4f97-9690-78697dd39683/resource/b97d2608-f0d8-4544-a9a6-024f43b1d55d/download/raw-data.zip
    Description: Paired-end reads (specified by the R1 and R2 suffixes) for each individual sample. The reads have been demultiplexed using cutadapt but primers remain present in the sequences.

de:
Resource Title: Analysis Data and Code.zip
    Size: 15005123 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/18ce28d1-0fa3-4f97-9690-78697dd39683/resource/275e7eac-1898-48ed-8c91-e3d2ee840782/download/analysis-data-and-code.zip
    Description: A zipped folder containing the scripts and data necessary to reproduce the analyses described in the study. Further instructions are provided in scripts/ReadMe.txt

biodiversity-assessment

display_name: biodiversity-assessment

colombia

display_name: colombia

environmental-dna

display_name: environmental-dna

forest-restoration

display_name: forest-restoration

mammals

display_name: mammals

soil-biodiversity

display_name: soil-biodiversity


en:
The Swiss Federal Research Institute WSL has extensive experience with surrogate bedload transport measurements. The first measuring site was established in the Erlenbach stream, a small (first-order) catchment in the pre-alpine valley Alptal in central Switzerland. Continuous bedload transport measurements were started in 1986, using first piezoelectric sensors (1986 to 1999) and then geophone sensors (from 2002 onwards) underneath a steel plate and mounted flush with the streambed. In the meantime, the so-called Swiss plate geophone (SPG) system has been installed at more than 20 field sites, primarily in smaller and steeper streams in Switzerland, Austria, and Italy but also in a few larger rivers and in some other streams worldwide (Israel, USA, Japan).
Sediment transport observations in Switzerland with the SPG system concern the following streams: Erlenbach near Brunni (Alptal valley), Albula at Tiefencastel, Navisence at Zinal, Avançon de Nant near Pont de Nant, and Riedbach near Graechen (see map). The data in this repository primarily refer to calibration measurements with the SPG system. The publications listed here discuss primarily the performance of the measuring system but also process-based aspects of bedload transport.

en: mountain-stream surrogate-measurement river sediment-transport bedload-transport torrent

en: Sediment transport observations in Swiss mountain streams

de:
Resource Title: Hydrological drivers of bedload transport in Avançon de Nant
    Size: 224860 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0387b6bb-a4f8-4e0d-87e0-6eff063422bd/resource/3e8ee124-0947-4d2a-aa1a-2fef979f838d/download/avancon-de-nant-hydrol-drivers.zip
    Description: Data used in paper: Antoniazza, G., Nicollier, T., Boss, S., Mettra, F., Badoux, A., Schaefli, B., Rickenmann, D., Lane, S.N., 2022. Hydrological Drivers of Bedload Transport in an Alpine Watershed. Water Resources Research, 58, e2021WR030663. https://doi.org/10.1029/2021WR030663

de:
Resource Title: Erlenbach-27-years-bedload-data.zip
    Size: 4320133 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/0387b6bb-a4f8-4e0d-87e0-6eff063422bd/resource/bb45250d-fa75-440d-9601-f5aa3ccdce35/download/erlenbach-27-years-bedload-data.zip
    Description:

de:
Resource Title: Data-variability-SPG-response
    Size: 1415578 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0387b6bb-a4f8-4e0d-87e0-6eff063422bd/resource/44fb4007-2e14-485a-8021-1ff48a75246e/download/data-variability-spg-response.zip
    Description: Data used in paper: Antoniazza, G., Nicollier, T., Wyss, C.R., Boss, S., Rickenmann, D., 2020. Bedload Transport Monitoring in Alpine Rivers: Variability in Swiss Plate Geophone Response. Sensors 2020, 20, 4089; doi:10.3390/s20154089

de:
Resource Title: avancon-de-nant-event-3-August-2020.zip
    Size: 23603446 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/0387b6bb-a4f8-4e0d-87e0-6eff063422bd/resource/02f144f6-bde3-4578-94e2-be47dd83c74e/download/avancon-de-nant-event-3-august-2020.zip
    Description: Data used in paper: Antoniazza, G., Dietze, M., Mancini, D., Turowski, J.M., Rickenmann, D., Nicollier, T., Boss, S., Lane, S.N. (2023). Anatomy of an Alpine bedload transport event: A watershed-scale seismic-network perspective. Journal of Geophysical Research: Earth Surface, 128, e2022JF007000. https://doi.org/10.1029/2022JF007000

de:
Resource Title: Erlenbach-27-years-bedload-data.zip
    Size: 4320133 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/0387b6bb-a4f8-4e0d-87e0-6eff063422bd/resource/94f81288-de72-4562-8ee0-e748208a280c/download/erlenbach-27-years-bedload-data.zip
    Description: Erlenbach-27-years-bedload-dat
Data used in paper: Rickenmann, D., 2023. Bedload transport fluctuations, flow conditions and disequilibrium ratio at the Swiss Erlenbach stream: results from 27 years of high- resolution temporal measurements. Earth Surface Dynamics.

de:
Resource Title: EU-1min-1986-1999-286_Qbcalculated_full_QbrepM.csv
    Size: 10380902 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/0387b6bb-a4f8-4e0d-87e0-6eff063422bd/resource/8b29c4e6-8005-4a9c-add8-f9b93dd423ae/download/eu-1min-1986-1999-286_qbcalculated_full_qbrepm.csv
    Description:

format: XLS

de:
Resource Title: Albula-2016-1min_data.zip
    Size: 6218056 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0387b6bb-a4f8-4e0d-87e0-6eff063422bd/resource/a75f0380-edc9-4c6d-a08e-f73deba3bf71/download/albula-2016-1min_data.zip
    Description: Bedload transport measurements with SPG system for Albula 2106.

de:
Resource Title: SPG field calibration data
    Size: 110305 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/0387b6bb-a4f8-4e0d-87e0-6eff063422bd/resource/173101e2-a6bb-4ddb-90d0-dfaea63aea73/download/field_calibration.xlsx
    Description: SPG field calibration data from the Erlenbach (since 2009), the Albula (2018), the Navisence (2019) and the Avançon de Nant (2019-2020) monitoring stations. Data used in following papers:
"Toward a General Calibration of the Swiss Plate Geophone System for Fractional Bedload Transport" submitted to Earth Surface Dynamics on the 14th of January 2022.
"Improving the Calibration of Impact Plate Bedload Monitoring Systems by Filtering Out Acoustic Signals from Extraneous Particle Impacts" submitted to Earth and Space Science on the 13th of August 2021.

de:
Resource Title: SPG flume experiment data
    Size: 270817 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/0387b6bb-a4f8-4e0d-87e0-6eff063422bd/resource/be0743a3-87fc-4e4d-bb42-3195224883be/download/flume_experiments.xlsx
    Description: SPG data from controlled flume experiments conducted at the Obernach outdoor flume facility (TU Munich) between 2018 and 2020. Data used in following papers:
"Toward a General Calibration of the Swiss Plate Geophone System for Fractional Bedload Transport" submitted to Earth Surface Dynamics on the 14th of January 2022.
"Improving the Calibration of Impact Plate Bedload Monitoring Systems by Filtering Out Acoustic Signals from Extraneous Particle Impacts" submitted to Earth and Space Science on the 13th of August 2021.

de:
Resource Title: Erlenbach_event-bedload_periods-A-B
    Size: 103076 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0387b6bb-a4f8-4e0d-87e0-6eff063422bd/resource/cf94099a-e87d-4920-9587-73f6cdd78943/download/erlenbach_event-bedload_periods-a-b.zip
    Description: Data used in paper: Rickenmann, D., 2020. Effect of Sediment Supply on Cyclic Fluctuations of the Disequilibrium Ratio and Threshold Transport Discharge, Inferred from Bedload Transport Measurements over 27 Years at the Swiss Erlenbach stream. Water Resources Research, 56, e2020WR027741. https://doi.org/10.1029/2020WR027741

de:
Resource Title: Comparison of calibration characteristics of different acoustic impact systems
    Size: 30300 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0387b6bb-a4f8-4e0d-87e0-6eff063422bd/resource/4d2eb4f8-99eb-4223-8627-527d3e6c629f/download/calibration-measurements-paper-esurf-2022.zip
    Description: Data used in paper: Rickenmann, D., Ammann, L., Nicollier, T., Boss, S., Fritschi, B., Antoniazza, G., Steeb, N., Chen, Z., Wyss, C., Badoux, A. (2022). Comparison of calibration characteristics of different acoustic impact systems for measuring bedload transport in mountain streams. Earth Surface Dynamics Discussions, https://doi.org/10.5194/esurf-2022-7

bedload-transport

display_name: bedload-transport

mountain-stream

display_name: mountain-stream

river

display_name: river

sediment-transport

display_name: sediment-transport

surrogate-measurement

display_name: surrogate-measurement

torrent

display_name: torrent


de: EnviDat

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

en: Predicted cloud droplet numbers Davos Wolfgang

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

envidat

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.

de: EnviDat: das Umwelt-Datenportal

envi

de: Umwelt

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

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

format: CSV

media_type: text/csv

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

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

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

wolfgangpass

display_name: wolfgangpass


en:
This repository contains the data necessary to reproduce Fig. 5 of the publication: L. Mewes, H. Löwe, M. Schneebeli & B. Walter, *Optical determination of snow density via sub-surface scattering*, Commun Phys, doi: [10.1038/s42005-026-02490-1](https://doi.org/10.1038/s42005-026-02490-1).
The source code generating Fig. 5 for the above mentioned publication is available at [10.16904/envidat.726](https://doi.org/10.16904/envidat.726).
 Files
`SI01_Reflectance_Coldlab.h5`: Reflectance and partial reflectance data of a snow profile using the [SnowImager](https://www.snowimager.ch/).
`uCT_reference_data.csv`: Reference data extracted from a micro-computed tomography measurement.
Please refer to the `README` of the analysis code at [10.16904/envidat.726](https://doi.org/10.16904/envidat.726) for further information.

en: reflection snow-microstructure snowimager snow-density snow-stratigraphy snow-profile laboratory-study

envidat

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

en: EnviDat: the Environmental Data Portal

en: Data for the publication Optical determination of snow density via sub-surface scattering

laboratory-study

display_name: laboratory-study

reflection

display_name: reflection

snow-density

display_name: snow-density

snow-microstructure

display_name: snow-microstructure

snow-profile

display_name: snow-profile

snow-stratigraphy

display_name: snow-stratigraphy

snowimager

display_name: snowimager


en:
This is the source code for modeling preferential snowfall deposition and snowfall downscaling as described in:
Helbig N., Mott R., Bühler Y., Le Toumelin L., Lehning M. (2024) Snowfall deposition in mountainous terrain: a statistical downscaling scheme from high-resolution model data on simulated topographies. Front. Earth Sci. 11, 1308269 (19 pp.). https://doi.org/10.3389/feart.2023.1308269
The code reads wind speed, wind direction, and snowfall values for each time step from the example file meteo.txt, as well as terrain elevations (DEM) from dem.txt. The snowfall value is then downscaled to the fine grid defined by the DEM and written to pref_dep.txt.
The current version can be found in the WSL/SLF Gitlab repository:
https://gitlabext.wsl.ch/snow-models/statistical-preferential-deposition-model

en: downscaling snowfall preferential-deposition statistical-model mountains

en: Source code for a preferential snowfall deposition model

envi

en: Environment

downscaling

display_name: downscaling

mountains

display_name: mountains

preferential-deposition

display_name: preferential-deposition

snowfall

display_name: snowfall

statistical-model

display_name: statistical-model


en:
The repository contains the source code necessary to reproduce Figs. 2-5 of the publication: L. Mewes, H. Löwe, M. Schneebeli & B. Walter, *Optical determination of snow density via sub-surface scattering*, Commun Phys, doi: [10.1038/s42005-026-02490-1](https://doi.org/10.1038/s42005-026-02490-1).
The data for Fig. 5 of the above mentioned publication is available at [10.16904/envidat.724](https://doi.org/10.16904/envidat.724).
Please refer to the `README` in this repository for additional information.
- **License:** The scripts in this repository are licensed under the MIT License; see LICENSE.txt for the full text.
 Files
`generate_figures.py`: Main script to generate the figures of the publication.
`snow_optics.py`: Core radiative-transfer functions.
`snow_image.py`: Utilities for loading and processing SnowImager ([www.snowimager.ch](https://www.snowimager.ch)) HDF5 data.
`helper.py`: Miscellaneous helper functions.
`lambertw.py`: Lambert W function implementation (John Burkardt, 2023, MIT license, -https://people.sc.fsu.edu/~jburkardt/py_src/lambert_w/lambert_w.py-).
 Third party resources
- Refractive indices of ice are taken from 'Warren, S. G., and R. E. Brandt (2008), Optical constants of ice from the ultraviolet to the microwave: A revised compilation, J. Geophys. Res., 113, D14220, doi:[10.1029/2007JD009744](https://doi.org/10.1029/2007JD009744).' available at: https://refractiveindex.info/database/data/main/H2O/nk/Warren-2008.yml
- The Lambert *W* function is implemented using the code of John Burkardt, modified 20 June 2023 and distributed under the MIT license (-https://people.sc.fsu.edu/~jburkardt/py_src/lambert_w/lambert_w.py-).
- The source code in this repository is part of a larger project for post-processing [SnowImager](https://www.snowimager.ch) data hosted at [https://code.wsl.ch/snow-physics/snowimagerpro](https://code.wsl.ch/snow-physics/snowimagerpro).

en: snow-profile laboratory-study snowimager snow-microstructure reflection snow-density snow-stratigraphy

en: Source code for the publication Optical determination of snow density via sub-surface scattering

de:
Resource Title: Source code
    Size: 22825 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5ef9984d-7fd8-4b95-87c4-f479b2e5ac13/resource/9af1ca7b-8780-46ec-abbc-e4b6890eead6/download/src.zip
    Description: Source code to generate the figures for Mewes et al., Commun Phys, 2026.

The scripts in this repository are licensed under the MIT License; see LICENSE.txt for the full text.

de:
Resource Title: README.md
    Size: 5181 bytes
    Format: text/markdown
    URL: https://www.envidat.ch/dataset/5ef9984d-7fd8-4b95-87c4-f479b2e5ac13/resource/97d46f1d-9321-4b1a-9a72-d79978da633f/download/readme.md
    Description: Instructions to generate figures for Mewes et al., Commun Phys, 2026.

format: markdown


en: This is the metadata collected in the Blue-Green Biodiversity Beavers project running from 2021 till 2025 about how beavers affect the biodiversity and ecosystem. See the linked datasets for studies and methods.

en: bats soil deadwood metadata beaver freshwater

en: Blue-Green Biodiversity Beavers affecting Ecosystems

bats

display_name: bats

beaver

display_name: beaver

deadwood

display_name: deadwood

freshwater

display_name: freshwater

metadata

display_name: metadata

soil

display_name: soil


en: This dataset accompanies the publication “Operational snow hydrological modeling for Switzerland” (Mott et al., 2023; https://doi.org/10.3389/feart.2023.1228158). It provides a climatological record of gridded snow water equivalent and snowmelt runoff for the period 1998–2022, resolved at 1 km spatial and daily temporal scales. All data were generated using the conceptual OSHD Temperature Index Model.

en: snow-cover-fraction snow-water-equivalent snow-climatology snow-melt operational-snow-hydrological-service

en: Climatological snow data since 1998, OSHD

operational-snow-hydrological-service

display_name: operational-snow-hydrological-service

snow-climatology

display_name: snow-climatology

snow-cover-fraction

display_name: snow-cover-fraction

snow-melt

display_name: snow-melt

snow-water-equivalent

display_name: snow-water-equivalent


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

CHELSA data published in EnviDat includes the deprecated version 1.2 (originally published under 10.5061/dryad.kd1d4). Please use the current 2.1 version.
Paper Citation:
 - _Karger DN. et al. Climatologies at high resolution for the earth’s land surface areas, Scientific Data, 4, 170122 (2017)  [doi: 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._

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

de:
Resource Title: CHELSA V1.2 (deprecated)
    Size: 1319413953331 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsav1%2F
    Description: CHELSA Version 1.2 based on ERA-Interim forcings and CMIP5

de:
Resource Title: CHELSA V2.1 (current)
    Size: 2308974418330 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fclimatologies%2F
    Description: CHELSA Version 2.1 based on ERA5 forcings and CMIP6

de:
Resource Title: CHELSA Technical Documentation
    Size: 285413 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/2adb0c83-4653-4337-af28-f75c63ab7c74/resource/61fff0a7-6abb-45f7-a5f6-48f6e2c24851/download/chelsa_file_specification.pdf
    Description: Technical documentation document including the variable names, units, scaling, offset, and background.

format: PDF

media_type: application/pdf

bioclim

display_name: bioclim

climate

display_name: climate

climate-change

display_name: climate-change

climate-layers

display_name: climate-layers

climatology

display_name: climatology

free-climate-data

display_name: free-climate-data

precipitation

display_name: precipitation

temperature

display_name: temperature


en:
Carabid beetle and wild bee occurrences in the city of Zurich, Switzerland.
Dataset available upon request (An agreement between the data provider and the data recipient is necessary).

en: ground-beetle species community-assembly environmental-heterogeneity bettergarden wild-bees niche-breadth wild-bee bettergardens ecology allotments functional urban-ecology niche allotment diversity functional-traits ground-beetles gardens urbanization composition species-composition functional-diversity garden trait

envidat

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

en: EnviDat: the Environmental Data Portal

en: The origin of urban communities: from the regional species pool to community assemblages in city

de:
Resource Title: Arthropod-databank query used to access data.
    Size: 7751 bytes
    Format: application/sql
    URL: https://www.envidat.ch/dataset/33eb0e1a-fa8b-4b57-8db9-7a929feda811/resource/040d9b89-6455-4af9-8092-47e4fdd487d8/download/fido_global_extract-2019-10-30_13-31-00-urban-carabidae-apidae.sql
    Description:

format: sql

de:
Resource Title: Urban carabid and bee species city of Zurich Switzerland
    Size: 12151612 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/33eb0e1a-fa8b-4b57-8db9-7a929feda811/resource/0d32e239-6d49-4d69-a1fd-81a3cd854591/download/fido_global_extract-2019-10-30_13-31-00-urban-carabidae-apidae.txt
    Description: Carabid beetle and wild bee occurrences in the city of Zurich, Switzerland, from the years (project) 2009 (BiodiverCity), 2011 (ENHANCE) and 2016 (BetterGardens).

allotment

display_name: allotment

allotments

display_name: allotments

bettergarden

display_name: bettergarden

bettergardens

display_name: bettergardens

community-assembly

display_name: community-assembly

composition

display_name: composition

diversity

display_name: diversity

ecology

display_name: ecology

environmental-heterogeneity

display_name: environmental-heterogeneity

functional

display_name: functional

functional-diversity

display_name: functional-diversity

functional-traits

display_name: functional-traits

garden

display_name: garden

gardens

display_name: gardens

ground-beetle

display_name: ground-beetle

ground-beetles

display_name: ground-beetles

niche

display_name: niche

niche-breadth

display_name: niche-breadth

species

display_name: species

species-composition

display_name: species-composition

trait

display_name: trait

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

wild-bee

display_name: wild-bee

wild-bees

display_name: wild-bees


en:
This is the source code for computing the seasonal fractional snow-covered area. It is written in Fortran 90. The code reads snow depth (HS) and snow water equivalent (SWE) data from the provided example file HS_SWE.txt and writes the computed fractional snow-covered area (fSCA) to a file fSCA.txt.
The current version can be found in the WSL/SLF Gitlab repository:
https://gitlabext.wsl.ch/snow-models/fractional-snow-covered-area

en: snow-depth snow-water-equivalent subgrid-topography snow-cover-modeling source-code hysteresis

en: Seasonal fractional snow-covered area algorithm

envi

en: Environment

hysteresis

display_name: hysteresis

snow-cover-modeling

display_name: snow-cover-modeling

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

source-code

display_name: source-code

subgrid-topography

display_name: subgrid-topography


en: This is an R script written to calculate the relative terrain aspect parameter, which is utilized in the preferential snowfall downscaling scheme for mountainous terrain. The code reads a terrain aspect angle grid from file 'terrain_aspect_grid.txt' and  writes the computed relative terrain aspect angle grid to file 'delta_psi_grid.txt'.

en: complex-topography downscaling snowfall preferential-deposition r-script

en: Snowfall  downscaling Source code to calculate the relative terrain aspect parameter

complex-topography

display_name: complex-topography

downscaling

display_name: downscaling

preferential-deposition

display_name: preferential-deposition

r-script

display_name: r-script

snowfall

display_name: snowfall


en:
Base data which were used for the methodology projecting future regional snow depths based on temperature scenarios. This study was supported by Seilbahnen Schweiz, Schweiz Tourismus, Seco and Speed2Zero.
The data is split in three folders:
- Literature-Fit dataset
- Literature-Validation dataset
- Projections
The Literature-Fit dataset is the data which was used to retrieve the fit parameters a, Delta b and Delta c, for different elevations and temperature scenarios, which can be found in data/literature_fit/fitfactor.csv. Those fit factors were used to project future snow depths from climatological snow depths. Futhermore, this dataset was used to link decreases in mean snow depths for different seasonal periods to the NDJFMA period (November until April, in total six months), those differences in reduction values can be found in data/literature_fit/difference_decrease_4differentperiods.csv.  The raw reference and future snow depths of different studies and different elevation and temperature scenarios (dt) can be found in data/literature_fit/literature_data/* where each file is named by data_author_station_dt.csv.
The Literature-Validation dataset can be found in data/literature_validation/literature_data.csv contains the reference, RCP scenario, reference period, projected period and corresponding temperature scenarios. Furthermore, it contains region, elevation and relative decreases with corresponding seasonal periods, and the number of days with a certain amount of snow during the reference period and the projected period, if literature reported these values.
The folder projections contains mean snow depths, 5th and 95th percentile snow depth for each day of the year for the reference period and a temperature scenario of +2°C for all four stations Engelberg, Maloja, Saanenmöser and Weissfluhjoch.

en: future snow-depth snow-climatology alps climate-change

en: Literature based approach to estimate future snow

alps

display_name: alps

climate-change

display_name: climate-change

future

display_name: future

snow-climatology

display_name: snow-climatology


en:
This dataset forms the base of the paper 'Contrasting canopy air temperatures across complex terrain during the snow season' submitted to JGR Atmospheres. It aims to ensure full reproducibility of the figures by providing all relevant scripts (MATLAB R2024a), functions, processed data, and primary data. The primary data consists of three January-April periods in two Alpine valleys (Flüela, 2023; Monstein; 2024, 2025) of:
i) Ventilated canopy air temperatures and humidity (60s intervals) measured at 2.5 m above ground by automatic weather stations (AWS) at opposing north- and south-facing slopes.
ii) Concurrent AWS observations (15s intervals) at a non-forested site in the Monstein valley (2024, 2025) of incoming LW, SW at approximately 0.5 m above ground. At approximately 2.5 m above ground, RH (ventilated), air temperature (ventilated), wind speed, and wind direction were measured.
iii) Passive-ventilated HOBO loggers measuring air temperature at 0.5 m, 1.0 m, 2.5 m, 4.5 m above ground along vertical profiles installed on stems at 30-minute (2023, 2024) or 15-minute (2025) intervals at multiple sites across at opposing north- and south-facing slopes.
iv) In addition, information on when snowfall occurred and when snow cover was present below the canopy or within the canopy is available for the 2023 and 2024 observation periods.
Full details are available in the corresponding publication (citation reference to be found under 'Related Research' once accepted).

en: canopy-cover snow forest-canopy forest-snow subalpine

en: Canopy air temperatures in subalpine forests during three snow seasons

canopy-cover

display_name: canopy-cover

forest-canopy

display_name: forest-canopy

forest-snow

display_name: forest-snow

snow

display_name: snow

subalpine

display_name: subalpine


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

en: Stability tests, avalanche observations, Switzerland, Norway

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

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

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

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

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

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland


en:
This repository contains all WRF model outputs and observational data sets used for the paper:
Georgakaki, P., Sotiropoulou, G., Vignon, É., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021.

en: ice-water-content liquid-water-content blowing-snow wrf secondary-ice-production swiss-alps ice-crystal-number-concentration jungfraujoch

envidat

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

en: EnviDat: the Environmental Data Portal

de: EnviDat

en: Data for the publication "Secondary ice production processes in wintertime alpine mixed-phase clouds"

de:
Resource Title: WRF outputs
    Size: 741751014 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a014f39f-34c3-499f-9627-0efde504d2e1/resource/45b96d72-d1ef-4208-8b22-6c6ab546208b/download/wrf-outputs.zip
    Description: The .zip file contains all the NetCDF outputs produced by the mesoscale Weather Research and Forecasting (WRF) model (version: 4.0.1). Two case studies were simulated in this study, between 25-28 January 2014 (00:00 UTC) and 29 January - 1 February 2014 (00:00 UTC), respectively. The former is denoted as "NW" and the latter as "SE", owing to the prevailing wind conditions at the Jungfraujoch (JFJ) station.

The outputs come from the innermost domain of the simulations with a spatial resolution of 1 km and are extracted from the nearest to JFJ model grid point (46.55°N 7.98°E). The dimensions of each model variable is [145,100], which corresponds to time and height, respectively. The frequency of the outputs is 30 min. Note that the first 24 hours of each simulation (i.e., the first 48 outputs) were considered spin up time.

The following sets of sensitivity simulations were presented in this study:

* CNTRL_NW.nc, CNTRL_SE.nc : Sensitivity simulations performed with the default version of WRF
* DS_PHILL_NW.nc, DS_PHILL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Phillips et al. (2018)
* DS_SULL_NW.nc, DS_SULL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Sullivan et al. (2018)
* PHILL_NW.nc, PHILL_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Phillips et al. (2017)
* TAKAH_NW.nc, TAKAH_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Sullivan et al. (2018) based on Takahashi et al. (1995)
* TAKAHrim_NW.nc, TAKAHrim_SE.nc : Same set-up as before but applied only to collisions between rimed ice particles
* BIPS_Xk0_DY_NW.nc, BIPS_Xk0_DY_SE.nc : Sensitivity simulations including a source of Xk0 (either 10.0 or 100.0 L⁻¹) ice crystals with sizes of DY (either 10 or 100 μm) in the first atmospheric level of the model
* BIPS100_PHILL_NW.nc, BIPS100_PHILL_SE.nc : Sensitivity simulations where the combined effect of blowing snow and ice-ice collisional break-up is examined

de:
Resource Title: Observational data
    Size: 4448 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a014f39f-34c3-499f-9627-0efde504d2e1/resource/4ea9d0c6-106e-424a-9d61-9fc1dc99f576/download/observations.zip
    Description: The .zip file contains 6 .csv files including all microphysical measurements taken at the Jungfraujoch station during the two periods of interest:

* 25-28 January 2014 : northwesterly-winds (NW)
* 29 January - 1 February 2014 : southeasterly-winds (SE)

The total Ice Crystal Number Concentration (ICNC; reported in L⁻¹) is measured by the two-dimensional stereo hydrometeor spectrometer (2D-S; Lawson et al., 2006) over the size range 10-1280 μm. The total ice water content (IWC; reported in gm⁻³) is also derived by the 2D-S data using the Brown and Francis (1995) mass-diameter relationship. Finally, the quantification of the liquid water content (LWC; reported in gm⁻³) is based on the liquid droplet size distribution data derived from a DMT cloud droplet probe (CDP; Lance et al., 2010) over the size range 2-50 μm. The frequency of the measurements in the .csv files is 30 min, to match the output frequency of the model. Datetime is also reported in the files (DD/MM/YYYY h:mm) in UTC.

__Acknowledgment__: Collection and pre-processing of the microphysical measurements has been performed by Gary Lloyd (gary.lloyd@manchester.ac.uk). More details about the instrumentation during the CLACE2014 campaign can be found in Lloyd et al. (2015).

blowing-snow

display_name: blowing-snow

ice-crystal-number-concentration

display_name: ice-crystal-number-concentration

ice-water-content

display_name: ice-water-content

jungfraujoch

display_name: jungfraujoch

liquid-water-content

display_name: liquid-water-content

secondary-ice-production

display_name: secondary-ice-production

swiss-alps

display_name: swiss-alps

wrf

display_name: wrf


en:
Data associated to the publication "Environmental DNA sequences from aquatic insects indicate freshwater ecosystem state"
 Authors:
Flurin Leugger, Martina Lüthi, Meret Jucker, Virginie Marques, Sarah Thurnheer, Zacharias Kontarakis and Loïc Pellissier
 Contact:
flurinleugger@gmail.com
loic.pellissier@usys.ethz.ch
 Project description:
In this project, we aimed to compare kick-sampling-derived taxa lists and ecosystem state classifications (IBCH: Indece biologique Suisse) with eDNA metabarcoding and CRISPR-Dx. Additionally, we calculated the association with environmental variables and ecosystem state.
We retrieved the kick-sampling-derived data from Haberthür et al. (2021) and Keck et al. (2023). We collected eDNA samples in 36 sites in the Rhine catchment in Switzerland (lowland) which are used for the national monitoring of freshwater in Summer 2024. We extracted the samples in the clean lab at ETH using the protocol described in Leugger et al. (2025). We used the 16S Ins primer (Elbrecht et al. 2016) for metabarcoding and for CRISPR analysis. To select indicator eDNA metabarcoding sequences, we applied a machine learning approach combining LASSO and rpart decision trees. For CRISPR-Dx, we used the rules of Leski et al. (2023) to in silicon predict detections based on the metabarcoding sequences and used the same machine learning approach as for the indicator metabarcoding eDNA sequences (see publication for more details). The data for the environmental variables is from the SWECO25 database (https://zenodo.org/communities/sweco25/records?q=&l=list&p=1&s=10&sort=newest; Külling et al. 2024).
 Folders:
 catchments\:
Catchments in Switzerland, downloaded from https://data.geo.admin.ch/browser/index.html/collections/ch.bafu.wasser-einzugsgebietsgliederung/items/wasser-einzugsgebietsgliederung?.language=en&.asset=asset-wasser-einzugsgebietsgliederung_2056-gpkg-zip
The subfolder upstream_catchments includes the catchment for each buffer size tested.
comparison_classification_methods\:
File with the ecosystem state classification based on kick-sampling, indicator eDNA metabarcoding sequences (using the rpart decision tree) and CRISPR-Dx (using predicted, lab and retrained rpart trees).
 eDNA\:
 Folder metabarcoding\:
Raw and cleaned metabarcoding data (ASVs) per environmental extract (EVE), including taxa information from Ecotag reference data base. Additionally, the rpart trees for indicator sequences only from Ephemeroptera and all EPT are provided.
Columns which might not be self explanatory
taxon: identified taxon
rank: rank of identified taxon
nb_reads: read numbers
EVS and metabarcoding: key columns to link with metadata files
ASV_cleaned_by_site.csv: Read count by site (after cleaning)
ASV_raw_by_site.csv: Read count by site (before cleaning), column 'NC' is for the negative controls
sequence_count_per_site.csv: Comparison of reads per site before and after cleaning/filtering. 'NC' refers again to Negative Controls.
 Folder CRISPR\:
crispr_by_site.csv: Lab-based detection of the guide per site. Maximum number of detections is 6, as each site had two environmental extracts (EVE), and the PCR replicates were combined in 3 pools each. GRN refers to lab internal numbering of CRISPR-Dx assays.
detection_matrix_Ephemeroptera_28_site.csv: Predicted detection based on eDNA metabarcoding sequences and the model of Leski et al 2023 per site.
rpart_model_Ephemeroptera.rds: best performing rpart model for ecosystem state classification using the predicted CRISPR-Dx detections
rpart_model_retrained.rds: best performing rpart model for ecosystem state classification using the actual lab-based CRISPR-Dx detections (thus "retrained" based on actual detections).
 IBCH\:
IBCH_classification_filtered_and_extended.csv: includes IBCH_cat (ecosystem category from kick-sampling with IBCH 2019 categorization).
IBCH_taxa_kick-sampling.csv: contains taxa detect per site (site_code as column name to link with metadata or eDNA data)
kicknet_Trend_IBCH_sites.csv: taxa detected by site, raw data
 metadata\:
Folder containing files with overview of site names, extract number and coordinates. Columns named "metabarcoding" (and "EVS") are keys to link rows from the different tables.
 SWECO25\:
Folder containing raster stack from SWECO25 data (Külling et al. 2024) used in the article and a table with the extracted variable value per site/catchment with the different upstream buffer sizes tested.
site_code: Code for the sampling site, see also metadata to link to EVS
Temperature_avg: annual mean temperature in upstream catchment [°C]
Precip_annual: annual precipitation [mm]
Population_density: mean population density in 25m cells
Traffic_noise: noise levels
EVI, NDVI and LAI: each standard deviation (sd) and average (avg) value per upstream catchment
Natural rivers: proportion of upstream river length which is classified as natural
Little-disturbed_rivers: proportion of upstream river length which is classified as little disturbed
Heavily-disturbed_rivers: proportion of upstream river length which is classified as heavily disturbed
unnatural_rivers: proportion of upstream river length which is classified as unnatural
culverted_rivers: proportion of upstream river length which is culverted
Forest_edges: proportion of forest edges in upstream catchment
Forest: proportion of forest in upstream catchment
Crops: proportion of crops in upstream catchment
Built_environment: proportion of built environment in upstream catchment
buffer: buffer width for upstream catchment area [m]
catchment_area_m2: upstream catchment area in square meters
 References:
Elbrecht, V., Taberlet, P., Dejean, T., Valentini, A., Usseglio-Polatera, P., Beisel, J. N., Coissac, E., Boyer, F., & Leese, F. (2016). Testing the potential of a ribosomal 16S marker for DNA metabarcoding of insects. PeerJ, 2016(4). https://doi.org/10.7717/peerj.1966
Haberthür, M. (2021). Ergebnisse der 3. Erhebung NAWA-Trend Los 2, Makrozoobenthos. https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.bafu.admin.ch/dam/bafu/de/dokumente/wasser/externe-studien-berichte/nawa_trend_biologie_2019-teil_makrozoobenthos.pdf.download.pdf/nawa_trend_biologie_2019-teil_makrozoobenthos.pdf&ved=2ahUKEwjI5crG-IaPAxUF_gIHHQisBZEQFnoECBgQAQ&usg=AOvVaw1QL3GRZdTllPsHLQ6hQtlP
Külling, N., Adde, A., Fopp, F., Schweiger, A. K., Broennimann, O., Rey, P. L., Giuliani, G., Goicolea, T., Petitpierre, B., Zimmermann, N. E., Pellissier, L., Altermatt, F., Lehmann, A., & Guisan, A. (2024). SWECO25: a cross-thematic raster database for ecological research in Switzerland. Scientific Data, 11(1). https://doi.org/10.1038/s41597-023-02899-1
Leski, T. A., Spangler, J. R., Wang, Z., Schultzhaus, Z., Taitt, C. R., Dean, S. N., & Stenger, D. A. (2023). Machine learning for design of degenerate Cas13a crRNAs using lassa virus as a model of highly variable RNA target. Scientific Reports, 13(1), 6506. https://doi.org/10.1038/s41598-023-33494-4
Leugger, F., Lüthi, M., Schmidlin, M., Kontarakis, Z., & Pellissier, L. (2025). Rapid field-based detection of a threatened and elusive species with environmental DNA and CRISPR-Dx. Global Ecology and Conservation, 59, e03518. https://doi.org/10.1016/j.gecco.2025.e03518

en: landuse conservation biodiversity-monitoring river-restoration biomonitoring edna aquatic-invertebrates dna-metabarcoding crispr-dx

envidat

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.

de: EnviDat: das Umwelt-Datenportal

en: Assessing ecosystem state with environmental DNA of aquatic insects

envi

de: Umwelt

en: Environment

de:
Resource Title: IBCH.zip
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/02849ade-2de9-43ce-b2bb-c99b0749f52f/download/ibch.zip
    Description:

de:
Resource Title: catchments.zip
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/352b1914-eab0-44eb-af50-1629b7c59060/download/catchments.zip
    Description:

de:
Resource Title: metadata.zip
    Size: 18080 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/2596f08b-959e-45d1-9aaa-6694b81e43c2/download/metadata.zip
    Description:

de:
Resource Title: Readme.txt
    Size: 6898 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/f477b2e4-59f5-495d-b7fa-7a611161fb36/download/readme.txt
    Description:

de:
Resource Title: comparison_classification_methods.zip
    Size: 1165 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/a64df406-4654-423e-b5d5-a3835fb1ddc1/download/comparison_classification_methods.zip
    Description:

de:
Resource Title: eDNA.zip
    Size: 885782 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/d1e0c8df-b744-44f4-9721-e95e144bd7cc/download/edna.zip
    Description:

aquatic-invertebrates

display_name: aquatic-invertebrates

biodiversity-monitoring

display_name: biodiversity-monitoring

biomonitoring

display_name: biomonitoring

conservation

display_name: conservation

crispr-dx

display_name: crispr-dx

dna-metabarcoding

display_name: dna-metabarcoding

edna

display_name: edna

landuse

display_name: landuse

river-restoration

display_name: river-restoration


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.

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

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

forests

display_name: forests

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

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

en: Sentinel-2 Vegetation Height Model Armenia

de:
Resource Title: S2-VHM_2025_max.tif
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    Format: image/tiff
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    Description:

de:
Resource Title: S2-VHM_2018_max.tif
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    Format: image/tiff
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    Description:

de:
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    Description:

de:
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/692904ba-e575-4e69-8950-288ffe11a952/download/s2-vhm_2023_max.tif
    Description:

de:
Resource Title: S2-VHM_2019_max.tif
    Size: 3178275799 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/7bde832d-dcba-4df3-b2ff-e01941712029/download/s2-vhm_2019_max.tif
    Description:

de:
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    Size: 3178275799 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/511abee2-19eb-45e9-b28c-23a3f8cada46/download/s2-vhm_2017_max.tif
    Description:

de:
Resource Title: S2-VHM_2021_max.tif
    Size: 2834678415 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/5b72d532-7a61-45a2-a09a-d83dfa61f9f2/download/s2-vhm_2021_max.tif
    Description:

de:
Resource Title: S2-VHM_2022_max.tif
    Size: 3178275799 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/f0b468d3-1f4b-454d-93dc-d01a72902001/download/s2-vhm_2022_max.tif
    Description:

de:
Resource Title: S2-VHM_2024_max.tif
    Size: 3189013217 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/98667a97-fe5d-4d46-a0a0-93a0e951c7e4/download/s2-vhm_2024_max.tif
    Description:

armenia

display_name: armenia

cnn

display_name: cnn

foracca

display_name: foracca

forest

display_name: forest

remote-sensing

display_name: remote-sensing

vegetation-height

display_name: vegetation-height


en:
The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.
Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height - 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0.
The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.htmllinks). High vegetation is also derived from the classification and the calculated object height from the point cloud.
Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.
An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.
The work is published in:

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

envidat

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.

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

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

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology


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

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

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

envi

de: Umwelt

de:
Resource Title: HDM heterogeneity scales
    Size: 210551963 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/736a30b8-2b01-40b0-b97f-aba77c2272e6/resource/092b3ec7-afab-4692-9547-1aacba54f89e/download/hdm_heterogeneity_scales.zip
    Description: The Heterogeneity matrics in the Hengduan mountain scale

de:
Resource Title: Eurasia plant family richness map
    Size: 1149466779 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/736a30b8-2b01-40b0-b97f-aba77c2272e6/resource/8b94b856-31b3-42e3-bfb3-e77d7a54e209/download/eurasia_fam.zip
    Description: This is the plant family level richness map at the Eurasia scale calculated from https://www.nature.com/articles/s41597-021-00997-6

de:
Resource Title: Eurasia SR and heteorgeneity
    Size: 33861 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/736a30b8-2b01-40b0-b97f-aba77c2272e6/resource/f80b00d6-7381-4789-873c-c2bb4d2aa932/download/sr_eurasia_revision.csv
    Description: This table is used for the Eurasia scale analyses, including both family level species richness map and different heterogeneity predictors

de:
Resource Title: Plant range map at Hengduan scale
    Size: 635180593 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/736a30b8-2b01-40b0-b97f-aba77c2272e6/resource/95b657e6-4f16-4d44-aea1-6014d6560b54/download/three_models.7z
    Description: This is the plant range map at the Hengduan mountain scale based on species distribution model, elevation, and county level distribution information

de:
Resource Title: Hengduan plant family richness map
    Size: 159254007 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/736a30b8-2b01-40b0-b97f-aba77c2272e6/resource/fd56e712-502c-4c52-9ec4-fe7f0b461393/download/hengduan-plant-richness.zip
    Description: This is the family level plant richness map in the Hengduan mountain scale at 1km resoution

habitat-heterogeneity

display_name: habitat-heterogeneity

hengduan-mountains

display_name: hengduan-mountains

hypervolume

display_name: hypervolume

model-residuals

display_name: model-residuals

phytodiversity

display_name: phytodiversity


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

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

de:
Resource Title: readme_descriptioninputdata.pdf
    Size: 269312 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/3db8ca57-2838-4b26-82a9-9770b75b7096/download/readme_descriptioninputdata.pdf
    Description: Where the environmental and land use input data come from and how they were processed

de:
Resource Title: installAndRunTreeMigWrapperAndGUI_std.R
    Size: 553 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/916c8a50-1d57-4e28-97c8-f50b8a9f7bf7/download/installandruntreemigwrapperandgui_std.r
    Description: R-script to install the TreeMig framework and start the graphical user interface

de:
Resource Title: InstructionsForRunningTheSimulations.pdf
    Size: 68995 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/830a45db-7985-42f6-8864-4961fe5ef5a2/download/instructionsforrunningthesimulations.pdf
    Description: A condensed instruction for running the presented simulations.

de:
Resource Title: InstructionsForRunningTheSimulations.pdf
    Size: 127302 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/32c74611-c61e-48be-998b-6722bbb697c8/download/instructionsforrunningthesimulations.pdf
    Description: A condensed instruction for running the presented simulations.

de:
Resource Title: ExampleApplicationsTreeMig.zip
    Size: 1676301558 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/609e85c8-98d0-4043-be91-48bd32c162ea/download/exampleapplicationstreemig.zip
    Description: 1) Simulation environment TMEnvironmentExampleApplications for the simulation of the case study applications. It contains a) simulation settings (folder C),  b) species parameters (folder P), c) folders S, R and P for the state files and results and plots to be produced, and the binaries of the TreeMig model.
2) Input data for Switzerland in TMInputData (Climate, soil, land cover, DEM)
3) Several R scripts (folder Scripts) to run and plot the simulations directly from R without having to use the graphical user interface.
For learning how to use TreeMig we recommend the starter kit https://www.doi.org/10.16904/envidat.533.

de:
Resource Title: ExampleApplicationsTreeMig.zip
    Size: 1686005000 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/0f59997f-3e90-4b25-9e5b-37b641d9769b/download/exampleapplicationstreemig.zip
    Description: 1) Simulation environment TMEnvironmentExampleApplications for the simulation of the case study applications. It contains a) simulation settings (folder C),  b) species parameters (folder P), c) folders S, R and P for the state files and results and plots to be produced, and the binaries of the TreeMig model.
2) Input data for Switzerland in TMInputData (Climate, soil, land cover, DEM)
3) Several R scripts (folder Scripts) to run and plot the simulations directly from R without having to use the graphical user interface.
For learning how to use TreeMig we recommend the starter kit https://www.doi.org/10.16904/envidat.533.

bioclimatic-variables

display_name: bioclimatic-variables

climate-change

display_name: climate-change

example-applications

display_name: example-applications

forest-dynamics

display_name: forest-dynamics

forest-management

display_name: forest-management

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-migration

display_name: species-migration

treemig

display_name: treemig


en:
Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes, the Gayraca Bay data set.
The Gayraca bay is located in the Tayrona National Natural Park along the continental Colombian Caribbean coast bordering the Sierra Nevada de Santa Marta. Tayrona Park has a heterogeneous coastal topography composed of metamorphic rocks, with numerous rocky headlands, islets, and bays (Garzón-Ferreira & Díaz, 2003). Coral and other hard-bottom communities are distributed along the coast, mainly as fringing reefs, while seagrass beds, mangroves, and coral reefs have developed to some extent in sheltered conditions within the bays (Garzón-Ferreira & Cano, 1991). The study was carried out in Gayraca Bay, where corals on the exposed side exhibit mainly massive to encrusting growth forms with colonies and a reef-like structure. In Gayraca Bay, we sampled two filtration replicates from each of six stations, for a total of 12 water samples, from 23 to 26 October 2018. We recorded the GPS coordinates at the start and end of the transect. We conducted eDNA sampling by using a filtration device composed of an Athena® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.0 L/min), a VigiDNA® 0.20 μM cross-flow filtration capsule (SPYGEN, le Bourget du Lac, France) and disposable sterile tubing for each filtration capsule. We performed two filtration replicates in parallel on each side of a boat, at each station, for 30 min corresponding to a volume of ~30 L of water filtered by each capsule. At the end of each filtration, the water inside the capsules was emptied, and we filled the capsules with 80 ml of CL1 Conservation buffer (SPYGEN, le Bourget du Lac, France) and stored at room temperature. We followed a strict contamination control protocol in both field and laboratory stages (Valentini et al., 2016). Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).
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)

en: marine biomonitoring teleo biodiversity vert01 chon01 edna reef-fish environmental-dna caribbean-region

en: Environmental DNA Marine Colombia Gayraca 2018

envi

en: Environment

biodiversity

display_name: biodiversity

biomonitoring

display_name: biomonitoring

caribbean-region

display_name: caribbean-region

edna

display_name: edna

environmental-dna

display_name: environmental-dna

marine

display_name: marine

reef-fish

display_name: reef-fish

teleo

display_name: teleo


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

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

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

active-layer

display_name: active-layer

arctic

display_name: arctic

bacteria

display_name: bacteria

biobank

display_name: biobank

collections

display_name: collections

conservation

display_name: conservation

extreme-habitat

display_name: extreme-habitat

fungi

display_name: fungi

glaciers

display_name: glaciers

permafrost

display_name: permafrost

plastisphere

display_name: plastisphere

swiss-alps

display_name: swiss-alps


en: Data of 13C-pulselabeling experiments in a young beech model ecosystem and a mature pine forest exposed to drought and rewetting. Data include microclimate during the experiment, total PLFA contents, d13C signatures in PLFA , microbial biomass, soil-respired CO2 measure during 30 days following 3-4 hour long pulse labeling with highly 13C-enriched CO2.

en: drought microbial-biomass forest biogeochemical-cycling c13-labeling plfa

en: Gao_Drought resistance and resilience of rhizosphere communities_13C-PLFA

biogeochemical-cycling

display_name: biogeochemical-cycling

drought

display_name: drought

forest

display_name: forest

microbial-biomass

display_name: microbial-biomass

plfa

display_name: plfa