en:
**Article: "Species Species composition shapes canopy microclimate and foliar traits without modifying photosynthetic thermal acclimation in a beech-oak ecotone"**
Authors: **Janisse Deluigi**, Charlotte Grossiord, Alvaro Poretti, Lorenz Walthert, Christoph Bachofen
The study was conducted along a south-exposed elevational gradient ranging from 770 to 860 m a.s.l. in a central alpine valley (46°10`14``N, 7°10`01``E, Saillon, Switzerland). Along the elevational gradient, we selected three forest stands characterized by high coverage of mature European beech (Fagus sylvatica L.) and downy oak (Quercus pubescens Willd.) trees, with are naturally distributed in three species combinations: one pure beech stand (i.e., at least 80% of the basal area) at 860 m a.s.l. , one mixed beech-oak stand (i.e., similar relative abundances of the two species – at least 40% of the basal area each – and minimal presence of other tree species) at 785 m a.s.l., and one pure oak stand (i.e., at least 80% of the basal area) at 770 m a.s.l. An additional forest clearing of around 0.04 ha next to the pure oak stand was selected as reference for conditions outside the forest  (i.e., clearing or gap). At each forest stand, we selected five healthy, mature, overstory trees per tree species, resulting in a total of 20 individuals (5 individuals x 2 species x 2 mixture combinations, i.e., mixed vs. pure forest stand).
**Dataset 1: Climatic conditions at the study sites**
In 2023 and 2024 we monitored climatic conditions at each forest stand and in the clearing using climatic stations with air temperature, relative humidity and radiation sensors installed at 1.8 m height in a central position relative to the selected trees for each site. The meteo station in the clearing site had additionally a rain gauge and wind sensor.
**Dataset 2: Tree and canopy sizes**
Once in 2023 we measured tree height and diameter at breast height of all selected trees using a vertex hypsometer and a caliper. In August 2023 and 2024 we estimated canopy size measuring the height at which canopy starts (CS), canopy depth (= tree height - CS) and maximum canopy radius as the average of two orthogonal measurements (south and east direction) using a vertex hypsometer. We also estimated canopy total leaf area using species- and site-specific allometric equations from collected branches upscaled to the tree level.
**Dataset 3: Microclimatic conditions measured in tree canopies**
We monitored canopy microclimatic conditions in 2023 and 2024 attaching one air temperature and air humidity sensor (HOBO) on one branch at the top and one branch at the bottom of the south-exposed side of the canopy for each selected tree in June 2023.
**Dataset 4: Physiological traits**
During three field campaigns (June, August, September) per year (2023, 2024) we measured temperature response curves of leaf net photosynthesis and dark respiration. As in-situ measurements were not possible, tree-climbers collected one representative branch at the top and the bottom of the canopy from each selected tree at each site. Estimates of the thermal optimum of photosynthesis (Topt), photosynthetic rate at Topt (Aopt), respiration rate at a standard temperature of 25°C (R25) and thermal sensitivity of respiration (Q10) were extracted from the temperature response curves of photosynthesis and respiration, respectively.
Leaf water potentials at predawn and midday, as well as leaf chlorophyll content, were also measured on the same day for each selected tree at each site.
**Dataset 5: Physiological traits - specific leaf area and leaf size**
In August 2023 and 2024 we collected a random subset of 10 representative fully developed leaves of different sizes from a representative branch harvested at the top and bottom of the canopy of each selected tree to estimate the specific leaf area (SLA) and leaf size (average of the 10 leaves).
**Dataset 6: Soil water potentials**
Hourly soil water potentials and soil temperature were recorded during the two years of measurements (2023, 2024) at each forest site at different soil depths: 20 cm, 80 cm, 140 cm and 200 cm.

en: Dataset Article "Species composition shapes canopy microclimate and foliar traits without modifying photosynthetic thermal acclimation in a beech-oak ecotone", Deluigi et al.

en: forest-canopy climate-change photosynthesis respiration species-interaction acclimation

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: 3_Microclimatic measurements day and night Saillon 2023-2024.csv
    Size: 179804 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/69ce25f7-f502-4461-8336-0e0400182d03/download/3_microclimatic-measurements-day-and-night-saillon-2023-2024.csv
    Description: #Dataset 3: Microclimatic conditions measured in tree canopies
Site	= forest site (pure beech, mixture, pure oak)
Species	= tree species (B = beech, O = downy oak)
Tree = tree ID within each site (5 trees per site and tree species)
Time = time of the day (HH:MM:SS)
Tair_top	= air temperature at the top of the canopy (°C)
Tair_low	= air temperature at the bottom of the canopy (°C)
VPD_top = vapor pressure deficit at the top of the canopy (kPa)
VPD_low = vapor pressure deficit at the bottom of the canopy (kPa)
Tair_gap	= air temperature in the clearing (i.e., outside the forest) (°C)
VPD_gap = vapor pressure deficit in the clearing (kPa)
Tair_T_L	= difference in air temperature between the top and bottom of the canopy (°C)
VPD_T_L = difference in vapor pressure deficit between the top and bottom of the canopy (kPa)
Tair_T_gap	 =  difference in air temperature between the top of the canopy and the clearing (°C)
VPD_T_gap = difference in vapor pressure deficit between the top of the canopy and the clearing (kPa)
Tair_L_gap	= difference in air temperature between the bottom of the canopy and the clearing (°C)
VPD_L_gap = difference in vapor pressure deficit between the bottom of the canopy and the clearing (kPa)

en:
Resource Title: 2_Tree size and canopy Saillon 2023-2024.csv
    Size: 4465 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/b1b06195-b354-404b-b2e2-c44217446c54/download/2_tree-size-and-canopy-saillon-2023-2024.csv
    Description: #Dataset 2: Tree and canopy sizes
Year = year of measurement
Site = forest site (pure beech, mixture, pure oak)
Species = tree species (B = beech, O = downy oak)
Tree = tree ID within each site (5 trees per site and tree species)
height = tree height (m)
DBH = diameter at breast height (cm)
sapwood_depth = depth of sapwood from tree cores (cm)
sapwood_area = area of sapwood in tree trunks (cm2)
Al_As = ratio between branch total leaf area (Al, cm2) and branch section area (As, cm2) from sampled branches
WT_LA = canopy total leaf area estimated for each tree using allometric equations (m2)
radius_max = average maximum radius of the canopy (m) from two orthogonal measurements
radius_east = radius of the canopy (m) measured direction east
radius_south = radius of the canopy (m) measured direction south
crown_start = height at which the canopy starts (m)
canopy_depth = difference between tree height and height at which the canopy starts (m)
Asymetric = TRUE/FALSE to indicate whether the canopy was asymetric
Direction = direction to which the canopy was asymetric

en:
Resource Title: 1_Climatic conditions at the study sites Saillon 2023-2024.csv
    Size: 22240297 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/85ccee2d-7e73-40b2-ad63-c6ce1b030e26/download/1_climatic-conditions-at-the-study-sites-saillon-2023-2024.csv
    Description: #Dataset 1: Climatic conditions at the study sites
Datetime = date and time (CEST)
PAR	= photosynthetic photon flux density (µmol m-2 s-1)
RH = air relative humidity (%)
Temp = air temperature (°C)
site = forest site (pure beech, mixture, pure oak) and clearing (gap)
date = date (mm/dd/yyyy)
Precip	= rain precipitation (mm)
Wind = wind speed (m s-2)
Gusts = wind gusts (m s-2)
Direction = direction of the wind (°)
month = month of the year (1:12)
VPD = vapor pressure deficit (kPa)
Time = time of the day (HH:MM:SS)
Year = year of measurement

en:
Resource Title: 4_Physiological measurements Saillon 2023-2024.csv
    Size: 46961 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/e9f0e85f-b12b-4b29-a4e3-44600d37c34a/download/4_physiological-measurements-saillon-2023-2024.csv
    Description: #Dataset 4: Physiological traits
Year = year of measurement
Campaign = campaign of measurement (June, August, September)
Site	= forest site (pure beech, mixture, pure oak)
Species	= tree species (B = beech, O = downy oak)
Tree = tree ID within each site (5 trees per site and tree species)
Canopy_level = canopy level (top, bottom)
WP_predawn = leaf water potential at predawn (MPa)
WP_midday = leaf water potential at midday (MPa)
CHL = chlorophyll content (CCI)
Date = date of measurement (mm/dd/yyyy)
Aopt = photosynthetic rate at thermal optimum (µmol m-2 s-1)
sd_err_Aopt = standard error of Aopt estimates from temperature response curves of photosynthesis
b	= curvature coefficient of the temperature response curve of photosynthesis
sd_err_b = standard error of b estimates  from temperature response curves of photosynthesis
Topt = thermal optimum of photosynthesis (°C)
sd_err_Topt = standard error of Topt estimates from temperature response curves of photosynthesis
R25	= respiration rate at a standard temperature of 25°C (µmol m-2 s-1)
sd_err_R25 = standard error of R25 estimates from temperature response curves of dark respiration
Q10	= thermal sensitivity of respiration (i.e., increase in respiration rates for a 10°C increase in measurement temperature)
sd_err_Q10 = standard error of Q10 estimates from temperature response curves of dark respiration
Collection_time = time of the day at which the branch was collected (morning, midday)

en:
Resource Title: 6_Soil moisture and temperature Saillon 2023-2024.csv
    Size: 16861102 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/305fbfa7-56fa-4f62-88d2-f5b87cb51452/download/6_soil-moisture-and-temperature-saillon-2023-2024.csv
    Description: #Dataset 6: Soil water potentials
Datetime = date and time (CEST)
Site	= forest site (pure beech, mixture, pure oak)
Depth	= soil depth (cm)
WP_corr	 = soil water potential corrected by temperature (MPa)
Temp = soil temperature (°C)
Date = date of measurement (mm/dd/yyyy)
Year = year of measurement

en:
Resource Title: 5_SLA and leaf size measurements Saillon 2023-2024.csv
    Size: 4444 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/b825d938-273b-43a5-83a8-9d5c89c895e5/download/5_sla-and-leaf-size-measurements-saillon-2023-2024.csv
    Description: #Dataset 5: Physiological traits - specific leaf area and leaf size
Year = year of measurement
Site	= forest site (pure beech, mixture, pure oak)
Species	= tree species (B = beech, O = downy oak)
Tree = tree ID within each site (5 trees per site and tree species)
Canopy_level = canopy level (top, bottom)
SLA	= specific leaf area (cm2 g-1)
leaf_size = leaf area (cm2)

acclimation

display_name: acclimation

climate-change

display_name: climate-change

forest-canopy

display_name: forest-canopy

photosynthesis

display_name: photosynthesis

respiration

display_name: respiration

species-interaction

display_name: species-interaction


$oid: OBSOLETE

en:
**Schweizweite Karte des sky view factors (Kronenöffnungsgrad im Wald) in 5 m Auflösung (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.
Dieser Datensatz enthält eine schweizweite Karte mit einer Auflösung von 5 m, die den sky view factor darstellt. In Wäldern gibt sie den Kronenöffnungsgrad während der Vegetationsperiode wieder, ausserhalb von Wäldern zeigt die Karte lediglich den Terrain-Effekt. Wichtig: Der Kronenöffnungsgrad wurde anhand einer 180° Perspektive ermittelt, was dazu führt, dass die Werte nicht direkt vergleichbar sind mit Schätzungen im Feld, welche meistens auf einem kleineren Sichtradius basieren. Der Datensatz wurde mit dem auf synthetischen hemisphärischen Bildern basierendem Raytracing-Canopy-Radiation-Modell (CanRad) berechnet, unter Verwendung des LiDAR-basierten Vegetationshöhenmodells gemäss LFI (Stand 2024). Die Karte stellt die Bedingungen zum Zeitpunkt der LiDAR Datenerfassung dar. Informationen zu den Jahren der LiDAR-Erfassung finden Sie im Abschnitt „Data and ressources“. Der Datensatz wird jährlich aktualisiert, sobald ein neues LFI-LiDAR-Vegetationshöhenmodell verfügbar ist
**Carte nationale du facteur de vue du ciel (degré d'ouverture de la canopée dans la forêt) avec une résolution de 5 m (état IFN VHM 2024)**
Cet ensemble de données contient une carte couvrant l'ensemble du territoire suisse, avec une résolution de 5 m, qui représente le « sky view factor ». Dans les forêts, elle reflète le degré d'ouverture de la canopée pendant la période de végétation ; en dehors des forêts, la carte indique uniquement l'effet du relief. Important : le degré d'ouverture de la canopée a été déterminé à partir d'une perspective à 180°, ce qui signifie que les valeurs ne sont pas directement comparables aux estimations effectuées sur le terrain, qui se basent généralement sur un rayon de vision plus petit. L'ensemble de données a été calculé à l'aide du modèle CanRad (Raytracing-Canopy-Radiation), basé sur des images hémisphériques synthétiques, en utilisant le modèle altimétrique de végétation basé sur le LiDAR selon l'IFN (état en 2024). La carte représente les conditions au moment de la collecte des données LiDAR. Vous trouverez des informations sur les années de collecte LiDAR dans la section « Données et ressources ». L'ensemble de données est mis à jour chaque année dès qu'un nouveau modèle d'altitude de la végétation LFI-LiDAR est disponible.
**Mappa svizzera dello sky view factor (grado di apertura della chioma nella foresta) con risoluzione di 5 m (stato LFI VHM 2024)**
Questo set di dati contiene una mappa a copertura nazionale con una risoluzione di 5 m che rappresenta lo sky view factor. Nelle aree boschive essa riflette il grado di apertura della chioma durante il periodo vegetativo, mentre al di fuori delle aree boschive la mappa mostra semplicemente l'effetto del terreno. Importante: il grado di apertura della chioma è stato determinato sulla base di una prospettiva a 180°, il che comporta che i valori non siano direttamente comparabili con le stime sul campo, che si basano per lo più su un raggio visivo più ridotto. Il set di dati è stato calcolato con il modello Raytracing-Canopy-Radiation (CanRad), basato su immagini emisferiche sintetiche, utilizzando il modello altimetrico della vegetazione basato su LiDAR secondo l'IFN (aggiornato al 2024). La mappa rappresenta le condizioni al momento dell'acquisizione dei dati LiDAR. Le informazioni relative agli anni di acquisizione LiDAR sono disponibili nella sezione «Data and resources». Il set di dati viene aggiornato annualmente non appena è disponibile un nuovo modello di altezza della vegetazione LFI-LiDAR.
**Switzerland-wide map of the sky view factor (canopy openness in forests) with 5 m resolution (as of NFI VHM 2024)**
This dataset contains a map covering the whole of Switzerland with a resolution of 5 m, showing the sky view factor. In forests, it reflects the degree of canopy openness during the growing season; outside forests, the map simply shows the terrain effect. Important: The degree of canopy openness was determined using a 180° perspective, which means that the values are not directly comparable with field estimates, which are usually based on a smaller field of view. The dataset was calculated using the Raytracing Canopy Radiation model (CanRad), which is based on synthetic hemispheric images, utilising the LiDAR-based vegetation height model in accordance with the LFI (as of 2024). The map depicts conditions at the time of LiDAR data acquisition. Information on the years of LiDAR acquisition can be found in the ‘Data and resources’ section. The dataset is updated annually as soon as a new LFI LiDAR vegetation height model becomes available.

en: Switzerland-wide maps of sky view factor (canopy openness in forests) at 5 m resolution (as of NFI VHM 2024)

en: lidar canopy-openness canrad forestry light-availability

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

fr: Environnement

it: Ambiente

en:
Resource Title: Lidar_Befliegungsjahr_VHM_2024.png
    Size: 536750 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/fa3b2b7f-c0f0-4fd0-b04e-78b0b60ebb4b/resource/d55237b2-558a-49c5-a26d-7c558f5a54c8/download/lidar_befliegungsjahr_vhm2024.png
    Description:

format: PNG

media_type: image/png

en:
Resource Title: Sky_view_factor_CH_LI_2024.tif
    Size: 1642824991 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/fa3b2b7f-c0f0-4fd0-b04e-78b0b60ebb4b/resource/ca5d0cc0-1838-4e3d-90da-4c5e0b1992ff/download/map_chli.tif
    Description:

format: TIFF

media_type: image/tiff

canopy-openness

display_name: canopy-openness

canrad

display_name: canrad

forestry

display_name: forestry

lidar

display_name: lidar

light-availability

display_name: light-availability


en:
The dataset contains a time series of countrywide Sentinel-1 synthetic aperture radar (SAR) composites for Switzerland and Liechtenstein starting in 2015. The temporal sampling step of the time series is 6 days and the compositing period 12 days resulting in 60 generated composites per year. Each composite contains the amplitude information of C-band backscatter and features a grid spacing of 10 m. The composites were generated according to the local resolution weighting (LRW) methodology described in Small et al. (2022, https://doi.org/10.1109/TGRS.2021.3055562). For the generation of each composite, backscatter information from all Sentinel-1 acquisitions of Switzerland and Liechtenstein during the compositing period was obtained.
The naming of each composite file follows the logic “S1_LRW_PD\-LRW compositing period in days\-\_SP\-Temporal sampling step in days\-_\-Start date and time for LRW composite generation\--\-End date and time for LRW composite generation\-.tif”, e.g. “S1_LRW_PD12_SP6_20150101000000-20150112235959.tif” for the first file. Each file contains four raster layers: 1) Vertically submitted and horizontally received (VH) and 2) vertically submitted and vertically received (VV) polarisation backscatter in the logarithmic dB scale, 3) composite local resolution, which is a measure for the quality of the pixel (for details, see Small et al. (2022, https://doi.org/10.1109/TGRS.2021.3055562), and 4) the number of contributing observations.
Contains modified Copernicus Sentinel data.

en: Sentinel-1 Synthetic Aperture Radar Composite Backscatter Time Series of Switzerland

en: radar c-band switzerland earth-observation sar time-series remote-sensing sentinel-1

c-band

display_name: c-band

earth-observation

display_name: earth-observation

radar

display_name: radar

remote-sensing

display_name: remote-sensing

sar

display_name: sar

switzerland

display_name: switzerland

time-series

display_name: time-series


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

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

envidat

en: EnviDat: the Environmental Data Portal

en:
Resource Title: Verjüngungspotential_LFI_2024.tif
    Size: 163577856 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/c9b1019d-78cf-49b5-b065-25d8e2dd2700/resource/2a3adf57-5d17-4c04-897b-13c2c94149b9/download/verjpot_ch_18122025.tif
    Description:

en:
Resource Title: Verjüngungspotentialkarte_Kurzbeschrieb.pdf
    Size: 364954 bytes
    Format: application/pdf
    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

format: PDF

media_type: application/pdf

en:
Resource Title: LiDAR_Befliegungsjahre_VHM_LFI_2024.png
    Size: 201083 bytes
    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.

forest-management

display_name: forest-management

protection-forest

display_name: protection-forest

rejuvenation

display_name: rejuvenation


en: Gas exchange, online isotope discrimination, and chlorophyll fluorescence measurements were conducted on fully expanded leaves of grey alder (*Alnus incana*) and holm oak (*Quercus ilex*) tree saplings under different fractions of blue light (0–100%). Blue light was supplemented with red light to maintain a constant total light intensity. A detailed description of the measurements is provided in Diao *et al.* (2025) (https://doi.org/10.1093/jxb/eraf305). These measurements generated a dataset describing the responses of key leaf physiological parameters to varying blue/red light fractions, including net photosynthetic rate, stomatal conductance, mesophyll conductance, the maximum carboxylation rate of RuBP, non-photochemical quenching, etc. The dataset was further used for photosynthetic limitation analysis to disentangle the relative biochemical, mesophyll, and stomatal limitations on photosynthesis under different light-quality conditions. The results of the photosynthetic limitation analysis are also provided here.

en: Light-quality responses of photosynthetic, stomatal, and mesophyll traits of Alnus incana and Quercus ilex

en: stomata light-quality gas-exchange fluorescence photosynthesis

envi

en: Environment

format: XLS

fluorescence

display_name: fluorescence

gas-exchange

display_name: gas-exchange

light-quality

display_name: light-quality

photosynthesis

display_name: photosynthesis

stomata

display_name: stomata


en:
This dataset contains bryophyte species count data and information about the observers bryophyte expertise for 2950 relevés conducted from 2011 to 2023 on 10-m2 plots in a long-term monitoring program in Switzerland. Plots were situated in raised bogs and fens of national importance, which were distributed across the whole country. The majority of the plots is represented by two relevés as sites are revisited every six years. The dataset was used in the paper mentioned below to test if species richness estimates differed among categories of observer expertise.
Moser T, Boch S, Bedolla A, Ecker KT, Graf UH, Holderegger R, Küchler H, Pichon
NA, Bergamini A (2025) Greater observer expertise leads to higher liverwort species richness in bryophyte surveys. _Journal of Vegetation Science_.

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

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.

format: CSV

biodiversity-monitoring

display_name: biodiversity-monitoring

cover

display_name: cover

fen

display_name: fen

liverworts

display_name: liverworts

mosses

display_name: mosses

observer-bias

display_name: observer-bias

raised-bog

display_name: raised-bog

sphagnum

display_name: sphagnum

vegetation-survey

display_name: vegetation-survey


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

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

en:
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.

format: ZIP

media_type: application/zip

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

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

en:
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).

en:
Resource Title: data_IFM_RDFM.zip
    Size: 1202590843 bytes
    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.

en:
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

en:
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))

en:
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) .

en:
Resource Title: data_IFM_RDFM.zip
    Size: 1191853425 bytes
    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:

en:
Resource Title: node_RunOfRiver_15min.ftr
    Size: 527832187 bytes
    Format: application/octet-stream
    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

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: In the context of landslide early warning research, soil moisture (e.g. volumetric soil water content and soil water potential) has been measured at six locations in the Napf-region (Emmental, central Switzerland) since April 2019. Here, we provide the hourly mean values of all sensors from all 6 locations after a comprehensive data control, filtering and aggregation (from 10-min to 1-hour values). A sub-set of these data (for the locations Wasen i.E.) has previously been published in envidat.ch at https://www.doi.org/10.16904/envidat.369 applying the same data post-processing method. The dataset is complemented with meteorological data (locally measured air temperature and precipitation) as well as soil temperature. A detailed description of the sites and measurement installations can be found in a separate report (attached with the data).

en: In-situ soil moisture measurements Napf-region

en: landslide-early-warning soil-temperature soil-water-content soil-moisture soil-water-matrix-potential

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: In-situ soil moisture measurements, Napf, since 2021
    Size: 9982444 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/60d27b8c-1004-40fa-ab26-da593cd90e74/resource/ded05973-051e-438f-a123-0bf18a65eab6/download/soil_moisture_data_napf_napf_1.csv
    Description: Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each. Meteorological variables such as air temperature or precipitation can be received from MeteoSwiss.
Time interval: 1 hour.
Measurement period: since June 2021
Location: flat terrain, meadow (official MeteoSwiss station)

en:
Resource Title: In-situ soil moisture measurements, Wasen i.E., flat location, since 2019
    Size: 13662945 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/60d27b8c-1004-40fa-ab26-da593cd90e74/resource/49ea6359-5735-42fd-adea-2db3ce46328d/download/soil_moisture_data_wasen_i.e._flat_terrain_emm_2.csv
    Description: Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, and two additional soil temperature measurements (oC), air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: since April 2019.
Location: flat terrain, meadow
For a detailed description of site, soil and vegetation: see attached document.
Updates 2025:
The “precipitation” column was set to not available (NA) for periods when the pluviometer was identified as clogged. These periods were assigned (i) based on regular maintenance of the measurement station and (ii) irregular behavior of the measurement series compared to the precipitation signal from the other nearby measurement stations.
Column “VWC_0.7m_right” and “ST_5TE_0.7m_right” were set to not available (NA) for the period after the 2023-06-19T14:00:00Z due to irregular behavior of the ECH2O - 5TE sensor.

media_type: text/csv

en:
Resource Title: Detailed description of soil moisture measurment sites, soil properties and sensor installations (in German)
    Size: 5211423 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/60d27b8c-1004-40fa-ab26-da593cd90e74/resource/f9981d88-aaaa-4e89-8abf-6a6357194cf6/download/detaillierte-beschreibung-standorte-und-bodeneigenschaften-pilotstudie-napf.pdf
    Description:

en:
Resource Title: In-situ soil moisture measurements, Langnau i E, since 2021
    Size: 9919529 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/60d27b8c-1004-40fa-ab26-da593cd90e74/resource/4df60c67-a0b9-4d1e-952d-11913104c1cb/download/soil_moisture_data_langnau_i.e._napf_3.csv
    Description: Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each. Meteorological variables, such as air temperature or precipitation can be received from MeteoSwiss.
Time interval: 1 hour. Measurement period: since June 2021.
Location: flat terrain, meadow (measurement site of MeteoSwiss)
For a detailed description of site, soil and vegetation: see attached document.

en:
Resource Title: In-situ soil moisture measurements, Ranflüh, since 2021
    Size: 9751757 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/60d27b8c-1004-40fa-ab26-da593cd90e74/resource/ae6122ba-cbd3-4269-a140-f9d196d6046c/download/soil_moisture_data_ranflueh_napf_2.csv
    Description: Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, as well as air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: since April 2021.
Location: hillslope, meadow
For a detailed description of site, soil and vegetation: see attached document.
Update 2025:
The “precipitation” column was set to not available (NA) for periods when the pluviometer was identified as clogged. These periods were assigned (i) based on regular maintenance of the measurement station and (ii) irregular behavior of the measurement series compared to the precipitation signal from the other nearby measurement stations.

en:
Resource Title: In-situ soil moisture measurements, Wasen i.E., steep slope, since 2019
    Size: 13820232 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/60d27b8c-1004-40fa-ab26-da593cd90e74/resource/526d9589-dbbd-4e7c-99f5-3f399c139840/download/soil_moisture_data_wasen_i.e._hillslope_emm_1.csv
    Description: Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, and two additional soil temperature measurements (oC), air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: since April 2019.
Location: steep hillslope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en:
Resource Title: In-situ soil moisture measurements, Ranflüh, 2021-2023
    Size: 6994002 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/60d27b8c-1004-40fa-ab26-da593cd90e74/resource/6526d0fe-255b-47ba-ae37-c5614500e6ba/download/soil_moisture_data_ranflueh_napf_2.csv
    Description: Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, as well as air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2021-Dec 2023.
Location: hillslope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en:
Resource Title: In-situ soil moisture measurements, Trub, since 2021
    Size: 8975811 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/60d27b8c-1004-40fa-ab26-da593cd90e74/resource/00b3d08d-369e-421e-8b0a-09a874211b1b/download/soil_moisture_data_trub_napf_4.csv
    Description: Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, as well as air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: since April 2021.
Location: slope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en:
Resource Title: In-situ soil moisture measurements, Wasen i.E., flat location, 2019-2023
    Size: 11901338 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/60d27b8c-1004-40fa-ab26-da593cd90e74/resource/668b78c4-9473-47f5-8d58-721c4be5e30d/download/soil_moisture_data_wasen_i.e._flat_terrain_emm_2.csv
    Description: Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, and two additional soil temperature measurements (oC), air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2019-Dec 2023.
Location: flat terrain, meadow
For a detailed description of site, soil and vegetation: see attached document.

landslide-early-warning

display_name: landslide-early-warning

soil-moisture

display_name: soil-moisture

soil-temperature

display_name: soil-temperature

soil-water-content

display_name: soil-water-content

soil-water-matrix-potential

display_name: soil-water-matrix-potential


en: This dataset contains number concentrations of ice-nucleating particles active at -15 °C observed at Weissfluhjoch during February and March 2019, as well as complementary data (measured aerosol number concentrations and modelled total precipitation along air mass trajectories). This data formed the basis of our paper with the title “Towards parameterising atmospheric concentrations of ice-nucleating particles active at moderate supercooling”.

en: Ice nucleating particle concentrations active at -15 °C at Weissfluhjoch

en: backward-trajectories aerosol raclets ice-nucleation clouds

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:
Resource Title: INP15_WFJ_version1
    Size: 11030 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/abf0b918-c606-4d3b-99cb-e503e99435f6/resource/28c08311-01f4-4245-a139-f867cfe69677/download/inp15_wfj.csv
    Description:

en:
Resource Title: INP15_WFJ_version2.csv
    Size: 12704 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/abf0b918-c606-4d3b-99cb-e503e99435f6/resource/3fd9cf7f-1b00-4e70-be8d-eb3b70c4ad03/download/inp15_wfj_version2.csv
    Description: This dataset version includes revised formatting for the start and end time fields.

aerosol

display_name: aerosol

backward-trajectories

display_name: backward-trajectories

clouds

display_name: clouds

ice-nucleation

display_name: ice-nucleation

raclets

display_name: raclets


restwasser-datenbank

en:
(in English below)
Der erweiterte Ausbau der Wasserkraftproduktion steht im Spannungsfeld mit der Sicherstellung ökologischer Funktionen von Fliessgewässern. Die neu entwickelte Restwasser-Datenbank bietet eine öffentlich zugängliche Grundlage zu den Restwasserbestimmungen der Schweizer Wasserkraftanlagen (WKA). Sie umfasst 252 WKA mit einer installierten Leistung von mindestens 3 MW, darunter 160 Laufwasserkraftwerke, 75 Speicherkraftwerke, 16 Pumpspeicherkraftwerke und ein Umwälzkraftwerk. Die erwartete jährliche Stromproduktion dieser Anlagen (ohne Umwälzbetrieb) beträgt 31’540 GWh, was rund 76 % der gesamten schweizerischen Wasserkraftproduktion entspricht. Die Datenbank umfasst rechtliche, hydrologische und technische Attribute und erlaubt dank ihrer kraftwerksspezifischen Struktur eine einfache Verknüpfung mit der schweizerischen Wasserkraftstatistik (WASTA). Dadurch wird die effiziente Bearbeitung hydro-energetischer Fragestellungen ermöglicht.
Eine öffentlich zugängliche Datengrundlage ist insbesondere vor dem Hintergrund des Klimawandels relevant, da dieser die hydrologischen Verhältnisse verändert und Nutzungskonflikte um Wasserressourcen verschärft. Gleichzeitig steigt der Druck auf aquatische Lebensräume, unter anderem infolge steigender Wassertemperaturen. Die Restwasser-Thematik betrifft nicht nur das Spannungsfeld zwischen Energieproduktion und Gewässerökologie, sondern auch Aspekte der Wasserqualität sowie weitere Nutzungen wie Trinkwasserversorgung, landwirtschaftliche Bewässerung, Kühlung und den Betrieb von Abwasserreinigungsanlagen (ARAs). Für eine nachhaltige Wasserwirtschaft ist die Etablierung öffentlicher und belastbarer Datengrundlagen unerlässlich, da sie eine Quantifizierung und Abwägung divergierender Interessen ermöglicht. Nur auf dieser Basis lässt sich die bislang emotional geführte Debatte versachlichen und können Entscheidungsprozesse evidenzbasiert unterstützt werden, die eine zukunftsfähige und nachhaltige Wasserwirtschaft fördern.
The planned expansion of hydropower in Switzerland conflicts with the need to preserve the ecological functions of rivers. To support informed decision-making, a publicly accessible database on environmental flows has been developed. It documents the environmental flow requirements for 252 hydropower plants with an installed capacity of ≥ 3 MW, including 160 run-of-river plants, 75 storage plants, 16 pumped-storage plants, and one closed-loop pumped-storage plant. Together, these plants have an expected annual electricity production of 31’540 GWh—around 76% of Switzerland’s total hydropower generation.
The database contains legal, hydrological, and technical attributes and can be easily linked to the national hydropower statistics (WASTA), enabling efficient analysis of hydroelectric issues. In the context of climate change, such an open database is crucial, as shifting hydrological conditions intensify competing water uses and increase pressure on aquatic ecosystems. Environmental flow requirements involve not only the trade-off between energy production and river ecology but also water quality and other uses such as drinking water supply, irrigation, cooling, and wastewater treatment. Robust, publicly available data are essential for quantifying and balancing these competing interests and for supporting evidence-based decisions in sustainable water resources management.

en: Restwasser-Datenbank

en: wasserwirtschaft dotierwasser restwasser konzessionen wasserkraft

envidat

en: EnviDat: the Environmental Data Portal

title_for_slug: restwasser-datenbank

en:
Resource Title: Dokumentation_Restwasser_Datenbank.pdf
    Size: 1954979 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/58c9e08b-4163-4a0f-a90f-babca8444ae9/resource/9aad39fc-71b3-47e8-a48e-7b7a77294c85/download/dokumentation_restwasser_datenbank_jan26.pdf
    Description:

en:
Resource Title: Restwasserstrecken - Restwasser-Datenbank 2025.zip
    Size: 1604321 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/58c9e08b-4163-4a0f-a90f-babca8444ae9/resource/594bbc94-7b31-43d0-b11e-aded84961656/download/restwasserstrecken-restwasser-datenbank-2025.zip
    Description: Shapefile mit Restwasserstrecken - ein Produkt das in der Erarbeitung der Restwasser-Datenbank mitentstanden ist.

en:
Resource Title: Hydrologische Abflusszeitreihen - Restwasser-Datenbank 2025.xlsx
    Size: 12247368 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/58c9e08b-4163-4a0f-a90f-babca8444ae9/resource/82bdfb38-0d6b-4b32-8538-83d6820730de/download/hydrologische-abflusszeitreihen-restwasser-datenbank-2025.xlsx
    Description: Hydrologische Abflusszeitreihen von 1991-2020 für Anwendungen mit der Restwasser-Datenbank

en:
Resource Title: Einzugsgebiete - Restwasser-Datenbank 2025.zip
    Size: 6280235 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/58c9e08b-4163-4a0f-a90f-babca8444ae9/resource/96aafd27-de12-4e91-818a-972d8e4e465d/download/einzugsgebiete-restwasser-datenbank-2025.zip
    Description:

en:
Resource Title: Restwasserdatenbank_2025.xlsx
    Size: 169021 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/58c9e08b-4163-4a0f-a90f-babca8444ae9/resource/592a4142-73f8-4b06-8683-6bc93368a015/download/restwasserdatenbank_2025.xlsx
    Description: Restwasser-Datenbank mit rechtlichen, hydrologischen und technischen Attributen

dotierwasser

display_name: dotierwasser

konzessionen

display_name: konzessionen

restwasser

display_name: restwasser

wasserkraft

display_name: wasserkraft

wasserwirtschaft

display_name: wasserwirtschaft


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

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

envi

en: Environment

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: Source code for a preferential snowfall deposition model

en: preferential-deposition snowfall mountains statistical-model downscaling

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

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

envidat

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

en:
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:
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.


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: Blue-Green Biodiversity Beavers affecting Ecosystems

en: bats metadata freshwater beaver soil deadwood

en: EnviDat: the Environmental Data Portal

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 data set includes 57 years of hydrometeorological measurements from small (first-order) catchments in the pre-alpine valley Alptal. Here we provide daily mean values; values in hourly or 10-min resolution can be provided on demand.
Runoff has been measured at the outlet of three small (first-order) catchments of approximately 1 km2 area: Erlenbach (two independent runoff measurements), Vogelbach and Lümpenenbach. The catchments are similar with regard to geology (Flysch) and soil conditions (clay soils), but differ in forest coverage (20 to 60%). A detailed description of the catchments can be found at https://www.wsl.ch/alptal  .
Runoff in these small catchments is typically very dynamic and can temporally carry large amounts of sediment and large wood. Thus, the accuracy of the measurements at very large flow is limited.
Meteorological variables have been measured on a meadow (Erlenhöhe) located in the Erlenbach catchment at 1220 m a.s.l. using a standard meteorological station (incl. ventilated air temperature and heated rain gauges). In addition, precipitation has also been recorded at two other locations (in the Vogelbach and Lümpenenbach catchments).
Snow measurements have been conducted weekly to monthly since 1968 at more than 15 locations (30-m transects) representing different altitudes, aspects and land uses (meadow, forest). In addition, snow depth has been recorded continuously since 2003 at Erlenhöhe, and for this location we also include a simulation of snow depth and SWE (using the numerical models COUP and DeltaSnow) that assimilates the manual weekly snow-course measurements. Details on these snow measurements can be found in Stähli, M. and Gustafsson, D. 2006. Hydrol. Proc., 20, 411-428. doi: 10.1002/hyp.6058.
Further information on the methods and sensors can be found at https://www.wsl.ch/alptal  .
A first version of this data set (for the period 1968-2017) was uploaded in June 2018 at the occasion of the 50-year anniversary. This original data set was updated in February 2021 (with data from 2018 and 2019), and this data set was used for a longterm trend analysis, submitted for publication in a special issue of Hydrological Processes. Further updates of the data set (with data from 2020 to 2022) were uploaded in March 2023, February 2024, March 2025 and January 2026.

en: Longterm hydrological observatory Alptal (central Switzerland)

en: subalpine first-order-catchment runoff meteorological-data snow alptal

en:
Resource Title: Meteorological data Alptal 1968-2025 EnviDat updated
    Size: 2527068 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/4de5d4cd-f265-48b9-98a1-290d6f78fb23/resource/92245b92-5f2b-48a0-920d-4406cddc7d15/download/meteorological-data_alptal_1968-2025_envidat_updated.xlsx
    Description: Air temperature, dew point temperature, relative humidity, wind speed, precipitation and global radiation have been measured (since 1981) on a meadow (Erlenhöhe) in the Erlenbach catchment. It is located at 1'220 m a.s.l. with an exposure towards west. In order to assess the spatial variability of precipitation, two additional rain gauges have been running in the Vogelbach and Lümpenenbach catchment, respectively. For the period before 1981, meteorological variables have been extrapolated from the nearby MeteoSwiss station Einsiedeln (ca. 15 km north) to the WSL station Erlenhöhe using a linear correction factor for air temperature, wind speed and precipitation. Snow depth has been recorded automatically since 2003. Snow water equivalent has been calculated using two numerical models: COUP model (Jansson, 2012) and DeltaSnow model (Winkler et al., 2021)

Jansson, P.-E. (2012). COUP model: model use, calibration, and validation.Transactions of the ASABE, 55(4), 1335–1344. Winkler, M., et al. (2021): Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model, Hydrol. Earth Syst. Sci., 25, 1165-1187, 10.5194/hess-25-1165-861 2021.

en:
Resource Title: Runoff daily mean Alptal 1968-2025 EnviDat updated
    Size: 2474639 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/4de5d4cd-f265-48b9-98a1-290d6f78fb23/resource/bb1f67fd-4bd6-46fe-8b0a-e2ba2c076842/download/runoff_daily-mean_alptal_1968-2025_envidat_updated.xlsx
    Description: Runoff, water temperature and electrical conductivity have been measured since 1968 at the outlet of three torrential catchments (with an area of approximately 1 km2 each) located in the Alptal valley: Erlenbach, Lümpenenbach and Vogelbach. This data set provides daily mean values; data at 10-min resolution can be provided on request for the period 1984-2025 (contact: alptal@wsl.ch).

en:
Resource Title: Daily meteorological and snow data (1968-2022) Alptal
    Size: 2296381 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/4de5d4cd-f265-48b9-98a1-290d6f78fb23/resource/1207f33e-6efd-467f-8bf0-9e3303d529a7/download/meteorological-data_alptal_1968-2022_envidat_updated.xlsx
    Description: Air temperature, dew point temperature, relative humidity, wind speed, precipitation and global radiation have been measured (since 1981) on a meadow (Erlenhöhe) in the Erlenbach catchment. It is located at 1'220 m a.s.l. with an exposure towards west. In order to assess the spatial variability of precipiation, two additional rain gauges have been running in the Vogelbach and Lümpenenbach catchment, respectively.
For the period before 1981, meteorological variables have been extrapolated from the nearby MeteoSwiss station Einsiedeln (ca. 15 km north) to the WSL station Erlenhöhe using a linear correction factor for air temperature, wind speed and precipitation.
Snow depth has been recorded automatically since 2003. Snow water equivalent has been calculated using two numerical models: COUP model (Jansson, 2012) and DeltaSnow model (Winkler et al., 2021)

Jansson, P.-E. (2012). COUP model: model use, calibration, and validation.Transactions of the ASABE, 55(4), 1335–1344.
Winkler, M., et al. (2021): Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model, Hydrol. Earth Syst. Sci., 25, 1165-1187, 10.5194/hess-25-1165-861 2021.

en:
Resource Title: Snow measurements Alptal 1969-2025 EnviDat updated
    Size: 1059062 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/4de5d4cd-f265-48b9-98a1-290d6f78fb23/resource/81eb8552-6178-4071-97f5-905416ed9972/download/snow-measurements-alptal-1969-2025_envidat_updated.xlsx
    Description: Snow depth, snow density and snow water equivalent have been measured since winter 1968/69 along more than 15 transects distributed across the Alptal valley representing different altitudes, aspects and land use (closed forest and open meadows). Length of the snow courses: 20 to 30 m. The measurement interval has been weekly to monthly. More details about the locations and methods can be found in Stähli, M.; Gustafsson, D., (2006). Long-term investigations of the snow cover in a subalpine semi-forested catchment. Hydrological Processes, 20, 411-428. doi: 10.1002/hyp.6058.

en:
Resource Title: Manual snow measurements Alptal (1968-2022)
    Size: 1040241 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/4de5d4cd-f265-48b9-98a1-290d6f78fb23/resource/3fd1e126-25cc-490b-bb90-5fca04dc4ee9/download/snow-measurements-alptal-1969-2022_envidat_updated.xlsx
    Description: Snow depth, snow density and snow water equivalent have been measured since winter 1968/69 along more than 15 transects distributed across the Alptal valley representing different altitudes, aspects and land use (closed forest and open meadows). Length of the snow courses: 20 to 30 m. The measurement interval has been weekly to monthly.
More details about the locations and methods can be found in Stähli, M.; Gustafsson, D., (2006). Long-term investigations of the snow cover in a subalpine semi-forested catchment. Hydrological Processes, 20, 411-428. doi: 10.1002/hyp.6058.

en:
Resource Title: Daily runoff data from three steep torrents in the Alptal (1968-2022)
    Size: 2380268 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/4de5d4cd-f265-48b9-98a1-290d6f78fb23/resource/aae85e10-27fc-48bd-9f4d-f30a65f06b17/download/runoff_daily-mean_alptal_1968-2022_envidat_updated.xlsx
    Description: Runoff, water temperature and electrical conductivity have been measured since 1968 at the outlet of three torrential catchments (with an area of approximately 1 km2 each) located in the Alptal valley: Erlenbach, Lümpenenbach and Vogelbach. This data set provides daily mean values; data at 10-min resolution can be provided on request for the period 1984-2022 (contact: alptal@wsl.ch).

alptal

display_name: alptal

first-order-catchment

display_name: first-order-catchment

meteorological-data

display_name: meteorological-data

runoff

display_name: runoff

snow

display_name: snow

subalpine

display_name: subalpine


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

envi

en: Environment

en:
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

en:
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.

en:
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

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:
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: Seasonal fractional snow-covered area algorithm

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

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.

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

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

envidat

en: EnviDat: the Environmental Data Portal

en:
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).

en:
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

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:
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: Literature based approach to estimate future snow

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

alps

display_name: alps

climate-change

display_name: climate-change

future

display_name: future

snow-climatology

display_name: snow-climatology

snow-depth

display_name: snow-depth


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

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

envi

en: Environment

en:
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).

en:
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

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:
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 air temperatures in subalpine forests during three snow seasons

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

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