maintainer: Andrin Caviezel

en: rockfall natural-hazards rockfall-experiments sensor-stream rockfall-runout stonenodedata dem deposition-points stonenode induced-rockfall

en: StoneNode v1.0 Data for equipped experimental Runs.

en: Corresponding StoneNode v1.0 data streams belonging to the deposition points.

en: Induced Rockfall Data, Tschamut, GR

en:
 Induced Rockfall Experimental Data, Tschamut, GR, Switzerland

The archive contains site specific geographical data as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally availabe are the StoneNode v1.0 sensor streams for the equipped rocks.

The _OverviewFigures_ file serves as quick representation of the included data. It also contains the overview table for the rock masses in order to set the dimensions accordingly in RAMMS::ROCKFALL.

The archive contains the following folders:
<\ul>
* DEM: ArcGIS compatible DEM Tschamut_DEM_2m (grid resolution of 2 m) as well as the .asc grid used in RAMMS::ROCKFALL simulations
* _DepositionPoints_: containing two folders _RF Tschamut Frozen_ and _RF Tschamut WET_ containing each a shapefile with the complete deposition data of each series as well as deposition shapfiles for each rock shape.
* _DigitizedRocks_: Digitized point clouds of the used rocks as used for RAMMS::ROCKFALL simulations (.pts files) (dimensions have to be checked).
* _Orthofoto_: Orthophoto of the experimental site obtained via UAV with 5 cm resolution.

en:
 Dataset of an experimental campaign of induced rockfall in Tschamut, Grisons, Switzerland.

The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally available are all the StoneNode data streams for rocks equipped with a sensor. The data set consists of

* Deposition points from two series (wet (27/10/2016) and frozen (08/12/2016) ground)
* Digital Elevation Model (grid resolution 2 m) obtained via UAV
* Orthophoto (5 cm resolution) obtained via UAV
* Digitized rock point clouds (.pts input files for RAMMS::ROCKFALL)
* StoneNode v1.0 raw data stream for equipped rocks.

Further information is found in

* A. Caviezel et al., _Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments_, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/
* P. Niklaus et al., _StoneNode: A low-power sensor device for induced rockfall experiments_, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/

dem

display_name: dem

deposition-points

display_name: deposition-points

induced-rockfall

display_name: induced-rockfall

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

sensor-stream

display_name: sensor-stream

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata

en: Induced Rockfall Dataset (Small Rock Experimental Campaign), Tschamut, Grisons, Switzerland


maintainer: Nander Wever

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

state: active

type: dataset

en: Version 3

en:
Deprecated version. Release notes:

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

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

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

format: SERVICE

en: Undercatch correction

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Version 5

en:
Deprecated version. Release notes:

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

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 1

en:
Deprecated version. Release notes:

First release of dataset.

en: All versions

en: SNOWPACK ini file (new syntax)

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

en: SNOWPACK sno file

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

en: Version 7 (current)

en:
Release notes:

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

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

en: Version 4

en:
Deprecated version. Release notes:

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 6

en:
Deprecated version.

Release notes:

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

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

en: Version 2

en:
Deprecated version. Release notes:

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

en: SNOWPACK ini file (old syntax)

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

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

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

air-temperature

display_name: air-temperature

humidity

display_name: humidity

longwave-radiation

display_name: longwave-radiation

precipitation-amount

display_name: precipitation-amount

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

snow-ice-temperature

display_name: snow-ice-temperature

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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.

political_level: other

wsl

capacity: public

type: organization

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


maintainer: EnviDat Support

en: aerosol ccn hygroscopicity hac2 cloud-droplets

format: XLS

en: This repository contains the datasets that were used for used for the paper:: Drivers of Droplet Formation in East Mediterranean Orographic Clouds, EGUsphere, https://doi.org/10.5194/egusphere-2024-490, 2024.

aerosol

display_name: aerosol

ccn

display_name: ccn

cloud-droplets

display_name: cloud-droplets

hygroscopicity

display_name: hygroscopicity

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Dataset for the publication Drivers of Droplet Formation in East Mediterranean Orographic Clouds


alan-spongy-moth-laboratory-experiment

title_for_slug: alan-spongy-moth-laboratory-experiment

en: light-pollution lab-experiment insect-development spongy-moth alan

en: R code for the Cox hazard analysis and producing the Kaplan Meier survival curves for both Mortality and Pupation.

format: OCTET

en: R code for the pupation analysis and producing the graphics.

en: R code for the bodymass analysis and producing the graphics.

media_type: text/csv

en: Raw data used for the Pupation and Cox Hazard/Kaplan Meier Analysis.

format: CSV

en: Raw data modified and used for the Bodymass Analysis

en:
 Data and Code for ALAN Spongy Moth lab experiment
In this repository you will find all the raw datasets used for the data analysis performed in our paper about the impacts of artificial light at night (ALAN) on the development of Spongy Moth caterpillars. The files are attached as a csv file.
Also attached is all the R-code later used for the statistical analysis and for generating the graphics displayed in the paper. This files are attached as an R.file.

alan

display_name: alan

insect-development

display_name: insect-development

lab-experiment

display_name: lab-experiment

light-pollution

display_name: light-pollution

spongy-moth

display_name: spongy-moth

en: ALAN Spongy Moth Laboratory Experiment


weather-station-klosters

maintainer: Maxime Hervo

title_for_slug: weather-station-klosters

en: weather temperature raclets klosters wind precipitation

en: Weather Station Klosters

media_type: application/zip

en: All in one weather station measuring temperature (°C), humidity (%), wind (m s‾ ¹) and precipitation type. The time format is: number of days from January 0, 0000, UTC.

format: ZIP

mimetype: application/zip

en: A weather station (Lufft WS600) measured meteorological parameters at Klosters (LON: 9.880413, LAT: 46.869019). Detailed information on the specifications can be found [here](https://www.lufft.com/products/compact-weather-sensors-293/ws600-umb-smart-weather-sensor-1832/productAction/outputAsPdf/).

klosters

display_name: klosters

precipitation

display_name: precipitation

raclets

display_name: raclets

temperature

display_name: temperature

weather

display_name: weather

wind

display_name: wind


en: terrestrial-photogrammetry unmanned-aerial-vehicle-laser-scanning structure-from-motion terrestrial-laser-scanning mobile-laser-scanning forest-inventory 3d-point-cloud lidar

en:
Close Range Remote Sensing Benchmark for different LiDAR and photogrammetric Sensors in a mixed temperate forest.
Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices.

forest-inventory

display_name: forest-inventory

lidar

display_name: lidar

mobile-laser-scanning

display_name: mobile-laser-scanning

structure-from-motion

display_name: structure-from-motion

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

terrestrial-photogrammetry

display_name: terrestrial-photogrammetry

unmanned-aerial-vehicle-laser-scanning

display_name: unmanned-aerial-vehicle-laser-scanning

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Ramerenwald Close Range Remote Sensing Benchmark


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

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

en: 16S_Han et al_EUgradient.xlsx

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

en: ITS_Han et al_EUgradient.xlsx

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

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

fungi

display_name: fungi

latitudinal-gradient

display_name: latitudinal-gradient

microbial-biodiversity

display_name: microbial-biodiversity

prokaryotes

display_name: prokaryotes

soil-microbiome

display_name: soil-microbiome

soil-mulitfunctionality

display_name: soil-mulitfunctionality

en: Soil microbial diversity and multifunctionality across European biomes


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

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

en: Texture data of the soil locations

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

en: Daily averaged values used for the direct fitting

media_type: text/plain

en: Detailed information of each file in the dataset

format: TXT

en: Hydrus files used for the inverse fitting

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

alpine

display_name: alpine

glide-snow-avalanche

display_name: glide-snow-avalanche

lwc

display_name: lwc

matric-potential

display_name: matric-potential

measurements

display_name: measurements

soil

display_name: soil

soil-classification

display_name: soil-classification

soil-texture

display_name: soil-texture

en: Soil measurements of Seewer Berg and Davos


maintainer: Michael Eisenring

en: fagus-sylvatica drought-legacy phytochemistry herbivory climate-change

en:
What are the research data files about:
Raw data on various beech (Fagus sylvatica) leaf traits. Beech leaf chemistry (primary and specialized metabolites), leaf damage measurements from various herbivore feeding guilds and crown health assessment

Which methods were used:
Field surveys on trees that showed either prematured defoliation due to drought-stress or showed not pre-mature defoliation. .

When and where was the data collected/analyzed:
Field data was collected in 2019, 2020
Data was analyzed in 2021-2023

climate-change

display_name: climate-change

drought-legacy

display_name: drought-legacy

fagus-sylvatica

display_name: fagus-sylvatica

herbivory

display_name: herbivory

phytochemistry

display_name: phytochemistry

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Legacy effects of premature defoliation in response to an extreme drought event modulate phytochemical profiles with subtle consequences for leaf herbivory in European beech


en: microct specific-surface-area intercomparison snow icecube

en: SMP measurement files for snow types A, B, C and D.

en: Statistics of samples

en: IceCube and MicroCT data

en: The file contains all micro-CT and IC measurements for snow types A, B, C, D and E used in our study. All samples were processed (and stored) in a cold laboratory at -15°C.

en: Grain survey of snow particles

en: The file contains all measurements of the artificial particles for snow types A, B, C, D and E, including the artificial particle mass in the Petri dish.

en: TARTES simulations

en: Jupyter notebook with TARTES simulations.

en: artificial_snow_data set

en: The file contains all micro-CT and IC measurements of the artificial snow data set. The data set was originally produced to investigate the influence of black carbon on SSA measurements.

en: Influence of black carbon on the measurement of the specific surface of snow with the near infrared method - Philipp Baumann

en: These experiments investigate different possible errors by measuring the specific surface area (SSA) of snow with the near infrared method. The influence of black carbon on this measurement was disproved, the theory was therefore validated. It shows that sunlight has no influence on the measurement with the IceCube. Wind drift snow as thin layer on the sample can increase the measured value of the specific surface by a few percent. This could be part of the error, observed in former measurements at SLF. As reference for the measured SSA values with the near infrared method, computer tomography was used to verify the SSA values.

en: The file contains all data for the plot in figure 1 of the co-located density and SSA measurement at Summit, Greenland in May 2015, conducted by Schneebeli, Proksch, Schmid.

en: The specific surface area (SSA) of different snow types were measured with the IceCube instrument and the Scanco Medical microCT 40. In addition, the snow particles created during the preparation of IceCube samples were counted. The difference in SSA between these instruments is explained by the formation of the surface particles. A numerical simulation using TARTES simulation support the observations.

icecube

display_name: icecube

intercomparison

display_name: intercomparison

microct

display_name: microct

snow

display_name: snow

specific-surface-area

display_name: specific-surface-area


maintainer: Martin Schneebeli

en: 3d-print depth-hoar weak-layer 3d-model new-snow snow rounded-snow snow-cast

en: Snow models in STL-format

en: The zipped file contains all data described in the file 3d_snow_models_metadata. The stl-files should be ready for additive manufacturing.

en: Description of snowtypes, filenames and other metadata

en: This file contains information about the stl-files of the different snow models. Snowtype (Schneeart), International Snow Classification, preview picture, notes about the origin of the snow, filename of the stl-data, and physical size of the model are given.

en: The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).

depth-hoar

display_name: depth-hoar

new-snow

display_name: new-snow

rounded-snow

display_name: rounded-snow

snow-cast

display_name: snow-cast

weak-layer

display_name: weak-layer

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


maintainer: Joan Casanelles Abella

en: osmia-bicornis poznan hylaeus-communis plant-pollinator-interactions trap-nest dna-metabarcoding cavity-nesting-bees paris tartu urban-ecology antwerp zurich osmia-cornuta chelostoma-florisomne

en: Explanation data variables

en: Larval food composition

en: Information on the study sites

en: For each site, IDs, city, coordinates, structure where trapnest was attahced and height of the trapnest is provided

en: Urbanization poses threats and opportunities for the biodiversity of wild bees. A main gap relates to the food preferences of wild bees in urban ecosystems, which usually harbour large numbers of plant species, particularly at the larval stage. This data sets describes the larval food of four wild bee species (i.e. Chelostoma florisomne, Hylaeus communis, Osmica bicornis and Osmia cornuta) and three genera (i.e. Chelostoma sp., Hylaeus sp, and Osmia sp.) common in urban areas in five different European cities (i.e. Antwerp, Paris, Poznan, Tartu and Zurich). This data results from a European-level study aimed at understanding the effects of urbanization on biodiversity across different cities and citiscapes, and a Swiss project aimed at understanding the effects of urban ecosystems in wild bee feeding behaviour. Wild bees were sampled using standardized trap-nests in 80 sites (32 in Zurich and 12 in each of the remaining cities), selected following a double gradient of available habitat at local and landscape scales. Larval pollen was obtained from the bee nests and identified using DNA metabarconding. The data provides the plant composition at the species or genus level of the different bee nests of the studied species in the studied sites of the five European cities. For Hylaeus communis, this is the first study in reporting larval food composition.

antwerp

display_name: antwerp

cavity-nesting-bees

display_name: cavity-nesting-bees

chelostoma-florisomne

display_name: chelostoma-florisomne

dna-metabarcoding

display_name: dna-metabarcoding

hylaeus-communis

display_name: hylaeus-communis

osmia-bicornis

display_name: osmia-bicornis

osmia-cornuta

display_name: osmia-cornuta

paris

display_name: paris

plant-pollinator-interactions

display_name: plant-pollinator-interactions

poznan

display_name: poznan

tartu

display_name: tartu

trap-nest

display_name: trap-nest

urban-ecology

display_name: urban-ecology

zurich

display_name: zurich

en: Larval food composition of four wild bee species in five European cities


root-traits

maintainer: Ivano Brunner

title_for_slug: root-traits

en: soil-coring drought ingrowth-cores belowground-biomass fine-root-traits

en: Root-traits

en:
Fine-root traits of Scots pine in response to enhanced soil water availability deriving from long-term irrigation in the Pfynwald

Data_Fig.1.xlsx
Fine-root biomass of the topsoil (0-10 cm) in the dry and irrigated treatment of the Scots pine forest of the years 2003 to 2016 recorded by soil coring

Data_Tab1+2_2005.xlsx
Fine-root traits from roots of ingrowth cores from 2005 after two years of growth in the dry and irrigated treatment of the Scots pine forest

Data_Tab1+2_2016.xlsx
Fine-root traits from roots of ingrowth cores from 2016 after two years of growth, and from roots of the soil-coring sampling from 2016 in the dry and irrigated treatment of the Scots pine forest

belowground-biomass

display_name: belowground-biomass

drought

display_name: drought

fine-root-traits

display_name: fine-root-traits

ingrowth-cores

display_name: ingrowth-cores

soil-coring

display_name: soil-coring


maintainer: Marco Moretti

en: pollination sustainability urban-biodiversity honeybees urbanization remote-sensing

en: Dataset 3

en: Dataset on the the number of beehives and apiaries on the years 2012, 2013, 2014, 2015, 2016, 2017, 2018 in several Swiss cities.

en: Dataset 1

en: Dataset 1: Processed data for urban beekeeping 2012 & 2018

en: Dataset 2

en: Dataset 2: Processed data for modelling balance urban greenspace

en: Not used:

en: Not used for the analyses and not link to the paper.

en: Data on: (1) (Dataset 1) spatial distribution of urban beekeeping (number of hives and number of beekeeping locations) in 14 Swiss cities (Geneva, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) for the period 2012-2018; (2) (Dataset 2) aggregated data to model the sustainability of urban beekeeping.

honeybees

display_name: honeybees

pollination

display_name: pollination

remote-sensing

display_name: remote-sensing

sustainability

display_name: sustainability

urban-biodiversity

display_name: urban-biodiversity

urbanization

display_name: urbanization

en: Challenging the sustainability of urban beekeeping: evidence from Swiss cities


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

en: Low-flow storage

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

mimetype: text/plain

en: Precipitation

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

en: Discharge

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

en: Soil moisture

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

en: Shapefile of 307 catchments in Switzerland

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

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

discharge

display_name: discharge

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

precipitation

display_name: precipitation

prevah

display_name: prevah

soil-moisture

display_name: soil-moisture

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Franziska Koch

en: liquid-water-content snow-cover gps snow snow-depth

en: Read-me

en: The read-me file contains the description of the data provided.

en: Dataset with GPS-derived snow cover properties and validation data

en: This file includes the data as described in the Read-me file.

mimetype: text/csv

en:
This data set includes GPS-derived snow water equivalent (SWE), snow depth (HS) and liquid water content (LWC) data for three entire snow-covered seasons (2015-2016, 2016-2017, 2017-2018) at the study plot Weissfluhjoch 2540 m a.s.l. (Davos, Switzerland). The procedure to derive these snow properties is described in Koch et al. (2019). The novel approach is based on a combination of GPS signal attenuation and time delay.

The dataset also includes corresponding validation data for SWE and HS measured at Weissfluhjoch, and some additional meteorological data used for interpretation of the snow cover evolution.

Please refer to the Read-me file for further details on the data.

These data are the basis of the following publication:
> Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M., Schweizer, J., and Mauser, W., 2019. Retrieval of snow water equivalent, liquid water content and snow height of dry and wet snow by combining GPS signal attenuation and time delay. Water Resources Research, 55(5), 4465-4487. https://doi.org/10.1029/2018WR024431

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

en: GPS-derived data of SWE, HS and LWC and corresponding validation data


maintainer: Daniela Brito Melo

en: mixed-grain-transport large-eddy-simulations saltation drifting-snow snow-cohesion

en: LES-LSM code

en: Source code used to perform the analysis presented at the research article "Modeling snow saltation: the effect of grain size and interparticle cohesion". The source code is also available at the WSL/SLF GitLab Repository (https://gitlabext.wsl.ch/atmospheric-models/les-lsm).

en: Data in Figures

en: Simulation results presented in Figures 1 to 11 of the research article "Modeling snow saltattion: the effect of grain size and interparticle cohesion" in csv format.

en:
This dataset includes the parallel application and the main results supporting the research article "Modeling snow saltation: the effect of grain size and interparticle cohesion" published at the Journal of Geophysical Research: Atmospheres.
The code is a flow solver based on the Large Eddy Simulation (LES) technique coupled with a Lagrangian Stochastic Model (LSM). The interaction of snow particles with the bed is modeled with statistical and physically-based models for aerodynamic entrainment, rebound and splash, following the works of Groot Zwaaftink et al. (2014), Comola and Lehning (2017) and Sharma et al. (2018). This algorithm was also used by Sigmund et al. (2021) to model snow sublimation.

drifting-snow

display_name: drifting-snow

large-eddy-simulations

display_name: large-eddy-simulations

mixed-grain-transport

display_name: mixed-grain-transport

saltation

display_name: saltation

snow-cohesion

display_name: snow-cohesion


mountain-permafrost-hydrology

maintainer: Jeannette Noetzli

title_for_slug: mountain-permafrost-hydrology

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

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

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

climate-change-impacts

display_name: climate-change-impacts

ice-rich-permafrost

display_name: ice-rich-permafrost

mass-movements

display_name: mass-movements

monitoring-techniques

display_name: monitoring-techniques

mountain-permafrost-hydrology

display_name: mountain-permafrost-hydrology

permafrost-degradation

display_name: permafrost-degradation

educ

en: Education, culture and sport

en: Mountain Permafrost Hydrology


maintainer: Armin Sigmund

en: lagrangian-analysis backward-trajectories stable-water-isotopes snow-sublimation isotopic-distillation evaporation antarctica

en: Python scripts for data visualization

en: Scripts for generating the figures of the related research article and generating the data files (csv) from the model output

en: Readme

en: Meta data

en: Data

en: Modeled data including the data shown in the figures of the related research article

en: Model code

en: Model Sublimation and Model Air Parcel (python scripts)

en:
 Summary

This data set contains Python programming code and modeled data discussed in a related research article. We developed a simple isotope model to study the drivers of the particularly depleted vapour isotopic composition measured on the ship of the Antarctic Circumnavigation Expedition close to the outlet of the Mertz glacier, East Antarctica, in the 6-day period from 27 January 2017 to 1 February 2017. The model considers the stable water isotopologues H2(16O), H2(18O), and HD(16O). It uses data from the ERA5 reanalysis product with a spatial resolution of 0.25° x 0.25° (Hersbach et al., 2018) and 10-day backward trajectories for the location of the ship, published by Thurnherr et al. (2020a). Our data set includes the model code, Python scripts for visualizing the results, and data produced by the model including the results shown in the figures of the related research article. Here, we summarize the most important model characteristics while further details can be found in the readme.txt file and the related research article including its supporting information.

 Main model characteristics

The modeling approach consists of two steps called *Model Sublimation* and *Model Air Parcel*. The former estimates the isotopic compositions of the snow and sublimation flux across the Antarctic Ice Sheet using an Eulerian frame of reference while the latter models the vapour isotopic composition and specific humidity along air parcel trajectories using a Lagrangian frame of reference. The isotope effects of most phase changes are represented by equilibrium fractionation. Only for ocean evaporation, kinetic fractionation is additionally taken into account (original Craig-Gordon formula). For snow sublimation, two assumptions are tested: *Run E* assumes that sublimation is associated with equilibrium fractionation while *Run N* assumes that sublimation occurs without isotopic fractionation.

 Model Sublimation
Model Sublimation uses a simple one-dimensional mass-balance approach in each grid cell, considering snow accumulation due to snowfall and vapour deposition and snow ablation due to sublimation. The snowpack is represented by 100 layers of equal thickness (e.g., 1 cm) and density (350 kg m-3). The isotopic composition of snowfall is parameterized by generalizing a site-specific, empirical relationship between the daily mean air temperature and snowfall isotopic composition. In the case of vapour deposition, Model Sublimation assumes equilibrium fractionation and estimates the isotopic composition of the atmospheric vapour as the average value for two idealized situations: (i) locally sourced vapour which has the same isotopic composition as the sublimation flux; (ii) non-locally sourced vapour in isotopic equilibrium with snowfall. Model Sublimation is run with a time step of 1 h, independently of Model Air Parcel.

 Model Air Parcel
Every hour, an ensemble of trajectories arrives at different heights in the ABL above the ship. For each of these trajectories, we consider an air parcel with a constant volume of 1 x 1 x 1 m3. The air parcels are initialized at the first suitable time when the trajectories are located in the ABL, either over the ice-free ocean in conditions of evaporation or over snow (Antarctic Ice Sheet or sea ice). Subsequently, the masses of the water isotopologues in the air parcels are simulated with a time step of 3 h, considering vapour uptake or removal due to the moisture flux at the snow or liquid ocean surface (only if the parcel is in the ABL) and cloud/precipitation formation (if the saturation specific humidity is reached). Sea ice is taken into account in a very simplified way. We represent the sea ice by grid cells with a sea-ice cover of more than 90% and assume the isotopic composition of the sublimation flux to be identical to that in the nearest grid cell of the Antarctic Ice Sheet. The isotopic composition of the sublimation flux is taken from Model Sublimation whereas the isotopic composition of the vapour deposition flux (over snow) and condensation flux (over ice-free ocean) is simulated assuming an isotopic equilibrium with the air parcel. Isotope effects of cloud/precipitation formation are represented using the classic Rayleigh distillation model with equilibrium fractionation, where the cloud water is assumed to precipitate immediately after formation.

 References

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J.,... others (2018). *ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. doi: 10.24381/cds.bd0915c6

Thurnherr, I., Wernli, H., & Aemisegger, F. (2020a). *10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017*. Zenodo. doi: 10.5281/zenodo.4031705

antarctica

display_name: antarctica

backward-trajectories

display_name: backward-trajectories

evaporation

display_name: evaporation

isotopic-distillation

display_name: isotopic-distillation

lagrangian-analysis

display_name: lagrangian-analysis

snow-sublimation

display_name: snow-sublimation

stable-water-isotopes

display_name: stable-water-isotopes

envidat

en: EnviDat: the Environmental Data Portal

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

en: Modeled Isotopic Composition of Water Vapour Along Air Parcel Trajectories in the Antarctic


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

media_type: image/tiff

format: TIFF

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.

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: Sentinel-2 Vegetation Height Model Armenia


en: diurnal-variability ice-nucleation dust-particles biological-particles fluorescent-particles aerosol-cloud-interactions

en: Figure 2.xlsx

en: This file contains data presented in Figure 2 in the main text.

en: Figure 6.xlsx

en: This file contains data presented in Figure 6 in the main text.

en: Figure 1.xlsx

en: This file contains data presented in Figure 1 in the main text.

en: Figure 3.xlsx

en: This file contains data presented in Figure 3 in the main text.

en: Figure 7.xlsx

en: This file contains data presented in Figure 7 in the main text.

en: Figure 4.xlsx

en: This file contains data presented in Figure 4 in the main text.

en: Data for figures in the Supplementary.xlsx

en: This file contains data presented in Figure S2 to S36 in the supplementary.

en: Figure 5.xlsx

en: This file contains data presented in Figure 5 in the main text.

en:
This repository contains all observational data sets during CALISHTO campaign used for the paper: Gao, K., Vogel, F., Foskinis, R., Vratolis, S., Gini, M. I., Granakis, K., Zografou, O., Fetfatzis, P., Berne, A., Papagiannis, A., Möhler, O., Eleftheridadis, K., and Nenes, A.: On the drivers of ice nucleating particle diurnal variability in Eastern Mediterranean clouds, npj Climate and Atmospheric Science, Preprint link: https://www.researchsquare.com/article/rs-4378562/v1.
CALISHTO campaign was conducted between 12 October and 27 November 2021 to observe ice nucleating particles and aerosol properties at the Helmos Hellenic Atmospheric Aerosol and Climate Change station in Eastern Mediterranean, to understand cloud-aerosol interactions. To evaluate the spatial-temporal variabilities and characteristics of ice nucleating particles, a high-time resolution ice nucleation spectrometer was employed and different online aerosol property measurements were conducted in-situ, including particle number concentration, particle size distribution, particle fluorescent properties, and particle chemical composition. In addition, planetary boundary layer conditions at the observation site were determined by remote sensing techniques employed at a lower site.

aerosol-cloud-interactions

display_name: aerosol-cloud-interactions

biological-particles

display_name: biological-particles

diurnal-variability

display_name: diurnal-variability

dust-particles

display_name: dust-particles

fluorescent-particles

display_name: fluorescent-particles

ice-nucleation

display_name: ice-nucleation

educ

en: Education, culture and sport

en: CALISHTO campaign dataset for the publication On the Drivers of Ice Nucleating Particle Diurnal Variability in Eastern Mediterranean Clouds


en: chilling phenology budburst hatch spongy-month

en: 3 R-Scripts used for the analyses of the temperature, phenology and performance/preference tests

en: Temperature data Spongy moth experiment

en:
4 files with hourly temperature recorded at the height of buds in each of the four temperature treatments:
- Winter6: greenhouse where temperature was kept >6°C over winter
- Winter10: greenhouse where temperature was kept >10°C over winter
- OTC_Ambient: Open Top Chamber at Ambient temperature
- OTC_Warm: Open Top Chamber at +3.5°C warmer temperature

en: Phenology and performance/preference tests Data

en:
-"Synchronex_Cuttings_Pheno.txt" provides the budburst date ('Stage 2') of each twig of the six studies species in the four different temperature treatments ("ambient", "a+5"=Ambient +5°C, 'Greenhouse-5'= Greenhouse at temperature >5°C, 'Greenhouse_10' = Greenhouse at temperature >10°C); 'Success' give the percentage of buds within the twig that succeeded to reach at least stage 2 during the experiment.

'Synchronex-Gypsy.txt' provides the number of eggs that have hatched at each monitoring date (Day of the year in columns); "DOY_hatch_1" corresponds to the first DOY when at least one egg has hatched; "Replicate_egg_mass" correspond to the egg mass replicate (1 to 5)

'PerformanceTest_GypsyMoth.txt' provides the biomass in mg of the caterpillars fed with fresh leaves of each tested tree species during one week. "Fresh_alive" is the biomass of only the living caterpillars after 1 week and "Freshpermoth" corresponds the weight per living caterpillar (Fresh alive/Nalive)

'PreferenceTest_All_species.txt' provides the total surface of leaf disc of each tested tree species remaining after exposing 5 caterpillars to these leaves during 24H. "Total_area" is the surface of the disc of each species measured after 24h exposed to the spongy moth first instars. For each species 'control' means the surface of the disc after 24H without any exposure to the larvae. 'EatenPercent correspond to the percentage surface loss compared to the control.

en:
The files correspond to the data and R-script used for the analyses of the following paper "Feasting on the ordinary or starving for the exceptional: phenological synchrony between spongy moth and budburst of European trees in a warmer climate" published in Ecology and Evolution by Vitasse et al. 2023.
There are three zip files corresponding to the Temperature data, phenology/preference/performance tests and R-Scripts for the analyses.

Input data:
'Synchrony_Cuttings_Pheno.txt':

budburst

display_name: budburst

chilling

display_name: chilling

hatch

display_name: hatch

phenology

display_name: phenology

spongy-month

display_name: spongy-month

envidat

en: EnviDat: the Environmental Data Portal

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

en: Synchrony between spongy moth hatching and leaf phenology of temperate trees


en: tree-species remote-sensing time-series switzerland sentinel-2

en: Tree species profiles for 2020, CCI index

en: Tree species profiles for 2019, Green band

en: Tree species profiles for 2020, SWIR-1 band

en: Tree species profiles for 2019, Red-Edge-1 band

en: Tree species profiles for 2020, CIre index

en: Tree species profiles for 2019, NDMI/NDWI index

en: Tree species profiles for 2019, Red-Edge-2 band

en: Tree species profiles for 2020, Red-Edge-3 band

en: Tree species profiles for 2019, NIR band

en: Tree species profiles for 2019, SWIR-2 band

en: Tree species profiles for 2019, SWIR-1 band

en: Tree species profiles for 2020, EVI index

en: Tree species profiles for 2019, EVI index

en: Tree species profiles for 2020, NDMI index

en: Tree species profiles for 2019, Red band

en: Tree species profiles for 2020, NDVI index

en: Tree species profiles for 2019, Red-Edge-3 band

en: Tree species profiles for 2020, Red band

en: Tree species profiles for 2020, Green band

en: Tree species profiles for 2019, CIre Index

en: Tree species profiles for 2020, Blue band

en: Tree species profiles for 2019, CCI index

en: Tree species profiles for 2019, NDVI index

en: Tree species profiles for 2019, Blue band

en: Tree species profiles for 2020, Red-Edge-2 band

en: Tree species profiles for 2020, NIR band

en: Tree species profiles for 2020, SWIR-2 band

en: Tree species profiles for 2020, Red-Edge-1 band

en:
 Tree species profiles from Sentinel-2 time series
We obtained tree species-specific Sentinel-2 time series for the years 2019 and 2020. The Sentinel-2 time series originated from [here](https://www.envidat.ch/#/metadata/sentinel-2-time-series-of-switzerland), and the tree species information was derived from pure plots of the Swiss National Forest Inventory, spread all over Switzerland.
In addition to the 10 and 20 m bands from Sentinel-2 (Blue, Green, Red, Red-Edge-1, Red-Edge-2, Red-Edge-3, NIR, SWIR-1, SWIR-2), we calculated indices (CCI, CIre, EVI, NDMI/NDWI, NDVI).
In total, tree species profiles of 763 pixels for 2020 and 562 pixels for 2019 are available for seven tree species (*Abies alba*, *Castanea sativa*, *Fagus sylvatica*, *Fraxinus excelsior*, *Larix* spp., *Picea abies*, *Pinus sylvestris*).

switzerland

display_name: switzerland

time-series

display_name: time-series

tree-species

display_name: tree-species


maintainer: Dirk Nikolaus Karger

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

en: Reptiles

en: Species distribution models based on different climate predictor groups

en: Amphibians

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

en: Mammals

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

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

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

amphibians

display_name: amphibians

chelsa

display_name: chelsa

climate-variablity

display_name: climate-variablity

generalized-additive-models

display_name: generalized-additive-models

generalized-linear-models

display_name: generalized-linear-models

interannual-climate-variability

display_name: interannual-climate-variability

iucn

display_name: iucn

mammals

display_name: mammals

niche-models

display_name: niche-models

random-forests

display_name: random-forests

range-maps

display_name: range-maps

reptiles

display_name: reptiles

sdm

display_name: sdm

species-distribution-models

display_name: species-distribution-models

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


en: poznan antwerp paris zurich angiospermae floral-resources tartu floral-inventories urban plants

en: Floral units, mean number of flowers per floral unit and floral abundance of the different plants counted in quadrats in the study sites in Zurich during the four sampling periods

en: List of plant species sampled in the five cities (Antwerp, Paris, Poznan, Tartu and Zurich) during the different sampling perionds (four for Zurich, three for the other cities) with their frequency (oc1-oc16) within a 100 m radius

en: Trait values for the sampled plants

en: Data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland) sampled between April and July 2018.

angiospermae

display_name: angiospermae

antwerp

display_name: antwerp

floral-inventories

display_name: floral-inventories

floral-resources

display_name: floral-resources

paris

display_name: paris

plants

display_name: plants

poznan

display_name: poznan

tartu

display_name: tartu

urban

display_name: urban

zurich

display_name: zurich

en: Flowering Plants (Angiospermae) in Urban Green Areas in five European Cities


en: urban urban-biodiversity insects birds biodiversity fungal-pathogen urban-ecology fungi

en: Data tables including individual bird data with biometry, prey species from faecal samples, and fungi species from swab samples.

en:
We captured birds in urban parks and peri-urban forests in the cities of Lugano and Zurich. The urban sites were located in an urban forest (Lugano) and in a cemetery (Zurich), and the bird assemblages were comparable with those of the surrounding forests.
We captured individual birds during sessions lasting multiple days using mist-nets, concentrating the activities within 3-4 hours after sunrise and before sunset. We extracted birds from nets using single-use nitrile gloves and gently wiped their body parts with a dry foam swab MW940 (Medical Wire & Equipment), which was then placed in a screw-capped tube (2 ml), which was previously filled with 1 ml ethanol puriss. (Sigma-Aldrich) under sterile conditions.Then, we marked each bird with an alphanumeric leg band, identified the species, determined the sex and age whenever possible, measured for relevant biometrics (e.g. wing chord), the amount of body fat and pectoral muscle (i.e. body condition), and weighed.
Afterwards, we placed each bird in a disposable paper bag within a cotton bag that ensured respiration and left it for few minutes in a safe and calm place to allow for defecation and the collection of faecal samples. The faecal sample was collected from insectivorous and omnivorous birds, and few granivorous birds that are known to occasionally eat invertebrates, such as the house sparrow Passer domesticus.
After releasing the bird, we stored the faecal sample in a screw-capped tube (2 ml), which was previously filled with 1 ml ethanol puriss. (Sigma-Aldrich) under sterile conditions. We wore single-use nitrile gloves and used sterile toothpicks to collect solid samples. The tubes were initially stored in the refrigerator during the field campaign and no later than seven days after collection at -20°C until prepared for DNA extraction.
DNA extraction and library preparation was performed at WSL Phytopathology facilites, Sequencing was performed at Lausanne Genomic Technologies Facility (University of Lausanne; [https://wp.unil.ch/gtf/](https://wp.unil.ch/gtf/)) on an AVITI benchtop sequencing system (Element Biosciences). Bioinformatics was performed at the Genetic Diversity Center (ETH Zurich) using a custom pipeline.

biodiversity

display_name: biodiversity

birds

display_name: birds

fungal-pathogen

display_name: fungal-pathogen

fungi

display_name: fungi

insects

display_name: insects

urban-biodiversity

display_name: urban-biodiversity

urban-ecology

display_name: urban-ecology

en: Urban bird capture data with prey species and fungal community


maintainer: Vanessa Burg

en: switzerland manure bioenergy review biogas

en: Biogas from animal manure in Switzerland: energy potential, technology development and resource mobilization

en: Aim of this white paper is to provide decision-makers, administrations and stakeholders with the most current research findings in order to promote the optimal use of bioenergy from manure in the Swiss energy transition. For this purpose, the results of the Swiss competence center for bioenergy research - SCCER BIOSWEET - are summarized and presented in a broader context. If nothing else is mentioned, the results refer to Switzerland and in case of the feedstock to the domestic biomass potentials.

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland


en: sentinel-1 surface-roughness satellite snow-melt wet-snow radar snow-temperature snow-lwc snow-density snow-roughness snow-profiles snow-ssa

en: Snow profiles collected on the snow season 2023-2024.

en: Snow water equivalent time series automatically recorded from the snow scale (2022-2024).

en: Manually measured time series of surface roughness (2023-2024).

en: Manually measured time series of snow water equivalent (2022-2023).

en: General metadata and detailed data description.

en: Manually measured time series of surface roughness (2022-2023).

en: Manually measured time series of snow water equivalent (2023-2024).

en: Snow profiles collected on the snow season 2022-2023.

en: Runoff time series automatically recorded from the lysimeter (2022-2024).

en:
*Currently, this dataset is intended solely for article review purposes and may not be used for any other purposes.*
This dataset consists of a series of full snow profiles collected at the field site of Weissfluhjoch, Davos, Switzerland over the two snow seasons of 2022-2023 and 2023-2024. This series of snow profiles was collected with a high vertical and temporal resolution, with a specific focus on the melting seasons, during which up to 3 snow profiles per week were sampled. This dataset is part of the measurement campaign carried out within the SnowTinel project, whose aim is to explore the Sentinel-1 SAR backscattering response to melting Alpine snowpacks. Therefore, this dataset contains manual measurements of the main properties of snow which are responsible for scattering, i.e. temperature, density, specific surface area (SSA), liquid water content (LWC), surface roughness and snow water equivalent (SWE).
The dataset is composed of a total of 85 snow profiles, 38 carried out in 2022-2023 and 47 in 2023-2024.
 - Profiles of snow **temperature** were sampled at a vertical resolution of 10 cm (2022-2023) and 5 cm (2023-2024) using HI98501 Checktemp from Hanna Instruments;
  - Profiles of snow **density** were sampled at a vertical resolution of 3 cm using a box density cutter and a digital scale;
  - Profiles of snow **specific surface area** were sampled at a vertical resolution of 4 cm using the InfraSnow sensor from FPGA;
  - Profiles of snow **liquid water content** were sampled at a vertical resolution of 2 cm using the Denothmeter (2022-2023) and the New Capacitive Sensor from FPGA (2023-2024). In conditions of ripe snow, dielectric measurements were backed up by melting calorimetry measurements following a recently revised field protocol;
  - Snow **surface roughness** is expressed by the Root Mean Square of the Heights (RMSH) and the correlation length (CL), both computed from a digitized snow transect obtained by a digital photograph of a panel vertically inserted into the snow.
- **Snow water equivalent** was sampled in sections from the surface to the bottom of the snowpack with a cylinder cutter.

Additionally, **runoff** was automatically measured in the close proximity to the measurement field at a sub-hourly resolution by a lysimeter. The instrument was unfortunately discovered to be clogged at the start of the runoff in 2023 and only repaired in late May 2023. Therefore, the time series is not explanatory for the runoff start in 2023. The instrument was inspected timely and assessed as fully functional for the following snow season. The manually measured values of **SWE** are complemented by an automatically recorded time series at sub-hourly time intervals with a SSG1000 snow scale manufactured by Sommer Messtechnik, Austria.

 ---

- References:
 - HI98501 Checktemp: [https://www.hannainstruments.co.uk/modules/teapotknowledgehub/uploads/ist98501_06_18-60d496ca2cd31.pdf](https://www.hannainstruments.co.uk/modules/teapotknowledgehub/uploads/ist98501_06_18-60d496ca2cd31.pdf)
 - InfraSnow sensor: Wolfsperger, Fabian & Ziegler, Silvio & Schneebeli, Martin & Löwe, Henning. (2022). Evaluation of the InfraSnow: a handheld device to measure snow specific surface (SSA). 10.13140/RG.2.2.31566.95047; [https://snow-sen.com/infrasnow-ssa-sensor-2/](https://snow-sen.com/infrasnow-ssa-sensor-2/)
 - Denothmeter: Denoth, A. “An Electronic Device for Long-Term Snow Wetness Recording.” Annals of Glaciology 19 (1994): 104–6. [https://doi.org/10.3189/S0260305500011058](https://doi.org/10.3189/S0260305500011058)
 - New Capacitive Sensor: Wolfsperger, Fabian & Geisser, Michel & Ziegler, Silvio & Löwe, Henning. (2023). A NEW HANDHELD CAPACITIVE SENSOR TO MEASURE SNOW DENSITY AND LIQUID WATER CONTENT; [https://snow-sen.com/slf-snowpro-40/](https://snow-sen.com/slf-snowpro-40/)
 - Melting calorimetry: Barella, R., Bavay, M., Carletti, F., Ciapponi, N., Premier, V., and Marin, C.: Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow, The Cryosphere, 18, 5323–5345, [https://doi.org/10.5194/tc-18-5323-2024](https://doi.org/10.5194/tc-18-5323-2024), 2024.
 - Surface roughness: B. Riccardo, C. Marin, M. Callegari, M. Gianinetto, T. Moranduzzo and C. Notarnicola, "A Low-Cost Portable Automatic System for Snow Surface Roughness Measurements Based on Digital Photography," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 5562-5565, doi: [https://doi.org/10.1109/IGARSS47720.2021.9553989](https://doi.org/10.1109/IGARSS47720.2021.9553989).
 - SSG: [https://www.sommer.at/de/produkte/schnee-eis/schneewaage-ssg-2](https://www.sommer.at/de/produkte/schnee-eis/schneewaage-ssg-2)

radar

display_name: radar

satellite

display_name: satellite

snow-density

display_name: snow-density

snow-lwc

display_name: snow-lwc

snow-melt

display_name: snow-melt

snow-profiles

display_name: snow-profiles

snow-roughness

display_name: snow-roughness

snow-ssa

display_name: snow-ssa

snow-temperature

display_name: snow-temperature

surface-roughness

display_name: surface-roughness

wet-snow

display_name: wet-snow

educ

en: Education, culture and sport

en: SnowTinel high-temporal-resolution ground truth dataset for SAR remote sensing of snow


maintainer: Esther Frei

en: afforestation long-term-experiment mountain-forest stillberg treeline

mimetype: application/x-zip-compressed

en:
 Background information

The Stillberg ecological treeline research site in the Swiss Alps was established in 1975, with the aim to develop ecologically, technically, and economically sustainable reforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Long-term monitoring of the large-scale high-elevation afforestation has generated data about tree growth, survival, and vitality. In addition, detailed characteristics of the microsite conditions of the research were conducted. Besides providing a scientific basis and practical guidelines for high-elevation afforestation, this research has contributed to a comprehensive understanding of ecological processes in the treeline ecotone.

 Experiment description

The Stillberg afforestation experiment was established in 1975 by planting 92,000 seedlings of *Larix decidua*, *Pinus cembra* and *Pinus mugo* ssp. *uncinata* in the alpine treeline ecotone. The afforestation site is located on a northeast-facing slope with steep, topographically highly structured terrain and covers elevations from 2075 to 2230 m a.s.l. The afforestation site was divided into 4052 square plots of 3.5 × 3.5 m, arranged in a regular species-alternating pattern over the whole area. Each plot contained 25 trees of one species (1350 plots per species), and the seedlings were systematically planted 70 cm apart. The trees have been monitored since 1975. Specifically, tree mortality was assessed annually from 1975 until 1995 and has been documented every ten years since then, with surveys in 2005 and 2015 (the next survey is due in 2025). Height of the surviving trees was measured in 1975, 1979, 1982, 1985, 1990, 1995, 2005, and 2015. In 1995, 2005, and 2015, drivers of tree vitality were assessed for a subset of trees per plot. Additionally, an extensive set of environmental parameters characterizing microsite conditions of the afforestation area were recorded before and after the planting of the trees.

 Data description

The five datasets from the afforestation experiment comprise ecological and environmental data from the main afforestation experiment in five datasets with accompanying metadata (Stillberg_afforestation_all_metadata.xlsx). All data and metadata files are bundled in a ZIP-file (Stillberg_afforestation_v1.zip). In particular, a first dataset contains environmental data characterising microsite conditions of the 4000 plots with regard to soil, topography, vegetation and microclimatic conditions (Stillberg_afforestation_plot_data_v1.csv; Stillberg_afforestation_plot_metadata_v1.csv. In each plot, the natural tree regeneration was assessed by counting seedings of several tree species in 2005 and 2015 (Stillberg_afforestation_regeneration_data_v1.csv; Stillberg_afforestation_regeneration_metadata_v1.csv). Furthermore, specific information about each of the 92’000 planted trees of the tree species is available (Stillberg_afforestation_tree_parameter_data_v1.csv; Stillberg_afforestation_tree_parameter_metadata_v1.csv). Survival data for each of the 92’000 individual trees can be found in a separate dataset (Stillberg_afforestation_tree_survival_data_v1.csv; Stillberg_afforestation_tree_survival_metadata_v1.csv). Tree growth and vitality parameters are available for all trees from 1995, and for subsets of trees for 2005 and 2015 (Stillberg_afforestation_tree_measurements_data_v1.csv; Stillberg_afforestation_tree_measurements_metadata_v1.csv).

afforestation

display_name: afforestation

long-term-experiment

display_name: long-term-experiment

mountain-forest

display_name: mountain-forest

stillberg

display_name: stillberg

treeline

display_name: treeline

envidat

en: EnviDat: the Environmental Data Portal

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

en: Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland


c-n-p-release-treeline

maintainer: Frank Hagedorn

title_for_slug: c-n-p-release-treeline

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

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

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

biogeochemical-cycling

display_name: biogeochemical-cycling

carbon-cycle

display_name: carbon-cycle

climate-change

display_name: climate-change

enzymatic-activities

display_name: enzymatic-activities

litter-decomposition

display_name: litter-decomposition

microbial-biomass

display_name: microbial-biomass

nutrient-fluxes

display_name: nutrient-fluxes

treeline-ecosystems

display_name: treeline-ecosystems

en: C-N-P-Release Treeline


automatic-detection-of-avalanches

maintainer: Matthias Heck

title_for_slug: automatic-detection-of-avalanches

en: monitoring hidden-markov-model avalanche-activity seismic snow

en: Results and Scripts

en: This zip package contains the classification results, coherence results, reference data set, python scripts used for the classification and the start and end times for the pre-processed continuous seismic data. A further readme file is also include for more detailed information.

en: This dataset contains the results obtained by an automatic classification using hidden Markov models of a continuous seismic dataset. To avoid long computational times, we reduced the seismic data using pre-processing step. The start and end times of the windows used for the classification are also included in this dataset. Furthermore, an avalanche reference data set is included and the python scripts used to perform the processing steps and the classification.

avalanche-activity

display_name: avalanche-activity

hidden-markov-model

display_name: hidden-markov-model

monitoring

display_name: monitoring

seismic

display_name: seismic

snow

display_name: snow

en: Automatic detection of avalanches


weather-station-davos-wolfgang

maintainer: Jörg Wieder

title_for_slug: weather-station-davos-wolfgang

en: pressure weather raclets temperature relative-humidity

en: Weather Station Davos Wolfgang

en: The data file contains date and time (YYYY-MM-DD hh:mm:ss) in UTC, temperature (°C), relative humidity (%), pressure (hPa) at 10 m above ground and above sea level. The data was averaged over 10 minutes.

en: The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577).

pressure

display_name: pressure

raclets

display_name: raclets

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

weather

display_name: weather


maintainer: EnviDat Support

en: python numerical-model pattern-formation snow vapor-transport

en:
Data and code v1.0 downloaded from the git.

Running the code requires an installation of FEniCS via docker.

en:
This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format.

The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication:

_Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical
instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_

The code and potential updates can be accessed directly through git via:

https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics

numerical-model

display_name: numerical-model

pattern-formation

display_name: pattern-formation

python

display_name: python

vapor-transport

display_name: vapor-transport

educ

en: Education, culture and sport

en: A numerical solver for heat and mass transport in snow based on FEniCS


tree-ring-laser-ablation-data

maintainer: Paula Ballikaya

title_for_slug: tree-ring-laser-ablation-data

en: tree-rings dendrochronology dendrochemistry air-pollution laser-ablation

en: Laser ablation data

en:
This dataset contains the values of several chemical elements (Mg, Al, Si, S, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Tl, Pb, Bi) measured in the latewood of tree rings of Mongolian oak from Harbin, China, at a 5-year resolution. Due to the lack of a suitable reference material for wood, absolute concentration was not calculated, and the ratio between the chemical element and 13C was taken as proxy for the element signal.

In Harbin, one of the largest cities and most important industrial centers in northeastern China, air quality monitoring systems were built only by the end of 2015 to meet the national requirements. Thus, dendrochemical analyses could be used as a tool to complement for the lack of air quality data over longer periods of time, allowing for the reconstruction of the temporal trend of trace metals. Our main scopes were to: (a) assess the chemical composition of Quercus mongolica Fisch. ex Ledeb. tree rings from Harbin using a recently developed system of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), (b) identify the main chemical elements which
derived from air pollution and may be used as indicators over the period 1965–2020 in Harbin, while excluding those that were controlled by physiological processes in the tree, and (c) reconstruct the history of pollution in Harbin by comparing the tree-ring chemical composition of recent decades with that of previous decades, in trees growing in the highly polluted city of Harbin and in trees growing in a control site 90 km away from major pollution sources.

Briefly, the temporal trend of some elements was influenced by physiological factors, by environmental factors such as pollution, or influenced by both. Mg, K, Zn, Cu, Ni, Pb, As, Sr and Tl showed changes in pollution levels over time.

air-pollution

display_name: air-pollution

dendrochemistry

display_name: dendrochemistry

dendrochronology

display_name: dendrochronology

laser-ablation

display_name: laser-ablation

tree-rings

display_name: tree-rings

envidat

en: EnviDat: the Environmental Data Portal

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

en: Tree-ring laser ablation data


maintainer: Francesco Comola

en: saltation sand snow cohesion sediment wind

en: Output of the model simulations

en: Model results on granular splash and saltation process of sand and snow particles

en:
The wind-driven saltation of sand and snow shapes dunes and ripples, generates dust emission, and erodes the surface of the Antarctic ice sheet. Here, we use a model based on the discrete element method to simulate grain-flow interactions and study the effect of particle cohesion on saltation dynamics.

The data contains the model output of granular splash simulations and saltation simulations. Granular splash, the main particle entrainment process in saltation, occurs upon impact of saltating particles with the granular bed. We performed Monte Carlo simulations of granular splash for loose sand grains and for cohesive ice grains. The analysis indicate that different values of cohesion have significant effects not on the number of splashed grains, on the ejection velocity, and the rebound velocity.

In our saltation simulations, we trigger particle movement with a single splash event at the inlet section section and let the system evolve until steady state. Our results show that saltation over cohesive surfaces is difficult to initiate but easy to sustain at low wind speed. The occurrence of transport thus depends on the history of the wind speed, a phenomenon known as hysteresis. We also show that saltation over cohesive surfaces presents higher mass fluxes but requires longer distances to saturate, which increases the size of the smallest stable surface ripples. Our model results have implications for large-scale aeolian processes on Earth and Titan, where sand grains are thought to be very cohesive.

cohesion

display_name: cohesion

saltation

display_name: saltation

sand

display_name: sand

sediment

display_name: sediment

wind

display_name: wind

en: Saltation of cohesive granular materials


maintainer: James W. Kirchner

en: time-series-analysis de-mixing deconvolution arma-noise irf transfer-function nonlinear-deconvolution irfnnhsr impulse-response-function system-identification greens-function

en: README file for IRFnnhs.R and benchmark demonstration scripts

en: Impulse Response Function script

en: Benchmark demonstration scripts and results

en:
The R script IRFnnhs.R, which efficiently estimates impulse response functions for environmental systems that are nonlinear, nonstationary, or heterogeneous, based on their input and output time series.

Scripts and results for a series of benchmark tests are also provided, to accompany Kirchner, J.W., Impulse response functions for heterogeneous, nonstationary, and nonlinear systems, estimated by deconvolution and demixing of noisy time series, _Sensors_, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022.

arma-noise

display_name: arma-noise

de-mixing

display_name: de-mixing

deconvolution

display_name: deconvolution

greens-function

display_name: greens-function

impulse-response-function

display_name: impulse-response-function

irf

display_name: irf

irfnnhsr

display_name: irfnnhsr

nonlinear-deconvolution

display_name: nonlinear-deconvolution

system-identification

display_name: system-identification

time-series-analysis

display_name: time-series-analysis

transfer-function

display_name: transfer-function

en: Impulse response functions for nonlinear nonstationary and heterogeneous systems


maintainer: Jana von Freyberg

en: plant-water-uptake endmember-mixing-models ecohydrology stable-water-isotopes root-zone review uncertainties

en: Data set of MEE-20-04-264

en: The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal ‘Methods in Ecology and Evolution’.

en: The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal 'Methods in Ecology and Evolution'.

ecohydrology

display_name: ecohydrology

endmember-mixing-models

display_name: endmember-mixing-models

plant-water-uptake

display_name: plant-water-uptake

review

display_name: review

root-zone

display_name: root-zone

stable-water-isotopes

display_name: stable-water-isotopes

uncertainties

display_name: uncertainties

educ

en: Education, culture and sport

en: Data set of: Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources


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

en: Data plot of the forest station (NEB)

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

en: Site aerial photo (Neunkirch)

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

mimetype: image/jpeg

en: Field station data (NEF)

en: Data file.

en: Site photo of the field station (NEF)

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

en: Site photo of the forest station (NEB)

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

en: Forest station data (NEB)

en: Data plot of the field station (NEF)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Neunkirch in Switzerland where one station is located within a natural deciduous forest stand (NEB) with European beech (_Fagus sylvatica_; 160 yrs), sycamore maple (_Acer pseudoplatanus_; 160 yrs) and lime trees (_Tilia sp._; 110 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NEF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Neunkirch is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Michael Lehning

en: snow-conservation snow snow-farming snowpack winter-tourism terrestrial-laser-scanning

en: HS Fluela spring 2015

en:
Snow depth/ height of snow heap in spring 2015 obtained by terrrestrial laserscanning
cell size: 10 cm
spatial reference: CH1903

en: Martell .smet

en: Meteodata used as input for SNOWAPCK simulations, orginating from Station Hintermartell.

en: Fluela .sno

en: Input snow profile (.sno file) used for SNOWPACK simulations of the Flüela heap

en: Martell .sno

en: Input snow profile (.sno file) used for SNOWPACK simulations of the Martell heap

en: Fluela .smet

en: Meteodata used as input for SNOWAPCK simulations, orginating from Station Windtunnel and DAV.

en: HS Martell spring 2015

en:
Snow depth/ height of snow heap in spring 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: European Datum 1950 UTM
Nodata: -9999

en: HS Fluela autumn 2015

en:
Snow depth/ height of snow heap in autumn 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: CH1903
Nodata: -9999

en: HS Martell autumn 2015

en:
Snow depth/ height of snow heap in autumn 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: European Datum 1950 UTM
Nodata: -9999

en:
Two data sets obtained for snow farming projects (Fluela, Davos, CH and Martell, IT) in 2015.

The data set contains for each site:

* 10 cm GIS raster of snow depth calculated from terrestrial laserscanning surveys (TLS) in the end of winter season (April/May)

* 10 cm GIS raster of snow depth calculated from TLS in the end of summer season (October)

Input files for SNOWPACK model:

* .sno: snow profile at the end of winter

* .smet: meteorological data measured by weather stations in the area

For more details see Grünewald, T., Lehning, M., and Wolfsperger, F.: Snow farming: Conserving snow over the summer season, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-93, in review, 2017.

snow

display_name: snow

snow-conservation

display_name: snow-conservation

snow-farming

display_name: snow-farming

snowpack

display_name: snowpack

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

winter-tourism

display_name: winter-tourism

educ

en: Education, culture and sport

en: Snowfarming data set Davos and Martell 2015


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

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

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

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

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

en: Wind tunnel water vapour isotopic composition

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

en: Snow samples - microCT measurements

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

en: Snow samples - sphere size statistics files

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

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

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

blowing-snow

display_name: blowing-snow

cold-lab

display_name: cold-lab

drifting-snow

display_name: drifting-snow

experiments

display_name: experiments

metamorphism

display_name: metamorphism

snow-saltation

display_name: snow-saltation

water-isotopes

display_name: water-isotopes

wind-tunnel

display_name: wind-tunnel

envidat

en: EnviDat: the Environmental Data Portal

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

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


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

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

en: Readme

en: Meta data

en: Measurement data from Micro Rain Radar (MRR)

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

en: Simulation data for a situation with significant snow transport

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

en: Measurement data from micro-meteorological station

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

en: Simulation data for a situation with negligible snow transport

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

en: Measurement data from snow particle counter at MRR site

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

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

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

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

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

antarctica

display_name: antarctica

eddy-covariance-measurements

display_name: eddy-covariance-measurements

large-eddy-simulations

display_name: large-eddy-simulations

mass-flux

display_name: mass-flux

meteorological-data

display_name: meteorological-data

micro-rain-radar

display_name: micro-rain-radar

turbulence

display_name: turbulence

educ

en: Education, culture and sport

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


maintainer: Brandon Vanschaik

en: lidar atmospheric-turbulence remote-sensing wind-energy atmospheric-dynamics wind

en:
This data include wind speeds, wind directions, turbulence, temperature, pressure and humidity data of 10 LiDAR locations along a transect in the Gotthard wind park taken in during the summer of 2023.
The data is quality controlled and annotated according to CF-1.8 data principles. The methods behind this data collection are explained in an article under review called "Resolving three-dimensional wind velocity fields with sequential wind-Doppler LiDAR for wind energy in the complex terrain - Gotthard Pass, Switzerland".

atmospheric-dynamics

display_name: atmospheric-dynamics

atmospheric-turbulence

display_name: atmospheric-turbulence

lidar

display_name: lidar

remote-sensing

display_name: remote-sensing

wind

display_name: wind

wind-energy

display_name: wind-energy

envidat

en: EnviDat: the Environmental Data Portal

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

en: Sequential Wind-Doppler LiDAR wind profile measurements on the Gotthard pass in Switzerland - Summer 2023


maintainer: Tobias Wechsler

en: speed2zero wasserkraft datensatz kleinstwasserkraftwerke schweiz

en: .XLSX-Datei von CH-Kleinstwasserkraftwerke: 1'682 Kleinstwasserkraftwerke (≤ 300 kW) in der Schweiz.

en: Die 1'682 erfassten Kleinstwasserkraftwerke im Datensatz CH-Kleinstwasserkraftwerke

en: .XLSX-Datei der 2’388 registrierten Wasserkraftanlagen in der Schweiz aus den beiden Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW).

en: Shapefile der Einzugsgebiete (2 km² ), in denen sich die jeweiligen Kleinstwasserkraftwerke befinden (erstellt von Yasser Haddad. ETH).

en: Shapefile von CH-Kleinstwasserkraftwerke: 1'682 Kleinstwasserkraftwerke (≤ 300 kW) in der Schweiz.

en: Shapefile der 2’388 registrierten Wasserkraftanlagen in der Schweiz aus den beiden Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW).

en:
In der Schweiz werden Wasserkraftanlagen (WKA) mit einer Leistung von mehr als 300 kW systematisch erfasst und in der nationalen Wasserkraftstatistik (WASTA) zusammengetragen. Die WASTA bildet eine umfangreiche Datengrundlage für Analysen bezüglich Wasserkraft, auch im Kontext von Klimawandel, Biodiversitätsverlust und Energiewende. Nicht erfasst auf nationaler Ebene waren bis anhin die sogenannten Kleinstwasserkraftwerke, WKA mit einer Leistung von weniger als 300 kW. Kleinstwasserkraftwerke tragen zwar nicht wesentlich zur Schweizer Elektrizitätsversorgung bei, haben jedoch aufgrund ihrer hohen Anzahl Auswirkungen auf die Gewässerökologie, die mit den Klimawandel noch stärker unter Druck gerät.
Der neu erstellte Datensatz CH-Kleinstwasserkraftwerke umfasst 1'682 WKA mit einer Leistung von weniger als 300 kW. In den zwei Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW) sind zurzeit 2’388 WKA erfasst. Im Austausch mit den 26 Kantonen wurde jedoch klar, dass es sich nicht um eine vollständige Sammlung von Kleinstwasserkraftwerken handelt. Die 1'682 Kleinstwasserkraftwerke machen rund 70% aller erfassten WKA in der Schweiz aus, tragen jedoch mit weniger als 600 GWh/Jahr lediglich rund 1.5% zur schweizerischen Wasserkraftproduktion bei. In einem optimistischen Szenario, in dem die jährliche Produktionserwartung tendenziell überschätzt wird, liegt dieser Anteil um 0.3% höher. Von den erfassten 2’388 WKA erzeugen die 10% der grössten Anlagen über 90% der schweizerischen Wasserkraftproduktion. Der Datensatz CH-Kleinstwasserkraftwerke ist im Rahmen der ETH-Initiative SPEED2ZERO entstanden, die Klimadienstleistungen in den Bereichen Klimawandel, Biodiversitätsverlust und Energiewende erbringt (https://speed2zero.ethz.ch).
Hier verfügbare Datensätze:
- CH-Kleinstwasserkraftwerke - der hier vorgestellte Datensatz mit 1'682 Kleinstwasserkraftwerken
- EZG-Kleinstwasserkraftwerke - die Einzugsgebiete (2 km² ), in denen sich die 1'682 Kleinstwasserkraftwerke befinden (erstellt von Yasser Haddad. ETH)
- Metadaten - sämtliche Metadaten zum Datensatz CH-Kleinstwasserkraftwerke
- CH-WKA - eine Zusammenstellung sämtlicher 2'388 erfassten Wasserkraftanlagen in der Schweiz aus den beiden Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW)

datensatz

display_name: datensatz

kleinstwasserkraftwerke

display_name: kleinstwasserkraftwerke

schweiz

display_name: schweiz

wasserkraft

display_name: wasserkraft

en: CH-Kleinstwasserkraftwerke - ein schweizweiter Datensatz zu Kleinstwasserkraftwerken