en: microstructure snow-density snowmicropen antarctica snow-density-profile

en: Python code exported from the Jupyter notebook for processing the SnowMicroPen files

en: Documentation SnowMicroPen measurements

en: This *.zip archive contains all available documentation of the SnowMicroPen measurements, including the mapping of the SMP measurements in the terrestrial laser scan field at PEA (see documentation for 20161220) and at the Hammarryggen Ice Rise (see documentation for 20190104 and 20190111).

en: Manual snow pits and other snow density measurements

en: Jupyter notebook for processing the SnowMicroPen files.

en: SnowMicroPen pts files and processed txt files

en: Zip archive containing the acquired *.pts files (binary files recorded by the SnowMicroPen), and the converted *.txt files using the SMP.ipynb jupyter notebook (see the python code in the other resources in this dataset)

en: List of SnowMicroPen measurements

en: List of all SnowMicroPen measurements, including date and location, and processing status. This list has been generated by the python code (see other resources in this dataset).

en: SnowMicroPen (SMP) measurements and manual snowpits from Dronning Maud Land, East Antarctica. Measurements were taken in the vicinity of the Belgium Princess Elisabeth Station (PEA), in a transect towards the coast, and on the Lokeryggen and Hammarryggen Ice Rises near the coast. Measurements were taken during 3 individual campaigns in the 2016-2017, 2018-2019 and 2019-2020 field seasons.

antarctica

display_name: antarctica

microstructure

display_name: microstructure

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen

educ

en: Education, culture and sport

en: SnowMicroPen measurements and manual snowpits from Dronning Maud Land, East Antarctica


maintainer: EnviDat Support

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

state: active

type: dataset

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.

format: SERVICE

en: Measurement data from micro-meteorological station

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

en: Readme

media_type: text/plain

en: Meta data

format: TXT

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 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: Measurement data from Micro Rain Radar (MRR)

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

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

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.

blowing-snow

display_name: blowing-snow

drifting-snow

display_name: drifting-snow

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

fr: Éducation, culture et sport

de: Bildung, Kultur und 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

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

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


biomass-of-total-dead-wood

title_for_slug: biomass-of-total-dead-wood

en:
Dry weight (mass) of all deadwood. This consists of the standing dead trees and shrubs starting at 12cm dbh and the lying deadwood starting at 7cm in diameter.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

biogeographical-region

display_name: biogeographical-region

biomass-of-total-dead-wood

display_name: biomass-of-total-dead-wood

canton

display_name: canton

economic-region

display_name: economic-region

ownership

display_name: ownership

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

educ

en: Education, culture and sport

en: Biomass of total dead wood


pollination-experiment-insect-traits

title_for_slug: pollination-experiment-insect-traits

en: gardens plant-pollinator-interactions urban-ecology zurich insects biodiversity hoverflies bees seed-production phytometer urbanization

en: Trait values of bees

en:
The data contains the following columns:
- SiteID: numeric, the identity of the garden where the bee was sampled.
- Taxonname: character, species name
- Lecty: numeric discreet, a mesure of specialisation degree. Levels: 1 = monolectic; 2=oliolectic; 3=polilectic
- Pheno.start= numeric discreet, the starting week of the flying activity.
- Pheno.end= numeric discreet, the ending week of the flying activity.
- Pheno.duration=numeric discreet, Pheno.end - Pheno.start
- Daytime.activity = numeric continuous, individual-level measure of the day hour when the bee was sampled
- ITD = numeric continuous, individual-level measure of the InterTegular Distance, a metric of mobility in mm.
- Rel.tongue.length =numeric continuous, individual-level measure of the relative tongue length (Tongue length / ITD), a metric of specilaisation degree, in mm.

en: Understanding the interplay of local and landscape-scale drivers of plant-pollinator interactions is crucial to maintaining pollination services in urban environments. The data contains plant-pollinator interactions changed across two independent gradients of local flowering plant species richness and landscape-scale urbanisation level (proportional area of impervious surface within a 500-m radius) in 24 home gardens in the city of Zurich, Switzerland. The data also contains the trait values (tongue length, body size and activity time) of all visiting wild- and honeybees.

bees

display_name: bees

biodiversity

display_name: biodiversity

gardens

display_name: gardens

hoverflies

display_name: hoverflies

insects

display_name: insects

phytometer

display_name: phytometer

plant-pollinator-interactions

display_name: plant-pollinator-interactions

seed-production

display_name: seed-production

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

zurich

display_name: zurich

en: Pollination experiment: insect traits


maintainer: Esther Frei

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

media_type: application/zip

format: ZIP

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

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


en: icp-forests long-term-forest-ecosystem-research stem-diameter swiss-forest-lab tree-growth

en: Data Request

en:
Tree circumference change measurements from plastic girth bands

 Purpose:

Assessment of annual tree stem growth

 Manual Citation:

 * Dobbertin M, Neumann M, 2016: Part V: Tree Growth. In: UNECE ICP Forests, Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest Ecosystems, Eberswalde, Germany, 17 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)

 Paper Citation:

 * Etzold S, Waldner P, Thimonier A, Schmitt M, Dobbertin M (2014) Tree growth in Swiss forests between 1995 and 2010 in relation to climate and stand conditions: Recent disturbances matter. Forest Ecology and Management, 311: 41-55. [doi: 10.1016/j.foreco.2013.05.040](http://doi.org/10.1016/j.foreco.2013.05.040)

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

stem-diameter

display_name: stem-diameter

swiss-forest-lab

display_name: swiss-forest-lab

tree-growth

display_name: tree-growth

en: Manual circumference band measurement LWF


mosaic-snow-on-sea-ice-data

title_for_slug: mosaic-snow-on-sea-ice-data

en: drifting-and-blowing-snow arctic-sea-ice snow-distribution spatial-variability surface-mass-balance

en: SNOWPACK setup for MOSAiC

en:
 The zip file contains both input and output data as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".

The zip file contains 2 main folders:
* SNOWPACK_JGR_ALPHA1
* SNOWPACK_JGR_ALPHA3

In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file.

To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.

en: ALPINE3D setup for MOSAiC

en:
The zip file contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.
The setup contains both input and output data as well as the model configuration as used in the submitted manuscript
"Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".

The zip file contains 3 main folders:
* base_setup_files
* a3d_jgr_alpha1
* a3d_jgr_alpha3

The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file.

a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects.

To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here.

en:
Data accompanying David Wagners' Dissertation.
Covers model results and various input from ALPINE3D and SNOWPACK adjusted for sea ice during MOSAiC.

arctic-sea-ice

display_name: arctic-sea-ice

drifting-and-blowing-snow

display_name: drifting-and-blowing-snow

snow-distribution

display_name: snow-distribution

spatial-variability

display_name: spatial-variability

surface-mass-balance

display_name: surface-mass-balance

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: MOSAIC Snow on Sea Ice Data


high-resolution-climate-data-for-europe

title_for_slug: high-resolution-climate-data-for-europe

en: air-temperature eur11 maximum-temperature cordex daily-climate minimum-temperature europe precipitation

en: Data Access

en: CHELSA EUR11 Technical Documentation

media_type: application/pdf

en: As the CHELSA project is continuously expanded, certain new parameters, time scales etc. are not included anymore in the original publication (Karger et al. 2017). We are therefore provide this document as a guideline of the current climatic parameters available and will expand it as new parameters or time periods will become available. Please refer to the version history for changes made in the document.

format: PDF

en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present downscaled climate data for the CORDEX EUR11 domain at a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperature lapse rates. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height. The resulting data consist of a daily temperature and precipitation timeseries.

The data is distributed under a: Creative Commons: Attribution 4.0 International (CC BY 4.0) license.

air-temperature

display_name: air-temperature

cordex

display_name: cordex

daily-climate

display_name: daily-climate

europe

display_name: europe

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

educ

en: Education, culture and sport

en: High resolution climate data for Europe


automatic-detection-of-avalanches

maintainer: Matthias Heck

title_for_slug: automatic-detection-of-avalanches

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

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.

mimetype: application/zip

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


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

en: Site photo of the forest station (LEB)

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

mimetype: image/jpeg

en: Forest station data (LEB)

media_type: text/csv

en: Data file.

format: CSV

mimetype: text/csv

en: Data plot of the forest station (LEB)

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

mimetype: application/pdf

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 one meteorological station in Lens in Switzerland which is located within a natural coniferous forest with Scots pine (_Pinus sylvestris_; 150-170 yrs)) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lens 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

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


en: photovoltaic biomethane bioenergy gis complementarity

en:
Base data for quantitites of manure and agricultural residues available in Switzerland aggregated per municipalities and provided yearly and monthly.

Method:
The biomass considered in this study consists of agricultural residues arising according to current practice, i.e. mainly manure and some crop by-products. Availability was calculated following the method of (Burg et al., 2018)   based on the updated national agricultural inventory (FSO, 2018). The animal manure potential considered here represents the total annually collected amount, i.e. generated amount after subtracting losses from grazing. With regard to crop by-products, only a small percentage of the theoretical potential was considered to be available since most crop by-products cannot be readily collected today (Burg et al., 2018). The potential biogas production used in the present study was calculated analogously as in Burg et al. (2018) based on a specific biogas yield from fresh biomass. The additional potential of co-substrates from industrial or municipal organic wastes was not considered here.
Bsed on G1G22 (Municipalities in 2022) according to BFS

References:
Burg, V., Bowman, G., Erni, M., Lemm, R., Thees, O., 2018a. Analyzing the potential of domestic biomass resources for the energy transition in Switzerland. Biomass and Bioenergy 111, 60-69. https://doi.org/https://doi.org/10.1016/j.biombioe.2018.02.007
FSO, 2018. Individual data from the agricultural structure survey STRU. Federal statistical office FSO, Bern.

en: Additional references

en: Additional references to an article

en: Supplementary Information

en: Supplementary Information with regards to how the solar and biomass resources were calculated for the paper. Electricity yields of PV depending on various variables

en: Manure monthly resolution

en: This gives details on how calculation were made to estimate the monthly distribution of manure produced in Switzerland.

en:
Additional references to the article:
Linking solar and biomass resources to generate renewable en-ergy: can we find local complementarities in the agricultural setting?
Gillianne Bowman, Thierry Huber, Vanessa Burg
Energies, https://www.mdpi.com/1996-1073/16/3/1486

Today, the energy transition is underway to tackle the problems of climate change and energy sufficiency. For this transition to succeed, it is essential to use all available re-newable energy resources most efficiently. However, renewable energies often bring high volatility that needs to be balanced. One solution is to combine the use of different renewable sources to increase the overall energy output or reduce its environmental impact. Here, we estimate the agricultural solar and biomass resources at the local level in Switzerland, considering their spatial and temporal variability using Geographic In-formation Systems. We then identify the technologies that could allow synergies or complementarities. Overall, the technical agricultural resources potential is ~15 PJ/annus biogas yield from residual biomass and ~10 TWh/a electricity from solar installed on roofs (equivalent to ~36 PJ/a). Anaerobic digestion, combined heat & power plant, Raw manure separation, Biomethane upgrading, Power to X, Electrolysis, Chill generation and Pho-tovoltaic on biogas facilities could foster complementarity in the system if resources are pooled within the agricultural setting. Temporal complementarity at the farm scale can only lead to partial autarchy. The possible benefits from these complementarities should be better identified, particulary in looking looking at the economic viability of such systems.

bioenergy

display_name: bioenergy

biomethane

display_name: biomethane

complementarity

display_name: complementarity

gis

display_name: gis

photovoltaic

display_name: photovoltaic

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: Linking solar and biomass resources to generate renewable energy: can we find local complementarities in the agricultural setting?


en: snow-ice-temperature snow-stratigraphy weisfluhjoch snow-water-equivalent snow-density

en: WFJ Snow Profiles 1999-2014

en:
Biweekly manual snow profiles from Weissfluhjoch, Davos, Switzerland.
Version: 2015-09-14T16:25

en: README

en: README file giving use metadata.

en: Dataset of manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland. Typical snow profile measurements and observations are included (temperature, density, grain size, grain type, hardness, wetness), following the guidelines of the The International Classification for Seasonal Snow on the Ground (ICSSG) [Fierz, C., Armstrong, R.L., Durand, Y., Etchevers, P., Greene, E., McClung, D.M., Nishimura, K., Satyawali, P.K. and Sokratov, S.A. 2009. The International Classification for Seasonal Snow on the Ground. IHP-VII Technical Documents in Hydrology N°83, IACS Contribution N°1, UNESCO-IHP, Paris].

snow-density

display_name: snow-density

snow-ice-temperature

display_name: snow-ice-temperature

snow-stratigraphy

display_name: snow-stratigraphy

snow-water-equivalent

display_name: snow-water-equivalent

weisfluhjoch

display_name: weisfluhjoch

educ

en: Education, culture and sport

en: Manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland


en: forest-inventory tree-volume terrestiral-laserscanning biomass single-tree lidar

en: Individual tree parameters and volume estimates based on TLS point clouds and allometric models

en:
"ASM" denotes volume estimates calculated by allometric scaling models (as used in Swiss NFI), based on tree height, DBH and D7.
"QSM" denotes volume estimates derived from TLS point clouds by quantitative structure models (QSM).
The remaining variables are geometrical features directly calculated from single tree TLS point clouds.
For more detailed variable descriptions see related publication.

en:
 Dataset
This dataset is based on terrestrial laser scanning (TLS) data acquired during winter 2020/2021 in leaf-off conditions, with a Leica BLK 360 instrument following a tree-centric scanning pattern.
The data was acquired on two sites (47.42°N 8.49°E and 47.504°N, 7.78°E), both of which were managed mixed temperate forest stands.
Individual trees were semi-automatically segmented from the co-registered TLS point clouds.

 Background
Accurate estimates of individual tree volume or biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data.
Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, with existing approaches ranging from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories.
In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM).
Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total tree, stem, coarse wood and fine branch volume.

biomass

display_name: biomass

forest-inventory

display_name: forest-inventory

lidar

display_name: lidar

single-tree

display_name: single-tree

terrestiral-laserscanning

display_name: terrestiral-laserscanning

tree-volume

display_name: tree-volume

en: Individual tree TLS point clouds for tree volume estimation


en: air-temperature watershed-drainage-basins runoff snow-depth precipitation

en: Daten Sperbel-Rappengraben

en: Long-term data on precipitation and runoff are essential to draw firm conclusions about the behavior and trends of hydrological catchments that may be influenced by land-use and climate change. Here the longest continuous runoff records (1903 - 2015) from small catchments (less than 1 km2) in Switzerland (and possibly worldwide) are provided as a data set. The history of the hydrological monitoring in the Sperbel- and Rappengraben (Emmental) is summarized in Stähli et al., Environ Monit Assess (2011). The runoff stations operated safely for more than 90% of the summer months when most of the major flood events occurred. Nevertheless, the absolute values of peak runoff during the largest flood events are subject to considerable uncertainty (also discussed in Stähli et al., 2011). This treasure trove of data can be used in various ways, eg. for analysis of the generalized extreme value distributions of the two catchments, of the mechanisms governing the runoff behavior of small catchments, as well as for testing stochastic and deterministic models.

air-temperature

display_name: air-temperature

precipitation

display_name: precipitation

runoff

display_name: runoff

snow-depth

display_name: snow-depth

watershed-drainage-basins

display_name: watershed-drainage-basins

en: More than one century of hydrological monitoring in two small catchments with different forest coverage, Sperbelgraben and Rappengraben (Switzerland)


maintainer: Ionut Iosifescu Enescu

en: open-source jupyter-notebooks hpc reproducibility research-data-management open-data open-science

en: Open Science Support @ WSL (Poster)

en:
This poster was originally created for the swissuniversities Open Science Action Plan: Kick-Off Forum, and showed to the audience on 17.10.2019. It illustrates how the environmental data portal EnviDat provides the tools for fostering Open Science and Reproducibility of scientific research at WSL.

Supporting open science is a highly relevant user requirement for EnviDat and for implementing FAIR (Findability, Accessibility, Interoperability and Reusability) principles at dataset level.
EnviDat encourages WSL scientists to complement data publication with a complete description of research methods and the inclusion of the open source software, code or scripts used for processing the dataset or for obtaining the published results. By openly publishing open software (e.g. as Jupyter notebooks) alongside research data sets, researchers can contribute to mitigate reproducibility issues.

EnviDat also promotes and supports, where possible and practical, the publication of software as Jupyter notebooks. Jupyter notebooks provide a solution for improved documentation and interactive execution of open code in a wide range of programming languages (Python, R, Octave/Matlab, Java or Scala). These programming languages are widely used in environmental research at WSL and well supported by the Jupyter-compatible kernels. We have sucessfully interfaced EnviDat-hosted notebooks with the WSL High-Performance Computing (HPC) Linux Cluster through a JupyterHub/JuypterLab beta installation on the HPC cluster implemented in close collaboration with the WSL IT-Services.
For existing software that cannot be easily migrated to Jupyter Notebooks, the Open Science and Reproducibility is assisted by containerisation. We have proven that several Singularity containers can successfully run on WSL's HPC cluster.

Finally, the researchers can upload the data/results complemented by code (e.g. as Jupyter Notebooks, or Singularity containers) and any additional documentation in EnviDat. Consequently, they will receive a DOI for the entire dataset, which they can reference in their science paper in order to publish a more reproducible research.

_License_: This poster is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 "No Rights Reserved" international license. You can reuse this poster in any way you want, for any purposes and without restrictions.

hpc

display_name: hpc

jupyter-notebooks

display_name: jupyter-notebooks

open-data

display_name: open-data

open-science

display_name: open-science

open-source

display_name: open-source

reproducibility

display_name: reproducibility

research-data-management

display_name: research-data-management

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: Open Science Support at the Swiss Federal Research Institute WSL. The EnviDat Concept


en: nfi lfi national-forest-inventory forest 2009-2017

en: Corrigendum / Korrigendum

en: Registration in DORA

format: API

en:
Swiss National Forest Inventory. Results of the fourth survey 2009–2017.
The collection of data for the fourth National Forest Inventory (NFI) was carried out from 2009 to 2017, on average eight years after the third survey. The findings about state and development of Swiss forests are described and explained in detail. The report is structured according to the European criteria and indicators for sustainable forest management, namely: forest resources, health and vitality, wood production, biological diversity, protection forest and social economy. Finally, conclusions about sustainability are drawn based on the NFI findings.
Keywords: forest area, growing stock, increment, yield, forest structure, forest condition, timber production, biodiversity, protection forest, recreation, sustainability, results National Forest Inventory, Switzerland

Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009–2017.
In den Jahren 2009 bis 2017 fanden die Erhebungen zum vierten Schweizerischen Landesforstinventar (LFI) statt,
im Durchschnitt acht Jahre nach der dritten Erhebung. Die Resultate über den Zustand und die Entwicklung des Schweizer Waldes werden umfassend dargestellt und erläutert. Der Bericht ist thematisch strukturiert nach den europäischen Kriterien und Indikatoren zur nachhaltigen Bewirtschaftung des Waldes: Waldressourcen, Gesundheit und Vitalität, Holzproduktion, biologische Vielfalt, Schutzwald und Sozioökonomie. Eine Bilanz zur Nachhaltigkeit, basierend auf LFI-Ergebnissen, schliesst die Publikation ab.
Keywords: Waldfläche, Holzvorrat, Zuwachs, Nutzung, Waldaufbau, Waldzustand, Holzproduktion, Biodiversität, Schutzwald, Erholung, Nachhaltigkeit, Ergebnisse Landesforstinventar, Schweiz

Content license: All rights reserved. Copyright © 2020 by WSL, Birmensdorf.

forest

display_name: forest

lfi

display_name: lfi

national-forest-inventory

display_name: national-forest-inventory

nfi

display_name: nfi

educ

en: Education, culture and sport


maintainer: Frank Techel

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

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

format: XLS

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

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

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

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

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

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

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

avalanche-bulletin

display_name: avalanche-bulletin

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forecast

display_name: avalanche-forecast

avalanche-forecasting

display_name: avalanche-forecasting

verification

display_name: verification

en: Re-analyzed regional avalanche danger levels in Switzerland


en: long-term-forest-ecosystem-research swiss-forest-lab icp-forests soil-water-matrix-potential water-cycle

en: Data Request

en:
Continuous measurement of soil matrix potential at 15, 50 and 80 cm depth with Decagon MPS-2 sensors

 Purpose:

Improve the available data for the calibration or validation of the water cycle modells, i.e. the determination of the water flux needed for calculating the leaching fluxes.

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

soil-water-matrix-potential

display_name: soil-water-matrix-potential

swiss-forest-lab

display_name: swiss-forest-lab

water-cycle

display_name: water-cycle

en: MPS-2 soil water matric potential LWF


en: swiss-forest-lab nitrogen acidity atmospheric-deposition long-term-forest-ecosystem-research base-cations icp-forests

en:
Throughfall (precipitation under forest canopy) is a major pathway in forest nutrient cycling, and its quantification is necessary to establish both water and nutrient budgets. Furthermore, parallel sampling of throughfall and precipitation in the open field (bulk precipitation), together with assumptions about the canopy exchange processes (uptake and leaching of nutrients), allow the atmospheric deposition of nutrients and pollutants to be quantifed. Bulk precipitation and throughfall have been sampled since 1994 or later on 15 LWF plots using 3 (in the open) and 16 (in the forest) funnel-type precipitation collectors. These collectors are replaced by 1 (open area) and 4 (forest stand) snow buckets in winter on plots with abundant precipitation in the form of snow. The length of sampling intervals is usually 14 days.

 Purpose:

To assess a major flux of the water and nutrient budget in forests, and to quantify the atmospheric deposition of nitrogen, sulphur and other nutrients. Atmospheric deposition is one of the key factors in the causal chain between emission of air pollutants and acidifying or eutrophying effects in forest ecosystems.

 Manual Citation:

 * Thimonier, A., Brang, P., Wenger, K., 1997. Kapitel C4. Atmosphärische Deposition: Freiland- und Bestandesniederschläge, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-48.

 * Clarke N, Žlindra D, Ulrich E, Mosello R, Derome J, Derome K, König N, Lövblad G, Draaijers GPJ, Hansen K, Thimonier A, Waldner P, 2016: Part XIV: Sampling and Analysis of Deposition. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute  of  Forest  Ecosystems,  Eberswalde,  Germany,  32  p.  +  Annex  [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)

 Paper Citation:

 * Thimonier A, Schmitt M, Waldner P, Rihm B (2005) Atmospheric deposition on Swiss Long-term Forest Ecosystem Research (LWF) plots. Environmental Monitoring and Assessment, 104: 81-118. [doi: 10.1007/s10661-005-1605-9](http://doi.org/10.1007/s10661-005-1605-9)

 * Thimonier A, Kosonen Z, Braun S, Rihm B, Schleppi P, Schmitt M, Seitler E, Waldner P, Thöni L (2019) Total deposition of nitrogen in Swiss forests: Comparison of assessment methods and evaluation of changes over two decades. Atmospheric Environment, 198: 335-350. [doi: 10.1016/j.atmosenv.2018.10.051](http://doi.org/10.1016/j.atmosenv.2018.10.051)

acidity

display_name: acidity

atmospheric-deposition

display_name: atmospheric-deposition

base-cations

display_name: base-cations

nitrogen

display_name: nitrogen

envidat

en: EnviDat: the Environmental Data Portal

en: Atmospheric deposition (throughfall and bulk deposition) LWF


en: spatial-variability thur-catchment modeling streamflow hydrology

en: This study documents the development of a semi-distributed hydrological model aimed at reflecting the dominant controls on observed streamflow spatial variability. The process is presented through the case study of the Thur catchment (Switzerland, 1702 km2), an alpine and pre–alpine catchment where streamflow (measured at 10 subcatchments) has different spatial characteristics in terms of amounts, seasonal patterns, and dominance of baseflow. In order to appraise the dominant controls on streamflow spatial variability, and build a model that reflects them, we follow a two–stages approach. In a first stage, we identify the main climatic or landscape properties that control the spatial variability of streamflow signatures. This stage is based on correlation analysis, complemented by expert judgment to identify the most plausible cause-effect relationships. In a second stage, the results of the previous analysis are used to develop a set of model experiments aimed at determining an appropriate model representation of the Thur catchment. These experiments confirm that only a hydrological model that accounts for the heterogeneity of precipitation, snow related processes, and landscape features such as geology, produces hydrographs that have signatures similar to the observed ones. This model provides consistent results in space–time validation, which is promising for predictions in ungauged basins. The presented methodology for model building can be transferred to other case studies, since the data used in this work (meteorological variables, streamflow, morphology and geology maps) is available in numerous regions around the globe.

hydrology

display_name: hydrology

modeling

display_name: modeling

spatial-variability

display_name: spatial-variability

streamflow

display_name: streamflow

thur-catchment

display_name: thur-catchment

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

en: Data for: Understanding dominant controls on streamflow spatial variability to set-up a semi-distributed hydrological model: the case study of the Thur catchment


maintainer: Lyu

en: species-range-map species-distribution-model species-occurrence fagales species-richness pinales

en: Occurrence maps for species of Fagales and Pinales with few records

en: Point_buffer.zip includes 267 occurrence maps of species that do not have enough occurrence data for polygon mapping.

en: Species range maps of species in Fagales and Pinales (with enough occurrences)

en: species_map.zip includes species 1690 range maps generated based on the integrated method described in Lyu et al., (2022). File names with (polygon) indicate that this map is only generated by polygon mapping, as there are not enough occurrences for SDMs.

en:
This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022).
To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details).

fagales

display_name: fagales

pinales

display_name: pinales

species-distribution-model

display_name: species-distribution-model

species-occurrence

display_name: species-occurrence

species-range-map

display_name: species-range-map

species-richness

display_name: species-richness

educ

en: Education, culture and sport

en: Species distribution maps of Fagales and Pinales (GDPlants)


en: hydrological-response snow-modeling spatial-variability complex-topography snowmelt

en: Alpine3D output Dischma

en:
 Abstract
Snow and hydrological modeling in alpine environments remains a challenge because of the complexity of the processes complexity affecting the mass and energy balance. This study examines the influence of snowmelt on the hydrological response of a high-alpine catchment of 43.2 km2 in the Swiss Alps during the water year 2014-2015. Based on recent advances in Alpine3D, we examine how modeled snow distributions, and modeled liquid water transport within the snowpack influence runoff dynamics. By combining these results with multi-scale field data (snow lysimeter data, distributed snow depths and streamflow), we demonstrate the added value of a more realistic representation of snow distribution at the onset of melt season. At the site scale, snowpack runoff is well simulated when the snowpack mass balance errors are corrected (R2 = 0.95 vs. R2 = 0.61). At the sub-basin scale, a more heterogeneous snowpack leads to a more rapid runoff pulse originated in the shallower areas while an extended melting period (by more than a month) is caused by slower snowmelt from deeper areas. This result is a marked improvement over results obtained using a less heterogeneous snow distribution (i.e., traditional precipitation interpolation method). Catchment hydrological response is also improved by the more realistic representation of snowpack heterogeneity (Nash coefficient of 0.85 vs. 0.74), even though the calibration process smoothens out the differences.
The added value of a more complex liquid water transport scheme is obvious at the site scale but decreases at the sub-basin and basin scales. Our results highlight not only the importance but also the difficulty of getting a realistic snowpack distribution even in a well-instrumented area and present a model validation from multi-scale experimental datasets.

complex-topography

display_name: complex-topography

hydrological-response

display_name: hydrological-response

snow-modeling

display_name: snow-modeling

snowmelt

display_name: snowmelt

en: Influence of slope-scale snowmelt on catchment response simulated with the Alpine3D model


lwf-isone-long-term-research-site

title_for_slug: lwf-isone-long-term-research-site

en: meteorological-data plot swiss-forest-lab lwf meteorological-measurements infrastructure forest

en: Isone Meteorological Data LWF

en: LWF plot Isone. Photo: Anne Thimonier Rickenmann, 2011

en:
![alt text](https://www.envidat.ch/dataset/f4b44f60-4eed-471f-a09e-749a0f5f0683/resource/cb8e84ea-ad8b-476e-be32-e02a927d2449/download/lwf_isone.jpg "LWF Isone")

LWF Plot Isone

- Community: Isone / canton TI
- Date of installation: 5 September 1995
- Size of the plot: 2 ha
- Altitude (m): 1181-1259
- Mean slope: 58%
- Geology (in German): Untergrund: Südalpin, präpermisches Grundgebirge, Ceneri Zone; schiefriger Biotitplagioklasgneis - Oberfläche: Quartär; Moräne, Hängeschutt-.
- Provisional soil type (WSL) : Humic Cambisol
- Woodland association after EK72: 4: Luzulo niveae-Fagetum dryopteridetosum
- Main tree species: Fagus sylvatica
- Management system: former coppice
- Silvicultural system: unmanaged
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 37.4 cm
- Number of trees BHD >= 12 cm (2011): 1254
- Maximum tree age: Fagus sylvatica uneven-aged - 70-85-100 yr

More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/isone.html

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

envidat

en: EnviDat: the Environmental Data Portal

en: LWF Isone long-term research site


en: spatial-development land-use governance urban-regions strategic-spatial-planning spatial-planning

en: Guiding document for the interview transcripts (Metadata of the written transcripts)

en: This is a guiding document supporting the dataset Interview guide and transcripts (CONCUR Aim 2 on Governance).

en: This dataset is composed of an interview guide used to conduct 43 in-depth, qualitative, and in-person interviews with planning experts, academics and practitioners, in 14 European urban regions and the corresponding interview transcripts. These interviews were conducted in the selected urban regions between March and September 2016. They were first digitally recorded and later fully transcribed (verbatim).

en: This dataset is composed of an interview guide used to conduct 43 in-depth, qualitative, and in-person interviews with planning experts, academics and practitioners, in 14 European urban regions and the corresponding interview transcripts (verbatim). These interviews were conducted in the selected urban regions between March and September 2016. They were first digitally recorded and later thoroughly transcribed.

governance

display_name: governance

land-use

display_name: land-use

spatial-development

display_name: spatial-development

spatial-planning

display_name: spatial-planning

strategic-spatial-planning

display_name: strategic-spatial-planning

urban-regions

display_name: urban-regions

envidat

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


maintainer: Anita C. Risch

en: soil-chemistry nutrient-network soil mineralization soil-biology grassland global worldwide

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

bioclim.T.ann: Mean annual temperature (°C)

bioclim.T.var: Temperature variability (°C)

bioclim.T.q.wet: Temperature of the wettest quarter (°C)

bioclim.T.q.dry: Temperature of the driest quarter (°C)

bioclim.T.q.warm: Temperature of the warmest quarter (°C)

bioclim.P.ann: Mean annual preciptiation (mm)

bioclim.P.q.wet: Precipitation of the wettest quarter (mm)

ch.pH: pH

ch.WSK.vol: Water holding capacity (vol%)

ch.bulkdensity.gcm3: Bulk density (g cm-3)

ch.porespace: Pore space (vol%)

ch.sand.perc: Soil texture: sand (%)

ch.silt.perc: Soil texture: silt (%)

ch.clay.perc: Soil texture: clay (%)

ch.NO3start.mgg: NO3 at the beginning of the incubation (mgkg)

ch.Ntot.perc: Total soil N (%)

ch.Ctot.perc: Total soil C (%)

ch.Corg.perc: Soil organic C (%)

ch.netNminfield.mgkgd: Realised soil net N mineralisation (mg kg-1 d-1)

ch.netNminlab.mgkgd: Potential soil ne N mineralisation (mg kg-1 d-1)

archaea.cpg.log: Ammonia oxidising archaea (archaeal amoA gene copies g-1 soil)

bacteria.cpg.log: Ammonia oxidising bacteria (bacterial amoA gene copies g-1 soil)

micr.biom: Microbial biomass (ug g-1 soil)

ch.CtoN: C to N ratio

ch.netNnitriffield.mgkgd.sqrt: Realised soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniffield.mgkgd.sqrt: Realised soil net ammonification, transformed (mg kg-1 d-1)

ch.netNnitriflab.mgkgd.sqrt: Potential soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniflab.mgkgd.sqrt: Potential soil net ammonifiation, transformed (mg kg-1 d-1)

en:
This dataset contains all data on which the following publication below is based.

Paper Citation:

Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2

Please cite this paper together with the citation for the datafile.

We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil

display_name: soil

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide

educ

en: Education, culture and sport


maintainer: Michael Lehning

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

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

mimetype: text/plain

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: Martell .sno

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

en: Martell .smet

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

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: Fluela .smet

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

en: Fluela .sno

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

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:
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

en: Snowfarming data set Davos and Martell 2015


maintainer: Adam Winstral

en: snow-simulation jim snow jules-investigation-model snow-depth

en: HS and SWE observations

en: Automated (HS) and manual (SWE) data used to evaluate model simulations

en: File/data descriptor

en: DEM

en: 1-km resolution DEM used for downscaling gridded NWP data to stations and for distributing BDE evaluations at HS-stations to SWE-stations

en: Station forcing data

en: All forcing data need to run JIM at HS and SWE stations. These are data downscaled directly from COSMO NWP data (WY14, WY15) and further enhanced by additional station observations of snow depth (HS) and air temperature (Ta) in WY17. Further information can be found in README file. Note that in WY14 and WY15, shortwave radiation was not split into direct and diffuse (not necessary for JIM) and that the default method in JIM for splitting precipitation phase was used. In WY 17, direct and diffuse were combined as input to JIM and JIM was forced with pre-processed liquid and solid precipitation phases.

en: These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the "deterministic" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations.

jim

display_name: jim

jules-investigation-model

display_name: jules-investigation-model

snow-depth

display_name: snow-depth

snow-simulation

display_name: snow-simulation

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: Meteorological data used to develop and validate the bias-detecting ensemble (BDE)


lwf-celerina-long-term-research-site

title_for_slug: lwf-celerina-long-term-research-site

en: forest swiss-forest-lab meteorological-measurements plot lwf infrastructure meteorological-data

en: Celerina Meteorological Data LWF

en: LWF plot Celerina. Photo: Oliver Schramm, 2011

en:
![alt text](https://www.envidat.ch/dataset/0c244fe7-886a-4a1c-add5-5f706e995a29/resource/28e97caa-65fc-4396-8bca-860721eddfa2/download/lwf_celerina.jpg "LWF Celerina")

LWF Plot Celerina

- Community: Celerina / canton GR
- Date of installation: 3 July 1996
- Size of the plot: 2 ha
- Altitude (m): 1846-1896
- Mean slope: 34%
- Geology (in German): Untergrund: ostalpin; prätriadische Tiefengesteine - Oberfläche: Quartär; karbonatfreie Moräne
- Soil types (WSL): n.d.
- Woodland association after EK72: 59: Larici-Pinetum cembrae
- Main tree species: Pinus cembra
- Management system: high forest
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 48.6 cm
- Number of trees BHD >= 12 cm (2011): 469
- Maximum tree age: Pinus cembra uneven-aged - 210-250 years

More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/celerina.html

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

en: LWF Celerina long-term research site


basal-area-of-dead-wood

title_for_slug: basal-area-of-dead-wood

en:
Sum of the stem cross-section areas of all dead trees in a stand at a height of 1.3 m (dbh measurement height).

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

basal-area-of-dead-wood

display_name: basal-area-of-dead-wood

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

mechanical-strength-of-wood

display_name: mechanical-strength-of-wood

ownership

display_name: ownership

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

tree-state-standing-lying

display_name: tree-state-standing-lying

en: Basal area of dead wood


en: hazard-mapping snow satellite avalanche natural-hazard

en: SPOT6 Avalanche outlines 24 January 2018

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

format: SHP

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

The generation of the data is described in:

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

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

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

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

Koordinate System: CH1903+ LV95 LN02

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow


rorcc

maintainer: Tobias Wechsler

title_for_slug: rorcc

en: environmental-flow hydropower climate-change run-of-river-power-plants design-discharge

en: RoRCC

en:
The dataset “RoRCC” consists of simulation-based results on climate change impacts on Alpine RoR power production; it is based on 21 Swiss RoR power plants, with a total production of 5.9 TWh a-1. The dataset contains the following information:
-	metadata on the RoR power plants under consideration
-	annual and seasonal production scenarios differentiated into three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020–2049, T2: 2045–2074, T3: 2070–2099)
-	annual and seasonal production loss due to environmental flow requirements
-	annual and seasonal the technical increase potential (via design discharge optimisation)
-	annual and seasonal changes in the hydrological cycle for the three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020–2049, T2: 2045–2074, T3: 2070–2099)

en:
The dataset "RoRCC" consists of simulation-based results on climate change impacts on Alpine RoR power production; it is based on 21 Swiss RoR power plants, with a total production of 5.9 TWh a-1. The dataset contains the following information:
1) metadata on the RoR power plants under consideration, 2) annual and seasonal production potential scenarios under into three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020–2049, T2: 2045–2074, T3: 2070–2099), 3) annual and seasonal streamflow scenarios, 4) annual and seasonal production loss due to environmental flow requirements, 5) annual and seasonal the technical increase potential (via design discharge optimisation) and 6) annual and seasonal changes in the hydrological cycle.

climate-change

display_name: climate-change

design-discharge

display_name: design-discharge

environmental-flow

display_name: environmental-flow

hydropower

display_name: hydropower

run-of-river-power-plants

display_name: run-of-river-power-plants


en: roads buffer density jupyter gis geospatial

en: Road Density Calculation (Python Notebook) online

en: Road data from OSM for the surroundings of Menzberg, Switzerland

en: Plots

en: Random plot locations

en:
This dataset contains the road and plot data used for the geospatial analysis example showcased in "Fostering Open Science at WSL with the EnviDat Environmental Data Portal", a contribution to the 5th Open Source Geospatial Research and Education Symposium (OGRS), 2018.
The example uses Jupyter Notebook to calculate road densities in the neighbourhood of sample plot locations with Python. Road data were extracted from OpenStreetMap, while the point data (sample plots) were generated manually.

buffer

display_name: buffer

density

display_name: density

geospatial

display_name: geospatial

gis

display_name: gis

jupyter

display_name: jupyter

roads

display_name: roads

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Dataset for OGRS 2018 publication


maintainer: Rebecca Mott

en: patchy-snow-covers turbulent-sensible-heat-fluxes dischmex eddy-covariance-measurements snow-patch

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 3.2 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.3 m.

en: Gletschboden setup

media_type: image/png

en: Background map based on data © 2014/2016 swisstopo (JD100042)

format: PNG

mimetype: image/png

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.6 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.5 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.2 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (DA-600, Kaijo-Denki) in 0.3 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 1.7 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 0.7 m.

en: Output explanation

en:
This dataset contains eddy-covariance measurements in the ablation period of 2014-2016. Measurements were taken from two turbulence towers over a long-lasting snow patch, which are 5 m apart from each other (2014 and 2015). The turbulence towers were equipped with two YOUNG ultrasonic anemometers mounted 0.7 m (in 2014) and 3.3 m (in 2015) above snow-free ground, two ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) mounted at 2.6 m (in 2014) and 2.2 m (in 2015) above snow-free ground and one anemometer (DA-600, Kaijo Denki) mounted at 0.3 m above snow surface.

The measurement setup changed in 2016 and includes a measurement above the snow-free ground in upwind direction (Swiss coordinates: 790191/176689). The measurement tower is equipped with one ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 3.3 m above the snow-free ground. Additionally, one measurement tower is installed above the long-Lasting snow patch and equipped with the same setup as 2015.

Turbulence data were sampled at a frequency of 20 Hz. The processing of the data to quality controlled fluxes has been done with the Biomicrometeorology flux software (Thomas et al., 2009). The program applies plausibility tests and a despiking test after Vickers and Mahrt (1997) on the measured data. The routine further applies a time-lag correction and considers the deployment (e.g. the sonic azimuth). A frequency response correction (Moore, 1986) is done and a three-dimensional rotation is performed. Finally, quality assurance/quality control (QA/QC) flags after Foken et al., (2004) are issued and fast Fourier transform power and co-spectra are calculated. The change in snow height is considered in the post-processing for every measurement day. The turbulence data were averaged to 30 minute intervals.

dischmex

display_name: dischmex

eddy-covariance-measurements

display_name: eddy-covariance-measurements

patchy-snow-covers

display_name: patchy-snow-covers

snow-patch

display_name: snow-patch

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

en: DISCHMEX - Impact of extreme land-surface heterogeneity on micrometeorology over spring snow-cover


en: avalanche hazard-mapping impact-pressure modelling ramms release

en: Scenario 300 years with forest

en:
PRA_.shp = avalanche release areas shape file with the attributes in the attribute table
_maxHEIGHT.tif = maximum flow height [m]
_maxPRESSURE.tif = maximum impact pressure [kPa]
_maxVELOCITY.tif = maximum flow velocity [m/s]
_outlines = outline shape file of every avalanche simulation linked to the PRA shape file by ID

en: Extent of the canton of Grisons

media_type: application/json

format: JSON

en: Scenario 100 years without forest

en: Scenario 300 years without forest

en: Scenario 30 years without forest

en: Scenario 10years with Forest

en: Scenario 100 years with forest

en: Scenario 30 years with forest

en: Scenario 10 years without forest

en:
We developed a workflow to generate Large Scale Hazard Simulations for avalanches based on digital elevation models and information on the protective function of the forest. This datasets contains the potential avalanche release areas (PRA) as polygons, the simulation outputs (maximum pressure, maximum flow velocity and maximum flow height) as .tif rasters and the outlines of the simulated avalanches (polygon) for the entire area of the canton of Grisons (7105 km2). The simulations are performed for the scenarios wit return periods of 10, 30, 100 and 300 years, once with (FOR) and once without (NoFor) taking the effect of the forest into account.

The details can be found in this publication:

Bühler, Y., Bebi, P., Christen, M., Margreth, S., Stoffel, L., Stoffel, A., Marty, C., Schmucki, G., Caviezel, A., Kühne, R., Wohlwend, S., and Bartelt, P.: Automated avalanche hazard indication mapping on state wide scale, Nat. Hazards Earth Syst. Sci. Discuss., 2022, 1-22, 10.5194/nhess-2022-11, 2022.

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

impact-pressure

display_name: impact-pressure

modelling

display_name: modelling

ramms

display_name: ramms

release

display_name: release

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Large Scale Hazard Indication Simulations for avalanches, canton of Grisons


maintainer: Elisabeth Hafner

en: avalanche hazard-mapping natural-hazard snow satellite

en: SPOT6 Avalanche Outlines 16 January 2019

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset and within the limitations of the dataset described in the publications.

en:
Outlines of 6'041 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 16 January 2019. The dataset was acquired following a period with very high avalanche danger. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping16012019.pdf)

The generation of the data is described in:

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

The data was comprehensivly validated in a subset area in

Hafner, E.D.; Techel, F.; Leinss, S.; Bühler, Y., 2021: Mapping avalanches with satellites - evaluation of performance and completeness. Cryosphere, 15, 2: 983-1004. doi: 10.5194/tc-15-983-2021

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow

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: SPOT6 Avalanche outlines 16 January 2019


en: swiss-forest-lab deadwood coarse-woody-debris icp-forests long-term-forest-ecosystem-research

en: Data Request

en:
Assessment of coarse woody debris on LWF plots using full count methods on defined subplots (applying ForestBiota protocoll)

 Purpose:

Assessing the amount of coarse woody debris

 Manual Citation:

 * Franziska Heinrich, 2005: Totholz-Aufnahme mit ForestBIOTA Protokoll. Langfristige Waldökosystem-Forschung LWF. Eidg. Forschungsanstalt WSL, Birmensdorf, 7 S.

 * ForestBiota, 2005. Stand structure assessment including deadwood. EU/ICP Forests Biodiversity Test-Phase (ForestBIOTA). [>>>](http://www.forestbiota.org)

 * Fischer, R., Fischer, R., Seidling, W., Granke, O., Meyer, P., Stofer, S., Travaglini, D., 2007. ForestBIOTA – Testphase zur Erfassung der biologischen Vielfalt. AFZ/Wald 62, 1070.

 Paper Citation:

 * Seidling, W., Travaglini, D., Meyer, P., Waldner, P., Fischer, R., Granke, O., Chirici, G., Corona, P., 2014. Dead wood and stand structure - relationships for forest plots across Europe. IForest - Biogeosciences and Forestry 7, 269-281.

coarse-woody-debris

display_name: coarse-woody-debris

deadwood

display_name: deadwood

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

swiss-forest-lab

display_name: swiss-forest-lab

en: Deadwood survey LWF 2005 - subplot (Forets Biota)


en: rcm cryosphere software toposcale cordex downscaling

en:
Model code and documentation for the downscaling model TopoCLIM which provides methods to downscale climate timeseries from CORDEX RCM data. This scheme specifically addresses the need for hillslope scale atmospheric forcing timeseries for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps.

A working example is provided in this code archive but for full running of the scheme TopoSCALE is required https://doi.org/10.5194/gmd-7-387-2014 with code available at https://github.com/joelfiddes/tscaleV2. Standard library dependencies are given in the python requirements.txt of the archive with installation instructions in the README.md.

cordex

display_name: cordex

cryosphere

display_name: cryosphere

downscaling

display_name: downscaling

rcm

display_name: rcm

software

display_name: software

toposcale

display_name: toposcale

en: TopoCLIM v1.1 code


en: connectivity multifunctionality green-infrastructure planning-principles strategic-planning

en: Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure corresponding to items from 5 to 11 of the protocol. The data was discussed qualitatively in the research paper.

en:
The present dataset is part of the published scientific paper Grădinaru, S. R., & Hersperger, A. M. (2019). Green infrastructure in strategic spatial plans: Evidence from European urban regions. Urban forestry & urban greening, 40, 17-28.
The goal of this research was to conduct a comparative analysis of the integration of green infrastructure concept in strategic spatial plans of European Urban regions. Specifically, the paper has the following objectivs: 1) which principles of GI planning are followed in strategic plans of urban regions? 2) can we identify different approaches to GI integration into strategic planning?. The study focues on a sample consisting of 14 case studies spanning 11 countries. We retrieved the strategic plans from the websites of the planning authorities. The list of the reviewed planning documents can be found in Appendix A of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper.
The planning documents were read in order to address the protocol items. The answer to the protocol items in each of the first two categories (items 1–11) was documented as text, while the answer for the third category, namely items addressing the planning principles (items 12–36), was coded according to Table 1 of the article. As a result, we provide the folowing outputs:
•	GI_Dataset_1_Items_1-12.xlsx – available on request
o	Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure coresponding to items from 1 to 12 of the protocol. The data was discussed qualitativly in the research paper.
•	GI_Dataset_2_Items_12-36.csv – freely available
o	Results of the coding on principles of GI planning followed in strategic plans of urban regions– this data was derived from the coding procedure coresponding to items from 12 to 36 of the protocol. The data served as input for the classifications performed through hierarchical cluster analysis. This data is a detailed version of Appendix C in the paper.

connectivity

display_name: connectivity

green-infrastructure

display_name: green-infrastructure

multifunctionality

display_name: multifunctionality

planning-principles

display_name: planning-principles

strategic-planning

display_name: strategic-planning


en: larch-budmoth nitrogen phenol spongy-moth flight-traps nun-moth gypsy-moth

en: Moth monitoring

en: See ReadMe

en: The population dynamics of eruptive moths were monitored with pheromone traps and the composition of the larvae's foodplants were analyzed for water content, nitrogen and total phenolics. Moth catches cover a period of 20 years, leaf analyses 10 years. For Zeiraphera griseana (= Z. diniana) only needle analyses are available. The corresponding data on the moth population dynamics are property of A. Fischlin, ETH Zürich, and will be made available on EnviDat as well.

flight-traps

display_name: flight-traps

gypsy-moth

display_name: gypsy-moth

larch-budmoth

display_name: larch-budmoth

nitrogen

display_name: nitrogen

nun-moth

display_name: nun-moth

phenol

display_name: phenol

spongy-moth

display_name: spongy-moth

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Monitoring of Lymantria dispar, Lymantria monacha and Zeiraphera griseana and host food quality in the Swiss Alps


en: eddy-covariance-measurements snow-melt atmospheric-turbulence patchy-snow-cover surface-heat-exchange

en: Raw eddy covariance data from tower 2 at 1m height. Instrument is Campell Scientific IRGASON

en: Raw eddy covariance data from tower 2 at 3m height. Instrument is Campell Scientifc CSAT 3

en: IR-data that is visualized in the video

en: Raw eddy covariance data from the short-path ultrasonic ("Kaijo") at 0.3m above the snow

en: Raw eddy covariance data from tower 2 at 2m height. Instrument is Campell Scientifc CSAT 3

en: Data and code for the publication. The data is raw data. Processed data can be obtained upon request.

atmospheric-turbulence

display_name: atmospheric-turbulence

eddy-covariance-measurements

display_name: eddy-covariance-measurements

patchy-snow-cover

display_name: patchy-snow-cover

snow-melt

display_name: snow-melt

surface-heat-exchange

display_name: surface-heat-exchange

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: Turbulence in The Strongly Heterogeneous Near-Surface Boundary Layer over Patchy Snow


biomass-of-standing-dead-trees

title_for_slug: biomass-of-standing-dead-trees

en:
Dry weight (mass) of dead, standing trees and shrubs starting at 12 cm dbh. This consists of the tree parts: roots, stemwood and also, depending on the degree of decomposition, the branch coarse wood.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

biogeographical-region

display_name: biogeographical-region

biomass-of-standing-dead-trees

display_name: biomass-of-standing-dead-trees

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

en: Biomass of standing dead trees


en: Request Data

en: This dataset is hosted by IAP and therefore, access to the dataset must be requested to the original data provider.

en: Additional publications

media_type: text/html

format: HTML

en:
The IAP has been monitoring forest health since 1984 on behalf of cantonal forest and environmental offices and in collaboration with the Federal Office for the Environment. In the Intercantonal Forest Observation Program (WDB) project, the health, growth and nutrient supply of beech, spruce and oak in managed forest ecosystems are studied on about 190 forest plots. The plots cover the most important gradients of Swiss forests, such as different climatic conditions in precipitation, temperature, as well as differences in soil and in the input of air pollutants such as nitrogen and ozone. Currently (date 2020) 94 plots with 6268 beech trees, 75 plots with 4547 spruce trees, and 50 plots with 1858 oak trees are surveyed in the Intercantonal WDB. Annual investigations include crown condition and tree loss by mortality, uprooting and stem breakage. Every 4 year stem increment is measured on all 12’673 trees and 8 branches are harvested by helicopter very 4 years and subsequently examined for the following parameters:

- Nutrient analysis in foliage (N, P, K, Ca, Mg, Mn)
- Leaf area and weight
- Shoot growth (retrospective for Norway spruce, beech and oak)
- Discoloration and parasite infestation
- Fructification of beech (retrospectively)

In addition, soil samples for analysing the chemistry of the solid phase are taken every 12 years. Also changes in the ground vegetation is assessed every 12 years. On a subset of the plots soil solution is sampled monthly *[(Soil solution monitoring of the Intercantonal Forest Observation Program)](https://www.envidat.ch/dataset/soil-solution-monitoring-of-the-intercantonal-forest-observation-program)*. In addition, soil water potential and soil moisture are measured in a subset of sites of the Intercantonal Forest Observation Program *[(Soil moisture and soil water potential measurements of the Intercantonal Forest Observation Program)](https://www.envidat.ch/dataset/soil-water-measurements-wdb)*.

These data allow to gain a unique understanding of the main factors influencing the forest and the most important changes, which are very valuable especially in the context of climate change. The insights are of great importance for cantonal and regional forest planning as well as for national and international determinations of critical loads and limits (e.g. CLempN, UNECE CLRTAP).

iap

display_name: iap

long-term-data-sets

display_name: long-term-data-sets

long-term-monitoring

display_name: long-term-monitoring

swiss-forest-lab

display_name: swiss-forest-lab

wdb

display_name: wdb

en: Intercantonal Forest Observation Program – Interkantonale Walddauerbeobachtung (WDB)


en: forcing transport freshwater-flux freezing salinity cryospheric-science sea-ice satellite antartic melting sea-ice-divergence polar southern-ocean physical-oceanography salt sea-ice-volume mass-balance climate-change

en: Data in NetCDF4 CF-1.6 format

en: Link to Related Publication

en: Explanatory Document

en:
This data set provides estimates of annual fresh water fluxes related to sea-ice formation from ocean freezing and snow-ice formation, sea-ice melting, lateral transport of sea ice in the Southern Ocean over the period 1982 to 2008.It is derived from a mass balance calculation of local sea-ice volume change and divergence from satellite data and sea-ice reconstructions. The mass balance is calculated on a daily basis and fluxes are then integrated over the entire year, where a year is defined from March to February of the next year (i.e. from March 1982 to February 2009). This approach combines multiple products of sea-ice concentration (Cavalieri & Parkinson, 2008;Comiso, 1986; Meier et al., 2013), sea-ice thickness (Kurtz & Markus, 2012; Massonnet et al., 2013; Worby et al., 2008), and sea-ice drift (Fowler et al., 2013; Kwok 2005; Schwegmann et al., 2011). For a detailed description of the method see Haumann et al. (2016). The data set is derived to estimate large-scale (regional to basin-scale) fluxes on an annual basis. Our confidence is reduced on a grid cell basis, such as for single coastal polynyas, where the method and underlying data induce large, unknown uncertainties.

_Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues._

Related publication: http://www.nature.com/nature/journal/v537/n7618/full/nature19101.html (doi:10.1038/nature19101)

Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues.

antartic

display_name: antartic

climate-change

display_name: climate-change

cryospheric-science

display_name: cryospheric-science

forcing

display_name: forcing

freezing

display_name: freezing

freshwater-flux

display_name: freshwater-flux

mass-balance

display_name: mass-balance

melting

display_name: melting

physical-oceanography

display_name: physical-oceanography

polar

display_name: polar

salinity

display_name: salinity

salt

display_name: salt

satellite

display_name: satellite

sea-ice

display_name: sea-ice

sea-ice-divergence

display_name: sea-ice-divergence

sea-ice-volume

display_name: sea-ice-volume

southern-ocean

display_name: southern-ocean

transport

display_name: transport

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Antarctic sea-ice freshwater fluxes associated with freezing, transport, and melting


en: uas snow-depth snow swe remote-sensing

en: HS map Brämabül, 14. April 2015

media_type: image/tiff

en:
10 cm resolution
15 cm accuracy

format: TIFF

mimetype: image/tiff

en: .tfw file

en: coordinates for the .tif raster

en:
This snow depth map was generated 14 January 2015, close to peak of winter accumulation, applying Unmanned Aerial System digital surface models with a spatial resolution of 10 cm. The covered area is 285'000 m2 at the top of Brämabüel, 2490 m a.s.l. covering all expositions. Coordinate system: CH1903LV03.

A detailed description is given here:

Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075-1088, 10.5194/tc-10-1075-2016, 2016.

Abstract:
Detailed information on the spatial and temporal distribution, and variability of snow depth (HS) is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Nowadays, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network is not able to capture the large spatial variability of snow depth in alpine terrain.
Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, such data acquisition is costly, if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand, is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UAS), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Such systems have the advantage that they are comparatively cost-effective and can be applied very flexibly to cover also otherwise inaccessible terrain. In this study we map snow depth at two different locations: a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and b) an exposed location (2500 m a.s.l.) on a peak in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) better than 0.07 to 0.15 m on meadows and rocks and a RMSE better than 0.30 m on sections covered by bushes or tall grass. This new measurement technology opens the door for efficient, flexible, repeatable and cost effective snow depth monitoring for various applications, investigating the worlds cryosphere.

remote-sensing

display_name: remote-sensing

snow

display_name: snow

snow-depth

display_name: snow-depth

swe

display_name: swe

uas

display_name: uas

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: UAS based snow depth maps Brämabüel, Davos, CH