en: The reaction of ozone with bromide in polar regions results in the formation of reactive bromide species with impacts on ozone budget and the oxidative capacity of the lower atmosphere. Here, we present a data investigating the temperature dependence of bromide oxidation by ozone using a coated wall flow tube reactor coated with an aqueous mixture of citric acid and sodium bromide, a proxy for sea salt aerosol in snow or the free troposphere. Thus study shows the effect of of organic species at relatively mild temperatures between the freezing point and eutectic temperature as typical for Earth's cryosphere.

en: antartic cryosphere laboratory-study arctic sea-salt-aerosol polar snow

en: Kinetic experiments on the oxidation of bromide by ozone from 289-245 K.

envi

en: Environment

en:
Resource Title: Experimental Results: Uptake coefficients vs. temperature and ozone concentration
    Size: 1422 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8c77d71e-155b-40ab-a446-ae2dccac9740/resource/af7e17ca-b08b-48d2-8198-621ec0814252/download/dataenivdat.csv
    Description: * Final Data to Figure 3: Uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.
* Final Data to Figure 6: Uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

The average ozone uptake coefficients of the three cycles between the bypass and the flow tube per repeat is taken as the mean uptake coefficient for each experiment. Gamma was corrected for gas phase diffusion limitation following the Cooney–Kim– Davis (CKD) method52 (see supporting material of publication for details).

en:
Resource Title: Experimental results of uptake coefficient vs. temperature and ozone concentration
    Size: 1279 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8c77d71e-155b-40ab-a446-ae2dccac9740/resource/fd675417-23be-47bb-bcf3-44c3bb2eb572/download/dataenivdat.csv
    Description: Data to Figure 3: Measured uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.

Data to Figure 6: Measured  uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

antartic

display_name: antartic

arctic

display_name: arctic

cryosphere

display_name: cryosphere

laboratory-study

display_name: laboratory-study

polar

display_name: polar

sea-salt-aerosol

display_name: sea-salt-aerosol

snow

display_name: snow


$oid: OBSOLETE

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Othmarsingen in Switzerland where one station is located within a natural deciduous forest stand (OTB) with European beech (_Fagus sylvatica_; 120-140 yrs) and lime trees (_Tilia sp._; 120-140 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, OTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Othmarsingen is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

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

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

fr: Environnement

it: Ambiente

en:
Resource Title: Site photo of the forest station (OTB)
    Size: 319164 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/b1937555-6a89-4452-93f9-3cf418382fb6/download/otb.jpg
    Description: Site photo of the forest station from above (viewing downwards the slope).

format: jpeg

en:
Resource Title: Forest station data (OTB)
    Size: 10501901 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/f5f71a78-9574-4bcc-a326-4d455b6821a4/download/otbforest.csv
    Description: Data file.

format: CSV

media_type: text/csv

en:
Resource Title: Field station data (OTF)
    Size: 11582801 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/5b4e810a-ce77-47dc-9e23-ba691ade9192/download/otffield.csv
    Description: Data file.

en:
Resource Title: Data plot of the forest station (OTB)
    Size: 255873 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/f01faaab-8cee-4b07-b95c-1498f5b09285/download/otbplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

format: PDF

media_type: application/pdf

en:
Resource Title: Site photo of the field station (OTF)
    Size: 11868194 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/74f4dcb2-e18d-40bb-bb18-8383f907a285/download/otf.jpg
    Description: Site photo of the field station from below (viewing upwards the slope).

en:
Resource Title: Data plot of the field station (OTF)
    Size: 302026 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/663df714-b0fb-45d3-b83f-3e80755a0a1a/download/otfplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Site aerial photo (Othmarsingen)
    Size: 7687492 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/d74e4b24-bd75-4ce2-8bb5-4c04b77d8099/download/otfront.jpg
    Description: Site aerial photo from both Othmarsingen stations. Source: Google. 2016. Site aerial photo from both Othmarsingen stations. Retrieved November 11, 2016, from https://www.google.ch/maps

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed


en:
This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps).
The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting.
The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.
Additional notes for vector data:
Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API -  https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any  perimeter or area can be digitised freely using the QGIS editing toolbar.
The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:
- Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
- Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif
This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.
Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.
This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

en: accessible-forest seilaplan qgis geoprocessing forest openstreetmap

en: Sample Geodata and Software for Demonstrating Geospatial Preprocessing for Forest Accessibility and Wood Harvesting at FOSS4G2019

en:
Resource Title: Preview
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    Format: image/png
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/729c975c-df90-4af2-979a-af44069dc02f/download/preview.png
    Description:

format: PNG

media_type: image/png

en:
Resource Title: Power lines vector data (close to the area of the sample forest range)
    Size: 66908 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/c567f81c-26c9-4122-8fbd-045c55fb73c0/download/osm_powerlines_export_25832.zip
    Description: EPSG:25832

format: ZIP

media_type: application/zip

en:
Resource Title: Railways vector data (close to the sample forest range)
    Size: 50776 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/a787f798-0c3d-4cd3-a9d1-c93f51176465/download/osm_railways_export_25832.zip
    Description: EPSG:25832

en:
Resource Title: Juypter Notebook for Data Preparation (online)
    Size: 0
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    URL: https://nbviewer.jupyter.org/urls/www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/e0c2a4f8-ee8e-4ca9-b05f-0adf2401c879/download/data_preparation_foss4g2019.ipynb
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en:
Resource Title: DEM (for the area of the sample forest range)
    Size: 1695063 bytes
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    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/1bd80d45-a8fd-44dc-b153-7abfa6345d1c/download/eudem_ogdbayern_25m_25832.tif
    Description: EPSG:25832

format: TIFF

media_type: image/tiff

en:
Resource Title: Digitised sample perimeter
    Size: 2083 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/710a065d-d50e-4a3d-a993-2a65197cef7b/download/digitized_sample_perimeter.zip
    Description: EPSG:25832

en:
Resource Title: Juypter Notebook for Data Preparation
    Size: 29318 bytes
    Format: application/x-ipynb+json
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/e0c2a4f8-ee8e-4ca9-b05f-0adf2401c879/download/data_preparation_foss4g2019.ipynb
    Description:

en:
Resource Title: Forest vector data (for sample forest range)
    Size: 288311 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/16db97c5-5546-4f80-9cb9-e562637b589c/download/osm_forest_export_25832.zip
    Description: EPSG:25832

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan


en:
This data set includes GPS-derived snow water equivalent (SWE), snow depth (HS) and liquid water content (LWC) data for three entire snow-covered seasons (2015-2016, 2016-2017, 2017-2018) at the study plot Weissfluhjoch 2540 m a.s.l. (Davos, Switzerland). The procedure to derive these snow properties is described in Koch et al. (2019). The novel approach is based on a combination of GPS signal attenuation and time delay.
The dataset also includes corresponding validation data for SWE and HS measured at Weissfluhjoch, and some additional meteorological data used for interpretation of the snow cover evolution.
Please refer to the Read-me file for further details on the data.
These data are the basis of the following publication:
- Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M., Schweizer, J., and Mauser, W., 2019. Retrieval of snow water equivalent, liquid water content and snow height of dry and wet snow by combining GPS signal attenuation and time delay. Water Resources Research, 55(5), 4465-4487. https://doi.org/10.1029/2018WR024431

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

envi

en: Environment

en:
Resource Title: Dataset with GPS-derived snow cover properties and validation data
    Size: 90714 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/cd57ac77-a276-4783-88c9-f52f51552d2c/resource/01f4a113-7ad0-4993-9efa-f30bbe0f0474/download/gps-derived_snow_properties.csv
    Description: This file includes the data as described in the Read-me file.

en:
Resource Title: Read-me
    Size: 109310 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/cd57ac77-a276-4783-88c9-f52f51552d2c/resource/d491aadc-0a43-4caa-8b32-09c988ec4841/download/readme.pdf
    Description: The read-me file contains the description of the data provided.

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth


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

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

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

en:
Resource Title: Site aerial photo (Schänis)
    Size: 7026512 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/9a6bd255-998b-44c0-9ab2-cc501f9bb78b/download/scfront.jpg
    Description: Site aerial photo from both Schänis stations. Source: Google. 2016. Site aerial photo from both Schänis stations. Retrieved November 11, 2016, from https://www.google.ch/maps

en:
Resource Title: Data plot of the field station (SCF)
    Size: 304254 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/43a2b0ef-6611-4bf2-9d60-60577413515d/download/scfplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Further details on the data repository
    Size: 17153511 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/ee5c2d0d-e229-42e3-9144-37e92827f614/download/furtherdetailsschanis.pdf
    Description: Meta data documentation.

en:
Resource Title: Forest station data (SCB)
    Size: 10593777 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/f5242372-a0bd-42fa-a3be-038395f57134/download/scbforest.csv
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en:
Resource Title: Field station data (SCF)
    Size: 10669678 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/9bd7e72f-3b65-4250-9f24-f945c4476a0e/download/scffield.csv
    Description: Data file.

en:
Resource Title: Data plot of the forest station (SCB)
    Size: 253856 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/7a59ef22-0dea-4b48-b92e-8aaca534665c/download/scbplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Site photo of the field station (SCF)
    Size: 8615493 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/68696b24-8c46-48ad-8bb2-c15468b466ce/download/scf.jpg
    Description: Site photo of the field station from above (viewing downwards the slope).

en:
Resource Title: Site photo of the forest station (SCB)
    Size: 388600 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/00b2b4fa-a6b1-40b7-b1b7-f5c35c91b1a2/download/scb.jpg
    Description: Site photo of the forest station from below (viewing upwards the slope).

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par


data-broedlin-cnp

en: Mircocosm experiment to identify the individual patterns and controls of C, N, and P mobilization in soils under beech forests. Organic and mineral horizons sampled along a nutrient availability gradient in Germany were exposed to either permanent moist conditions or to dry spells in microcosms and quantified the release of inorganic and organic C, N, and P.

en: forest phosphorus stoichiometry drying-rewetting nitrogen dissolved-organic-matter soil carbon mineralization

envidat

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

en: Data Broedlin CNP

title_for_slug: data-broedlin-cnp

en:
Resource Title: Data Broedlin-CNP-Release
    Size: 1249259 bytes
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    URL: https://www.envidat.ch/dataset/9dd9a649-405d-413a-ad44-d859ea084bba/resource/2ec747a1-3a2b-4743-b89f-db13a372c49f/download/broedlin_et_al_cnp_rawdata_v2.xlsx
    Description: Raw data of C-N-P mobilization experiment from Oi, Oe/Oa, and A horizons of beech forest soils along a nutrient availability gradient.

format: XLS

en:
Resource Title: Data Broedlin-CNP
    Size: 1189635 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9dd9a649-405d-413a-ad44-d859ea084bba/resource/c4fce049-b79d-495f-a44d-94f1d59b913a/download/broedlin_cnp_rawdata.xlsx
    Description:

carbon

display_name: carbon

dissolved-organic-matter

display_name: dissolved-organic-matter

drying-rewetting

display_name: drying-rewetting

mineralization

display_name: mineralization

nitrogen

display_name: nitrogen

phosphorus

display_name: phosphorus

soil

display_name: soil

stoichiometry

display_name: stoichiometry


automatic-classification-of-avalanches

en: This dataset contains the classification and localization results obtained during the automatic classification of avalanches during the winter season 2017.

en: monitoring machine-learning seismic localization avalanche

envidat

en: EnviDat: the Environmental Data Portal

en: Automatic Classification of Avalanches

title_for_slug: automatic-classification-of-avalanches

envi

en: Environment

avalanche

display_name: avalanche

localization

display_name: localization

machine-learning

display_name: machine-learning

monitoring

display_name: monitoring

seismic

display_name: seismic


en:
Daily discharge and the related hydro-meteorological variables precipitation, snowmelt, and soil moisture are provided for current (1981-2017) and for future climate conditions (1981-2100) for 307 medium-sized catchments in Switzerland. The catchments have a median catchment area of 117 km². The 307 catchments together form a set representative of the climatological conditions and runoff characteristics in Switzerland. The four variables were simulated at a daily resolution using the hydrological model PREVAH. PREVAH is a conceptual process-based model that was run in this study in its fully distributed version on a 500 m grid (Viviroli et al. 2009a). For the calibration, runoff time series from 140 mesoscale catchments covering the different runoff regimes were used. The model calibration was conducted over the period 1993-1997. Verification was performed on the period 1983-2005 using (i) volumetric deviation (Viviroli et al. 2007) and (ii) benchmark efficiency (Schäfli et al 2007) as objective functions. The calibration and validation procedures are described in detail in Köplin et al. (2010). The parameters for each model grid cell were derived by regionalizing the parameters obtained for the 140 catchments with a procedure based on ordinary kriging (Viviroli et al. 2009b, Köplin et al. 2010).
The calibrated and validated model was then driven with transient meteorological data (precipitation, temperature, radiation, and wind) representing both reference (1981-2017) and future climate conditions (2018-2099). The data were derived from the CH2018 climate scenarios (NCCS 2018) provided by the Swiss National Centre for Climate Services (NCCS). They were obtained from climate experiments produced with different climate modeling chains, consisting of a global and a regional circulation model each, within EUROCORDEX  for three representative concentration pathways (RCP) emission scenarios. Downscaled output of ten climate model chains derived by quantile mapping were considered. The focus was on the chains of the EUR-11 domain with a horizontal resolution of 0.11 degrees (roughly 12.5 km). The climate model chains (GCM, RCM, RCP, and grid resolution) used are listed below:
- ICHEC-EC-EARTH	DMI-HIRHAM5	2.6	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	4.5	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	8.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	2.6	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	4.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	8.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	4.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	8.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	4.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	8.5	EUR-11
*References*:
-	Köplin, N., D. Viviroli, B. Schädler, and R. Weingartner (2010), _How does climate change affect mesoscale catchments in Switzerland? - A framework for a comprehensive assessment_, Advances in Geosciences, 27, 111-119, doi:10.5194/adgeo-27-111-2010.
-	National Centre for Climate Services (2018), CH2018 - _Climate Scenarios for Switzerland_, Tech. rep., NCCS, Zurich.
-	Schäfli, B., and H. V. Gupta (2007), _Do Nash values have value?_, Hydrological Processes, 21, 2075-2080, doi:10.1002/hyp.6825.
-	Viviroli, D., J. Gurtz, and M. Zappa (2007), _The hydrological modelling system PREVAH.  Part II - Physical model description_, Geographica Bernensia, 40, 1-89.
-	Viviroli, D., M. Zappa, J. Gurtz, and R. Weingartner (2009a), _An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools_, Environmental Modelling & Software, 24, 1209-1222, doi:10.1016/j.envsoft.2009.04.001.
-	Viviroli, D., H. Mittelbach, J. Gurtz, and R. Weingartner (2009b), _Continuous simulation  for flood estimation in ungauged mesoscale catchments of Switzerland-Part II: Parameter regionalisation and flood estimation results_, Journal of Hydrology, 377 (1), 208-225, doi:10.1016/j.jhydrol.2009.08.022.

en: hydrology soil-moisture precipitation climate-change discharge snowmelt

en: Simulated future discharge and climatological variables for medium-sized catchments in Switzerland

en:
Resource Title: Daily simulated data for the reference period
    Size: 31447784 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/6c1c0a28-a23f-4777-88b2-982841adb9b8/resource/a67b4308-25ca-47ea-9aed-f529b8dc7f4d/download/reference.zip
    Description: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the reference period 1981-2017 in mm.

en:
Resource Title: Shapefile of 307 catchments in Switzerland
    Size: 103266 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/6c1c0a28-a23f-4777-88b2-982841adb9b8/resource/05c1a038-40c2-423a-8ad9-f11b43689cf1/download/catchment_shapes.zip
    Description: Shapefile of the 307 medium-sized catchments in Switzerland. The "gridcode" provides the ID of the catchment used in the datafiles.

en:
Resource Title: Daily simulated data for the future period
    Size: 1010416415 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/6c1c0a28-a23f-4777-88b2-982841adb9b8/resource/be17bcca-3f34-414f-960e-bcbdd855ba81/download/climate_simulations.zip
    Description: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the period 1981-2100 in mm for the 307 catchments.

climate-change

display_name: climate-change

discharge

display_name: discharge

hydrology

display_name: hydrology

precipitation

display_name: precipitation

snowmelt

display_name: snowmelt

soil-moisture

display_name: soil-moisture


en: Data obtained in the free-air CO2 enrichment (FACE) experiment at Hofstetten, NW Switzerland, between 2009 and 2016. This dataset contains analyses of the soil solution throughout the experiment, especially for nitrate, as well as different analyses done at the end of the experiment: ammonium and nitrate captured by ion-exchange resin bags and extracted from soil cores, gross N mineralisation and nitrification measured by isotope dilution.

en: nitrogen soil forest face picea-abies co2

en: Nitrogen availability under trees exposed to CO2 enrichment (FACE)

en:
Resource Title: Resin bags
    Size: 1237 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/594288d3-95ff-4ad6-914e-d3e9c1d0ea42/download/resin-bags.csv
    Description: Ammonium, nitrate and their 15N in resin bags

en:
Resource Title: Collection points
    Size: 453 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/1e7dfaf0-d504-4bab-ae3b-8f96181b4e84/download/points.csv
    Description: Collection points under the trees

en:
Resource Title: Water analyses
    Size: 65553 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/50749920-cf85-4140-8802-54beffcf490e/download/water-analyses.csv
    Description: Analyses of water, especially soil solution

en:
Resource Title: Collection of soil solution
    Size: 33840 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/3cfe06e1-c280-4b18-903a-321ceef6044a/download/soil-solution.csv
    Description: Soil solution collected by suction cups

en:
Resource Title: Soil C and N
    Size: 1298 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/dc99385c-0ab8-4c7f-88cd-cf53ba0aea41/download/soil-c-n.csv
    Description: Analyses of C, N and their stable isotopes in the soil, and soil pH

en:
Resource Title: Gross N mineralisation and nitrification
    Size: 1183 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/f6a9e85f-187b-4a55-9f57-e243d14906ab/download/mineralisation-nitrification.csv
    Description: Gross rates of N mineralisation and nitrification (15N dilution)

face

display_name: face

forest

display_name: forest

picea-abies

display_name: picea-abies


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

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

en:
Resource Title: Precipitation
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/0f551cfd-804d-4f3a-80d9-c33562ea8b45/download/precipitation.txt
    Description: Simulated precipitation for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

format: TXT

media_type: text/plain

en:
Resource Title: Soil moisture
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/0ad6127b-f7d6-4356-9ac4-283166385578/download/soil_moisture.txt
    Description: Simulated plant-available soil moisture for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

en:
Resource Title: Discharge
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/4c11fec4-0846-48a0-8dfe-9f7f5a4b574f/download/discharge.txt
    Description: Simulated discharge for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

en:
Resource Title: Low-flow storage
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/a1338c95-09df-4bce-8d9c-7b059ae2f6a1/download/low_flow.txt
    Description: Simulated low-flow storage for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

en:
Resource Title: Shapefile of 307 catchments in Switzerland
    Size: 103266 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/7ebed8a1-98ce-4833-b1dd-64246bd07e5f/download/catchment_shapes.zip
    Description: Shapefile of 307 catchments for which simulations of precipitation, discharge, soil moisture, and low flow are provided. The OBJECTID entry corresponds to the data columns in the textfiles of the simulated data provided.

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

prevah

display_name: prevah


en:
The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)).
 Manuscript Abstract:
Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.

en: snow mars model sediment-transport erosion

en: Energy- and momentum-conserving model of splash entrainment in sand and snow saltation

envi

en: Environment

en:
Resource Title:
    Size: 3587 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/1f30b883-fc62-4bbf-a8f5-88f716008097/download/figure2.txt
    Description: Datasets of Figure 2

en:
Resource Title:
    Size: 1434 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/8ab9bafb-62f5-4420-80da-a2a96d22be74/download/figure3a.txt
    Description: Datasets of Figure 3a

en:
Resource Title:
    Size: 2190 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/82479779-1b20-486f-b4bc-414c6024c25d/download/figure3b.txt
    Description: Datasets of Figure 3b

en:
Resource Title:
    Size: 2904 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/709f4330-9a35-4685-aac8-347067a5e483/download/figure4b.txt
    Description: Datasets of Figure 4b

en:
Resource Title:
    Size: 3626 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/6ee21796-08ad-4069-a4cb-da6c35ffede7/download/figure4a.txt
    Description: Datasets of Figure 4a

erosion

display_name: erosion

mars

display_name: mars

model

display_name: model

sediment-transport

display_name: sediment-transport

snow

display_name: snow


snow-depth-mapping

en:
The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData).
For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the
state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground - 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths - 15m and smaller than - -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth.
The work is published in:
Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015

en: image-matching snow-water-equivalent aerial-imagery dsm avalanche snow-hydrology snow-depth

envidat

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

en: Snow Depth Mapping

title_for_slug: snow-depth-mapping

en:
Resource Title: Snow depth map 20160126
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/9229984f-b417-45e5-8dbe-031f295d0406/download/snowmap_20160126_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m
Because of very diffuse light conditions at time of image acquisition the quality of the resulting snow depth values are in some regions quite poor.

en:
Resource Title: Snow depth map 20150415
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/578e95ce-ffa0-4fc6-8109-d9653b2c622a/download/snowmap_2015_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

en:
Resource Title: Snow depth map 20100416
    Size: 412324171 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/4ba96052-9e3e-4b7a-9968-2dcc58fc96c0/download/snowmap_2010_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en:
Resource Title: Snow depth map 20140417
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/5cf5d3a5-555d-429e-9819-6c93ccce6dfc/download/snowmap_2014_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.18 m

en:
Resource Title: Snow depth map 20160420
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/bc035833-3193-4394-af92-f8d195d5e034/download/snowmap_20160420_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

en:
Resource Title: Snow depth map 20120320
    Size: 412272526 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/aff41055-8e6e-4287-b52d-2c2fb61633c2/download/snowmap_2012_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en:
Resource Title: Snow depth map 20160309
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/ceae68a6-28f9-43f4-8ff5-9715588ade57/download/snowmap_20160309_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

en:
Resource Title: Snow depth map 20130415
    Size: 412272526 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/da3ca703-c629-45c0-8dac-e78645b0fa5e/download/snowmap_2013_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en:
Resource Title: Snow depth mapping in high-alpine catchments using digital photogrammetry
    Size: 8122296 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/3b711e29-cea1-48e8-8de6-a008850c4f94/download/snow-depth-mapping-in-high-alpine-catchments.pdf
    Description:

aerial-imagery

display_name: aerial-imagery

avalanche

display_name: avalanche

dsm

display_name: dsm

image-matching

display_name: image-matching

snow-depth

display_name: snow-depth

snow-hydrology

display_name: snow-hydrology

snow-water-equivalent

display_name: snow-water-equivalent


en:
Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap.
In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.

en: fagus-orientalis-lipsky magnesium biogeochemical-cycling calcium potassium

envidat

en: EnviDat: the Environmental Data Portal

en: Base cation dynamics in an Oriental beech forest

envi

en: Environment

biogeochemical-cycling

display_name: biogeochemical-cycling

calcium

display_name: calcium

fagus-orientalis-lipsky

display_name: fagus-orientalis-lipsky

magnesium

display_name: magnesium

potassium

display_name: potassium


seilaplan

en:
Cable-based technologies have been a backbone for harvesting on steep slopes. The layout of a single cable road is challenging because one must identify intermediate support locations and heights that guarantee structural safety and operational efficiency while minimizing set-up and dismantling costs. Seilaplan optimizes the layout of a cable road by
Seilaplan stands for Cable Road Layout Planner. Seilaplan is able to calculate the optimal rope line layout (position and height of the supports) between defined start and end coordinates on the basis of a digital elevation model (DEM).
The program is designed for Central European conditions and is designed on the basis of a fixed suspension rope anchored at both ends. For the calculation of the properties of the load path curve an iterative method is used, which was described by Zweifel (1960) and was developed especially for standing skylines. When testing the feasibility of the cable line, care is taken that 1) the maximum permissible stresses in the skyline are not exceeded, 2) there is a minimum distance between the load path and the ground and 3) when using a gravitational system, there is a minimum inclination in the load path. The solution is selected which has a minimum number of supports in the first priority and minimizes the support height in the second priority.
The newly developed method calculates the load path curve and the forces occurring in it more accurately than tools available on the market to date (status 2019) and is able to determine the optimum position and height of the intermediate supports.
The reason for the more accurate results of the new tool is the assumption that the skyline is anchored at both end points. Forest cable yarders used in Europe have a skyline that is fixed at both ends. The behaviour of fixed-anchored suspension ropes is very difficult to describe mathematically and cannot be solved analytically. For this reason, simplified linearized assumptions have so far been used in the forestry sector, which corresponds to the behaviour of a weight-tensioned suspension rope and is known as the Pestal method (1961). Weight-tensioned suspension ropes are used for passenger transport.
For the calculation of the load path curve we use an iterative method, which was described by Zweifel (1960) and developed especially for fixed anchored suspension ropes. This makes mathematics much more demanding, but leads to more accurate and realistic results. Since there are no current models which describe the installation costs with adequate accuracy, the solution sought is the one which has a minimum number of supports in the first priority and minimises the support height in the second priority (Figure 2). The presented method is the first one, which starts from a fixed anchored supporting rope and identifies the mathematically optimal column layout at the same time. In contrast to methods that assume a weight-tensioned suspension rope, this approach achieves more realistic solutions with longer spans and lower support heights, which ultimately leads to lower installation costs. Background information on rope mechanics and calculation methods is documented in Bont and Heinimann (2012).
License: GNU, General Public License, Version 2 or newer.
Literature:
Bont, L., & Heinimann, H. R. (2012). Optimum geometric layout of a single cable road. European journal of forest research, 131(5), 1439-1448.

en: cable-yarding steep-terrain-harvesting cable-planning qgis python catenary-load-path software

en: Seilaplan

title_for_slug: seilaplan

cable-planning

display_name: cable-planning

cable-yarding

display_name: cable-yarding

catenary-load-path

display_name: catenary-load-path

python

display_name: python

qgis

display_name: qgis

software

display_name: software

steep-terrain-harvesting

display_name: steep-terrain-harvesting


en:
The dataset comprises the time series of overland flow (OF) and topsoil interflow (TIF) discharges from 14 small runoff plots (1 x 3 m) over 27 rainfall events in 2022 across the Studibach in the Alptal, a typical pre-Alpine catchment in Switzerland.
The runoff plots were installed on different topographic indices and vegetation covers in order to study the spatio-temporal variability of OF and TIF.
The runoff was collected into self-made "Upwelling Bernoulli Tubes" and the water level inside these tubes was measured by pressure sensors (DCX-22-CTD, Keller Druck, Switzerland) at a 5-min resolution. The discharge was derived from the water level.
The precipitation was measured with a tipping bucket at the Erlenhöhe meteorological station and has a 10-min resolution.

en: runoff studibach land topsoil-interflow rainfall overland-flow time-series alptal plot

envidat

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

en: Overland and topsoil interflow time series from the Studibach - summer 2022

alptal

display_name: alptal

land

display_name: land

overland-flow

display_name: overland-flow

plot

display_name: plot

rainfall

display_name: rainfall

runoff

display_name: runoff

studibach

display_name: studibach

time-series

display_name: time-series

topsoil-interflow

display_name: topsoil-interflow


en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. The plugin ‘Swiss Geo Downloader’, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model ‘swissALTI3D’ with the help of the ‘Swiss Geo Downloader’ and how to use it for digital planning of a cable line with the plugin ‘Seilaplan’. Please note that the tutorial language is German!
Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader
Link to Seilaplan website: https://seilaplan.wsl.ch
*********************
Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Das Plugin Swiss Geo Downloader, welches für das Open Source Geoinformationssystem QGIS zur Verfügung steht, ermöglicht frei verfügbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgenössischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue Höhenmodell swissALTI3D herunterladen und für die Seillinienplanung mit dem Plugin Seilaplan verwenden kann.
Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader
Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch

en: cable-yarding swiss-geo-downloader seilaplan digital-elevation-model swiss-geodata geoinformation-system open-source digital-planning qgis-plugin

en: Seilaplan Tutorial: DTM download with SwissGeoDownloader

en:
Resource Title: View video on youtube!
    Size: 1024 bytes
    Format: No Info
    URL: https://youtu.be/u7WRrO3GplI
    Description:

en:
Resource Title: Herunterladen des DHM mit SwissGeoDownloader
    Size: 429428137 bytes
    Format: video/mp4
    URL: https://www.envidat.ch/dataset/659dc637-e0ca-4393-9f07-13b04290d6ec/resource/05723528-b1c9-4476-a69b-18327d823588/download/seilaplan_tutorial_1_swissgeodownloader.mp4
    Description:

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geo-downloader

display_name: swiss-geo-downloader

swiss-geodata

display_name: swiss-geodata


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.

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

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

en:
Resource Title: LES-LSM_code
    Size: 226304 bytes
    Format: application/zip
    URL: https://gitlabext.wsl.ch/atmospheric-models/les-lsm/-/archive/master/les-lsm-master.zip
    Description: Source code of the large-eddy simulations coupled with a Lagrangian stochastic model (LES-LSM), available on GitLab.

en:
Resource Title: Simulation data for a situation with significant snow transport
    Size: 3687791 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0d5870d2-19c1-42c0-b6c9-46a3db93a328/resource/a1da4a3a-f9d6-4445-a542-8f09bc5e03ca/download/les-lsm_drift.zip
    Description: 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:
Resource Title: Measurement data from snow particle counter at MRR site
    Size: 64061921 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0d5870d2-19c1-42c0-b6c9-46a3db93a328/resource/ea337030-1803-40d7-b7b8-afd5d58a2395/download/spc-at-mrr-site.zip
    Description: Near-surface observations of drifting snow from a Snow Particle Counter (SPC) installed at the location of the Micro Rain Radar.

en:
Resource Title: Simulation data for a situation with negligible snow transport
    Size: 1991241 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0d5870d2-19c1-42c0-b6c9-46a3db93a328/resource/bb6b912f-cc5c-4c21-81c2-e493004bf916/download/les-lsm_nodrift.zip
    Description: Results from two LES-LSM simulations called NoDrift_1 and NoDrift_2, which reproduce the conditions observed at the S17 site on 12th January 2019, 05:40 to 05:50 UTC.

en:
Resource Title: Readme
    Size: 33528 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0d5870d2-19c1-42c0-b6c9-46a3db93a328/resource/3da99719-199e-4db7-84bc-dcef72df1c14/download/readme.txt
    Description: Meta data

en:
Resource Title: Measurement data from Micro Rain Radar (MRR)
    Size: 36829344563 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.237/MRR/MRR.zip
    Description: Remote sensing data of blowing snow from a Micro Rain Radar (MRR).

en:
Resource Title: Measurement data from micro-meteorological station
    Size: 402248169 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0d5870d2-19c1-42c0-b6c9-46a3db93a328/resource/94aad4d7-ade0-4e12-90bd-54a37db679bb/download/micro-met-station.zip
    Description: Automatic in-situ measurements of micro-meteorology and snow transport

antarctica

display_name: antarctica

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


individual-detected-trees

en: This dataset provides individual detected trees for selected Swiss Cantons (Appenzell Outer-Rhodes - AR, Grisons - GR, Nidwald - NW, Obwald - OW and St. Gall - SG), including the derived diameter at breast height and the derived NFI development stage. Data show the Individual detected trees (ITD) with the x- and y-positions of each detected tree based on a spike-free vegetation height model based on nationwide LiDAR data. The shapefile contains the following attributes: tree height [m] (Height), estimated diameter at breast height [cm] (BHD), and the NFI development stage (Ent; 1: young growth/thicket (- 12 cm); 2: pole timber (12-30 cm); 3: young timber (31-40 cm); 4: medium timber (41-50 cm); 5: old timber (- 50 cm).

en: development-stage nfi mountain-forest forest-structure forest-management itd

envidat

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

en: EnviDat: the Environmental Data Portal

en: Individual Detected Trees

title_for_slug: individual-detected-trees

envi

en: Environment

development-stage

display_name: development-stage

forest-management

display_name: forest-management

forest-structure

display_name: forest-structure

itd

display_name: itd

mountain-forest

display_name: mountain-forest

nfi

display_name: nfi


en:
Important
This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry "Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland." The entry can be found under this link -https://www.envidat.ch/dataset/afforestation-stillberg-  and with this DOI 10.16904/envidat.397.

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

en: Long-term treeline research dataset at Stillberg, Davos

en:
Resource Title: Dataset V1
    Size: 18459904 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b72cc62e-cf31-4f4c-b89e-eba90f27ac50/resource/f488643a-8f64-4427-8b08-a947c47a1302/download/stillberggis.zip
    Description: This dataset is outdated and has been replaced with an updated version (see description).

en:
Resource Title: Site photo
    Size: 32941 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/b72cc62e-cf31-4f4c-b89e-eba90f27ac50/resource/ca3ffbc8-b692-4f9c-ab49-f1c09fdb4e69/download/stillpic.jpeg
    Description: This site photo is outdated and has been replaced with an updated version (see description).

en:
Resource Title: Documentation
    Size: 57344 bytes
    Format: application/msword
    URL: https://www.envidat.ch/dataset/b72cc62e-cf31-4f4c-b89e-eba90f27ac50/resource/545c2ff6-8fdb-4ea0-85a8-49f49d877022/download/documentationstillberg1520160325.doc
    Description: This data documentation is outdated and has been replaced with an updated version (see description).

format: msword

afforestation

display_name: afforestation

long-term-experiment

display_name: long-term-experiment

stillberg

display_name: stillberg

treeline

display_name: treeline


en:
This dataset provides 50 years of seasonal synthetic hourly time series for five meteorological variables: incoming shortwave radiation, total precipitation, relative humidity, 2-meter air temperature and wind speed.
The data are generated from observational records (1990–present) from nine MeteoSwiss stations (ABO, BAS, BER, LUG, PUY, SCU, SIO, STG, WFJ). We use a sampling approach that preserves both the temporal structure and inter-variable dependencies. Specifically, the time series are constructed by drawing 7-day blocks from historical observations, ensuring realistic short-term variability and maintaining the physical coherence between variables.
The synthetic sequences are constrained to follow daily temperature change trajectories corresponding to three future climate scenarios for the period 2040–2070: RCP2.6, RCP8.5 and RCP0: a counterfactual scenario with no long-term warming trend, but with temperature variability consistent with the RCP2.6 one.
The temperature constraints are derived from projections downscaled at the staiton level, available at: https://www.envidat.ch//metadata/climate-change-scenarios-at-hourly-resolution

en: speed2zero time-series precipitation shortwave-radiation climate-change air-temperature relative-humidity meteo-station wind-speed

en: Meteorological Time Series Aligned with Projected Temperature Changes

air-temperature

display_name: air-temperature

climate-change

display_name: climate-change

meteo-station

display_name: meteo-station

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

shortwave-radiation

display_name: shortwave-radiation

time-series

display_name: time-series

wind-speed

display_name: wind-speed


en:
Assessment of damges, i.e. symptoms, extent and causes on trees.
 Purpose:
Occurrence and extent of diseases
 Manual Citation:
 * Matthias Dobbertin, Christian Hug, Andreas Schwyzer, Serge Borer, Hanna Schmalz, 2016: Aufnahmeanleitung Kronenansprachen auf den Sanasilva- und den LWF-Flächen (Version 10). Sanasilva Inventur und Langfristige Waldökosystem-Forschung, Birmensdorf, 86 S. [---](https://www.wsl.ch/fileadmin/user_upload/WSL/Wald/Waldentwicklung_Monitoring/LWF/Sanasilva/ssi_anleitung_v10_extern.pdf)
 *  Eichhorn  J,  Roskams  P,  Potocic  N,  Timmermann  V,  Ferretti  M,  Mues  V,  Szepesi  A,  Durrant  D,  Seletkovic   I,   Schröck   H-W,   Nevalainen   S,   Bussotti   F,   Garcia   P,   Wulff   S,   2016:   Part   IV:   Visual   Assessment  of  Crown  Condition  and  Damaging Agents.  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, 49 p. + Annex [---](https://www.icp-forests.org/pdf/manual/2016/ICP_Manual_2017_02_part04.pdf )
 Paper Citation:
 * Michel A, Seidling W, Prescher A K (2018) Forest Condition in Europe. 2018 Technical Report of ICP Forests. Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). BFW-Dokumentation 25/2018, BFW Austrian Research Centre for Forests, Vienna, 92 p. [---](http://icp-forests.net/page/icp-forests-technical-report)
 *  Köhl M, San-Miguel-Ayanz J, Cools N, de Vos B, Fischer R, Camia A, Granke O, Hiederer R, Lorenz M, Montanarella L, Mues V, Nagel H-D, Poker J, Scheuschner T, Schlutow A (2011) Maintenance of Forest Ecosystem Health and Vitality. State of Europe s forests: status and trends in sustainable forest management in Europe 29-49. [---](http://www.foresteurope.org/documentos/State_of_Europes_Forests_2011_Report_Revised_November_2011.pdf)
 *  Brang P., 1998: Sanasilva-Bericht 1997. Zustand und Gefährdung des Schweizer Waldes – eine Zwischenbilanz nach 15 Jahren Waldschadenforschung. Berichte der Eidg. Forschungsanstalt für Wald, Schnee und Landschaft, Vol. 345. Eidg. Forschungsanstalt WSL, Birmensdorf, 102 S. [---](https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14555)

en: forest-vitality swiss-forest-lab forest-condition icp-forests long-term-forest-ecosystem-research damage-cause-assessment

en: Crown Condition Assessment and Damage Cause Assessment LWF

format: HTML

damage-cause-assessment

display_name: damage-cause-assessment

forest-condition

display_name: forest-condition

forest-vitality

display_name: forest-vitality

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

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

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

en:
Resource Title: StemBranchwood_Didionetal_revised_for publication.pdf
    Size: 306424 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/b23a6bfa-3a55-4677-91e2-d32f91945574/resource/c8499517-f803-45e6-a7f2-7cde8e6afd3c/download/stembranchwood_didionetal_revised_for-publication.pdf
    Description: Revised version of the initial submission StemBranchwood_Didionetal_initialsubmission.pdf after review.

en:
Resource Title: Background.pdf
    Size: 132521 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/b23a6bfa-3a55-4677-91e2-d32f91945574/resource/9006c9b7-0dc0-46f9-8b24-8aadef0ca654/download/background.pdf
    Description: additional background information on this work

en:
Resource Title: Stem&Branchchwood_2024-04-25.txt
    Size: 8211522 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/b23a6bfa-3a55-4677-91e2-d32f91945574/resource/8ba7cb02-3d90-409f-9113-4e9aa5e7d33c/download/stembranchchwood_2024-04-25.txt
    Description: Updated dataset of outdated_Stem&Branchchwood_2024-02-29.txt, including corrections to the assignment of tree species to NFI main species and location information for two additional sites.

en:
Resource Title: outdated_Stem&Branchchwood_2024-02-29.txt
    Size: 8114251 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/b23a6bfa-3a55-4677-91e2-d32f91945574/resource/cb7d58c5-e952-4a4e-af24-b590d7a25d87/download/stembranchchwood_2024-02-29.txt
    Description: dataset with section-wise stem diameter and branchwood volume measurements. For column definitions see Column_definitions.pdf.

en:
Resource Title: StemBranchwood_Didionetal_initialsubmission.pdf
    Size: 1174376 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/b23a6bfa-3a55-4677-91e2-d32f91945574/resource/fbe9e951-31f7-43d3-ba1c-f8176380ee3f/download/stembranchwood_didionetal_forpublication.pdf
    Description: manuscript submitted for publication.

en:
Resource Title: Column_definitions.pdf
    Size: 73134 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/b23a6bfa-3a55-4677-91e2-d32f91945574/resource/6ac26fd6-4c7b-47d3-86ee-aaa59ab7f5d0/download/column_definitions.pdf
    Description: definitions of data in columns contained in the dataset Stem&Branchchwood_2024-02-29.txt

allometry

display_name: allometry

branchwood

display_name: branchwood

growing-stock

display_name: growing-stock

section-wise-diameter

display_name: section-wise-diameter

stemwood

display_name: stemwood

tree-volume

display_name: tree-volume


tree-species-map-of-switzerland

en:
Dominant tree species map of Switzerland
We created a tree species map of Switzerland for the dominant tree species in the forested areas. The spatial resolution of the map is 10 m and the coordinate system is ETRS89-extended / LAEA Europe (EPSG 3035). The map comprises Sentinel-2 index time series from the year 2020, a digital elevation model and species reference data from the Swiss National Forest Inventory. The map is available as raster (.tif) or vector dataset (.gpkg).
**Access will be granted upon request.**
In total, the following 15 species were mapped:
*Abies alba*, *Acer pseudoplatanus*, *Alnus glutinosa*, *Alnus incana*, *Betula pendula*, *Castanea sativa*, *Fagus sylvatica*, *Fraxinus excelsior*, *Picea abies*, *Pinus cembra*, *Pinus mugo arborea*, *Pinus sylvestris*, *Quercus petraea*, *Quercus robur*, *Sorbus aucuparia*.
-br/--br/-
 Approach
-br/--br/-
 Data
- Swiss National Forest Inventory Data (stand species with - 60 % dominance in upper canopy; on at least more than 9 plots dominant)
- Sentinel-2 time series (2020, Indices: CCI, CIRE, NDMI, EVI, NDVI)
- Digital elevation model (DEM) (swissalti3d, 5 m)
- Biogeographical regions (Federal Office for the Environment FOEN)
- Forest mask 2017 (Approach: Waser et al., 2015)
-br/--br/-
 Modeling approach
We identified the most meaningful variables that led to separation of the respective groups by using random forest models with a forward feature selection (Meyer et al., 2018; Ververidis & Kotropoulos, 2005). In this approach, the final random forest model is solely built from the selected meaningful variables. By identifying meaningful variables, we can determine which variables might influence the grouping. Further, to avoid overfitting and overly optimistic results, we applied 10-fold spatial cross-validation and put all pixels from a plot in the same spatial fold.
The modeling was realized using the CAST package in R (Meyer et al., 2022), based on the well-known caret package (Kuhn, 2022). We used the ranger package in R (Wright & Ziegler, 2017) to implement the random forest models, due to its short computation time.
-br/--br/-
 Training data for modeling
- 295 Sentinel-2, DEM & Biogeographical variables
- 10525 tree species pixels
-br/--br/-
 Selected variables for final model
1. EVI of 2020.05.16
2. NDMI of 2020.03.12
3. CIRE of 2020.04.16
4. NDMI of 2020.07.05
5. CCI of 2020.05.11
6. dem
7. CCI of 2020.08.14
8. NDMI of 2020.08.24
9. CCI of 2020.12.22
10. NDMI of 2020.04.21
11. NDMI of 2020.11.17
12. NDMI of 2020.08.09
13. CIRE of 2020.03.22
14. CIRE of 2020.08.09
14. CCI of 2020.11.02
15. CIRE of 2020.06.10
-br/--br/-
 Overall Accuracy of final model
- 0.759
-br/--br/-
 Nationwide prediction
- Predicted throughout forest mask 2017 (Approach: Waser et al., 2015)
- Not applied on incomplete Sentinel-2 time series (own category in final map: incomplete_ts)
- Applied the Area of Applicability (Meyer 2022) to sort out pixels outside of the feature space; basically where the model had not the same values for pixels as in the available training data
-br/--br/-
-br/--br/-
 *Be aware that the map is only validated with the training data itself, an independent validation with other data sources remains missing*
-br/--br/-
-br/--br/-
 References
- Kuhn, M. (2022). Classification and Regression Training.  6.0-93.
- Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., & Nauss, T. (2018). *Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation*. Environmental Modelling and Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001
- Meyer, H., Milà, C., & Ludwig, M. (2022). *CAST: 'caret' Applications for Spatial-Temporal Models*.  0.7.0.
- Ververidis, D., & Kotropoulos, C. (2005). *Sequential forward feature selection with low computational cost*. 2005 13th European Signal Processing Conference.
- Waser, L., Fischer, C.,Wang, Z., & Ginzler, C. (2015). *Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition*. Forests, 6, 12, 4510–4528.
- Wright, M. N., & Ziegler, A. (2017). *ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R*. Journal of Statistical Software, 77(1), 1-17. https://doi.org/doi:10.18637/jss.v077.i01

en: map switzerland remote-sensing forest tree-species

envidat

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

en: EnviDat: the Environmental Data Portal

en: Tree species map of Switzerland

title_for_slug: tree-species-map-of-switzerland

envi

en: Environment

forest

display_name: forest

map

display_name: map

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

tree-species

display_name: tree-species


photogrammetric-drone-data-dorfberg

en: The data was collected with a Wingtra Gen II drone and a Sony RX1R II sensor. In total, 10 flights were conducted at different dates, both in summer and winter. A DSM, an orthophoto, a snow depth raster and the original drone images from every flight are available at a high resolution (10cm and 3cm, respectively).

en: uav snow snow-distribution photogrammetry drone-data dsm orthophoto snow-depth-mapping

en: Photogrammetric Drone Data Dorfberg

title_for_slug: photogrammetric-drone-data-dorfberg

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow

display_name: snow

snow-depth-mapping

display_name: snow-depth-mapping

snow-distribution

display_name: snow-distribution

uav

display_name: uav


en:
This study examines the siting scenarios for renewable energy infrastructure (REI) in Switzerland, incorporating the external costs of ecosystem services and, innovatively, social preferences. This approach challenges the prevalent techno-economic siting paradigm, which often overlooks these externalities. To minimize the external costs of the scenarios while maximizing energy yield, Marxan, an optimization software, was employed.
MARXAN was run for 2 versions: a) without ground-mounted open space PV infrastructure (excl. OS) and b) with ground-mounted open space PV infrastructure (incl. OS). In each version optimization was done using ecological costs (ECUess) or social costs (ECUsoc) in a regular grid of 4x4km (planning unit).
File:
PU_data.shp: compressed Shapefile (ESRI) from the MARXAN optimization with 2216 rows (objects) and 18 columns (variables) with sf (simple feature) and data frame classes.
Headers are described below:
X01 = Planning_units (PUs)
X02 = incl.OS. number of times pu was selected in MARXAN  when optimized for ecological costs
X03 = incl.OS. number of times pu was selected in MARXAN when optimized for social  costs
X04 = incl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X05 = incl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X06 = incl.OS. total energy (MWh/a)
X07 = incl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X08 = incl.OS. energy from wind turbines (MWh/a)
X09 = incl.OS. energy from ground-mounted PV infrastructure (MWh/a)

X10 = excl.OS. number of times pu was selected in MARXAN  when optimized for ecological costs
X11 = excl.OS. number of times pu was selected in MARXAN when optimized for social  costs
X12 = excl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X13 = excl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X14 = excl.OS. total energy (MWh/a)
X15 = excl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X16 = excl.OS. energy from wind turbines (MWh/a)
X17 = excl.OS. energy from ground-mounted PV infrastructure (MWh/a)
geometry = Simple feature XY geometry (SFC_POINT) each representing the center of a 4x4km planning unit (PU) in EPSG 21781 (CRS CH1903 / LV03).
For MARXAN optimizations (for meaning of standard files in MARXAN see https://scholar.google.ch/scholar_url?url=https://courses.washington.edu/cfr590/software/Marxan1810/marxan_manual_1_8_2.pdf&hl=de&sa=X&ei=NAWHZI6JJ8PFmAGMo47oDQ&scisig=AGlGAw-DBrsV4kUzkR6GkN1jtG66&oi=scholarr), pu_data.shp was used to generate MARXAN files input.dat, pu.dat, puvfeat.dat, spec.dat.
Coordinate Reference System: CH1903 / LV03. EPSG: 21781.

en: optimization landscape photovoltaic social-acceptance marxan spatial-planning wind-power energy ecosystem-services

en: Optimizing renewable energy siting in the Swiss landscape

ecosystem-services

display_name: ecosystem-services

energy

display_name: energy

landscape

display_name: landscape

marxan

display_name: marxan

optimization

display_name: optimization

photovoltaic

display_name: photovoltaic

social-acceptance

display_name: social-acceptance

spatial-planning

display_name: spatial-planning

wind-power

display_name: wind-power


high-resolution-climate-data-for-europe

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.

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

en: High resolution climate data for Europe

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

en:
Resource Title: CHELSA_EUR11 technical documentation
    Size: 713475 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/9e2c4a69-443d-4eb1-b8ab-697fd50bc7d2/resource/bc1df26a-0b8f-4547-872d-f00ed45db554/download/chelsa_eur11_technical_documentation.pdf
    Description: Technical documentation of the data with variable names and methodology.

en:
Resource Title: Data Access
    Size: 2199023255552 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsav2&prefix=EUR11%2F
    Description:

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


soil-property-maps-for-the-swiss-forest

en:
We used 2071 forest soil profiles to model a wide range of soil properties for the forested area of Switzerland. The spatial prediction is based on the principle of «digital soil mapping». This involves linking soil profiles with soil forming factors using statistical or machine learning methods.
A quantile regression forest (QRF) approach was applied to predict the following soil properties at six depth ranges: clay, gravel, sand, fine earth density, SOC. The depth ranges correspond to the standard depths of the [GlobalSoilMap.Net](https://www.isric.org/) specification: 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm. In addition, the total soil depth down to a non-root-permeable layer or solid rock soil thick was predicted. To quantify the uncertainty for each predicted pixel, the upper and lower limit of the 90% prediction interval derived from QRF was calculated. More details on the methods and results are described in Baltensweiler et al. 2021 and Baltensweiler et al 2022.
The soil property maps, and the uncertainty maps are provided as a GeoTIFF files at 25 m resolution. The excel file (xlsx) provides a short description of the raster layers.
**The soil and the uncertainty maps can be viewed in a simple web-GIS application available at:**
[www.wsl.ch/soilmaps](https://www.wsl.ch/soilmaps).

en: soil-texture soil switzerland soil-depth soil-ph soc dsm forest soil-properties

en: Soil property maps for the Swiss forest

title_for_slug: soil-property-maps-for-the-swiss-forest

en:
Resource Title: gravel_uncertainty_maps.zip
    Size: 89436761 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/d7b44437-7fc2-4750-a322-e26887a830c6/download/gravel_uncertainty_maps.zip
    Description:

en:
Resource Title: pH_maps.zip
    Size: 129172696 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/639353b5-92a5-43d1-b0ec-4ce8ca7773c0/download/ph_maps.zip
    Description:

en:
Resource Title: pH_uncertainty_maps.zip
    Size: 192523204 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/c5bea000-de46-4b93-8de8-f246c6135b9f/download/ph_uncertainty_maps.zip
    Description:

en:
Resource Title: depth_map.zip
    Size: 37183065 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/7c58ee27-6410-41bc-b533-1c3842032878/download/depth_map.zip
    Description:

en:
Resource Title: depth_uncertainty_maps.zip
    Size: 69351779 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/b4dc7b7b-e800-45d4-8d03-997968710b56/download/depth_uncertainty_maps.zip
    Description:

en:
Resource Title: clay_uncertainty_maps.zip
    Size: 105083064 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/1059745c-d83e-4f31-b723-07cce9fb2104/download/clay_uncertainty_maps.zip
    Description:

en:
Resource Title: density_maps.zip
    Size: 172162688 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/d5a87cce-64aa-40db-aa02-589fb89c7984/download/density_maps.zip
    Description:

en:
Resource Title: soc_uncertainty_maps.zip
    Size: 222671274 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/a214d4d2-72bd-451d-8fd5-a461b3a78b28/download/soc_uncertainty_maps.zip
    Description:

en:
Resource Title: SoilMaps_description.xlsx
    Size: 11429 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/5f08172f-967c-4aa3-beaf-a81338290f9e/download/soilmaps_description.xlsx
    Description:

en:
Resource Title: density_uncertainty_maps.zip
    Size: 143926943 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/5886ee25-bfb6-44a2-97a9-00d5c968d1c9/download/density_uncertainty_maps.zip
    Description:

en:
Resource Title: gravel_maps.zip
    Size: 80316578 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/c799d7c4-ae83-402d-976d-f23ef7021ad7/download/gravel_maps.zip
    Description:

en:
Resource Title: clay_maps.zip
    Size: 55978125 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/1616b34f-542a-4622-9bdd-e454500393c8/download/clay_maps.zip
    Description:

en:
Resource Title: sand_uncertainty_maps.zip
    Size: 116700070 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/25bdf03f-226b-4274-8867-6553b2c12bd3/download/sand_uncertainty_maps.zip
    Description:

en:
Resource Title: sand_maps.zip
    Size: 65109556 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/1a2addd8-1b83-4502-b015-43face6c519b/download/sand_maps.zip
    Description:

en:
Resource Title: soc_maps.zip
    Size: 144898851 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/d80078ce-cf36-4590-8f01-e79de67f8b2f/download/soc_maps.zip
    Description:

dsm

display_name: dsm

forest

display_name: forest

soc

display_name: soc

soil

display_name: soil

soil-depth

display_name: soil-depth

soil-ph

display_name: soil-ph

soil-properties

display_name: soil-properties

soil-texture

display_name: soil-texture

switzerland

display_name: switzerland


en:
Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds.
All metadata can be found in the attached documents.

en: acclimation fluorescence photosynthesis drought vpd gas-exchange pinus-sylvestris

envidat

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

en: EnviDat: the Environmental Data Portal

en: 2013-2020 gas exchange at Pfynwald

envi

en: Environment

en:
Resource Title: Methods light response
    Size: 607 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/c5ef8bc5-79ab-4aec-9e02-d031e1e7d976/download/methodsmetadata_lightcurves_darkresp.csv
    Description: Methods and parameters for light response curves

en:
Resource Title: Methods diurnal measurements
    Size: 593 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/45f2d648-386c-42bb-b766-6f804e7c7de6/download/methodsmetadata_diurnal.csv
    Description: Parameters and methodology for diurnal measurements

Metadata format from
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en:
Resource Title: Instrument details
    Size: 1174 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/e7342279-bf44-451b-afa2-78cb77babf5e/download/instrumentdetails_pfynwald.csv
    Description: Instrument details for all LiCor instruments used during collection of dataset.

Metatdata created following guidelines from
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en:
Resource Title: Methods survey
    Size: 596 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/229fbf92-a0ff-44b5-b799-b55fabe2b99c/download/methodsmetadata_point.csv
    Description: Parameters and methods for point measurements / survey measurements

en:
Resource Title: Variable names and units
    Size: 15839 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/39614a56-a982-4123-a8d6-e9aaa51c838a/download/variable_names_units.xlsx
    Description: Variable names, description and units for the Pfynwald Gas exchange data

en:
Resource Title: Methods ACi curves
    Size: 731 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/c7536938-120e-4975-97e4-e40649637634/download/methodsmetadata_acicurves.csv
    Description: Parameters and methods for CO2 response curves of Photosynthesis.

Metatdata format by
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en:
Resource Title: Methods fluorescence
    Size: 534 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/1d2343e4-bdf9-4ff0-9582-0c9ef11e139e/download/methodsmetadata_fluoresence.csv
    Description: Parameters and methodology for Fluorescence measurements

Metadata format from
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en:
Resource Title: Pfynwald gas exchange
    Size: 4072706 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/3d0f2a0a-c365-4f15-a8e7-312943cbcad6/download/pfynwald_gasexchange_201306_202010.csv
    Description: Full dataset of Pfynwald gas exchange data taken in the period from June 2013 to October 2020.
All metdata can be found in attached documents.
Data uploaded after quality control using guidelines by Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

acclimation

display_name: acclimation

drought

display_name: drought

fluorescence

display_name: fluorescence

gas-exchange

display_name: gas-exchange

photosynthesis

display_name: photosynthesis

pinus-sylvestris

display_name: pinus-sylvestris

vpd

display_name: vpd


en: Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017.

en: elevation alps herbivores-communities plant-herbivore-interactions community-indices functional-traits environmental-gradients

en: Above- and below-ground herbivore communities along elevation

en:
Resource Title: Orthoptera surveys
    Size: 17366 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/2e5114d6-4dee-4dc9-8bf7-434425af15a7/resource/f877bfcd-df8c-4059-bdca-0176154eeb02/download/orthoptera_surveys.txt
    Description: Species inventories of Orthoptera

en:
Resource Title: Vegetion surveys
    Size: 115943 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/2e5114d6-4dee-4dc9-8bf7-434425af15a7/resource/a2f466e2-1eb8-4436-a0cb-8dd3387243bd/download/vegetation_surveys.txt
    Description: Species inventories of plants

en:
Resource Title: Nematode surveys
    Size: 13302 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/2e5114d6-4dee-4dc9-8bf7-434425af15a7/resource/b564230b-cfea-4587-84b7-06fe299995af/download/nematode_surveys.txt
    Description: Surveys of soil nematode along elevation

en:
Resource Title: Orthoptera traits
    Size: 3646 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/2e5114d6-4dee-4dc9-8bf7-434425af15a7/resource/083b639b-9a30-4d58-bb15-2e6965c72271/download/orthoptera_traits.txt
    Description: Orthoptera functional traits (incisive mandibular strength averaged by species)

en:
Resource Title: Plant traits
    Size: 25166 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/2e5114d6-4dee-4dc9-8bf7-434425af15a7/resource/6da02cea-6955-4d5d-b389-85e24cda8a93/download/plant_traits.txt
    Description: Plant functional traits (SLA, LDMC, carbon and nitrogen content, punch strenght; averaged by species)

en:
Resource Title: Study sites
    Size: 1120 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/2e5114d6-4dee-4dc9-8bf7-434425af15a7/resource/5f605109-b59f-40cd-aa3f-804623828d36/download/study_sites.txt
    Description: Coordinates and elevation of the study sites

alps

display_name: alps

community-indices

display_name: community-indices

elevation

display_name: elevation

environmental-gradients

display_name: environmental-gradients

functional-traits

display_name: functional-traits

herbivores-communities

display_name: herbivores-communities

plant-herbivore-interactions

display_name: plant-herbivore-interactions


en:
Since 2001 soil water content and soil water potential are measured in 40 plots of the *[Intercantonal Forest Observation Program](https://www.envidat.ch/dataset/interkantonale-walddauerbeobachtung-wdb)*. Soil water content and soil water potential is measured in hourly resolution in 3 depths according to the soil horizons.
The following parameters about soil water balance are measured:
 * Volumetric soil water content (SMT-100, Truebner)
 * Soil water potential (TEROS21, METER)
More information about the analytical methods and results can be found in *[Braun et al. (2021)](https://doi.org/10.5281/zenodo.5724756)*.

en: swiss-forest-lab long-term-monitoring soil-moisture-water-content wdb soil-water-potential long-term-forest-ecosystem-research

envidat

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

en: EnviDat: the Environmental Data Portal

en: Soil moisture and soil water potential measurements of the Intercantonal Forest Observation Program

envi

en: Environment

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

long-term-monitoring

display_name: long-term-monitoring

soil-moisture-water-content

display_name: soil-moisture-water-content

soil-water-potential

display_name: soil-water-potential

swiss-forest-lab

display_name: swiss-forest-lab

wdb

display_name: wdb