maintainer: Frank Techel

en: avalanche-fatalities long-term-trends avalanche-accident-statistics avalanche-accident avalanche-statistics

en: Data avalanche fatalities 1969/1970 - 2014/2015

en: The data comprises avalanche fatality data from the Alpine regions in the seven countries Austria, France, Germany, Italy, Liechtenstein, Slovenia, and Switzerland.

mimetype: text/plain

en:
During the last 45 years, about 100 people lost their lives in avalanches in the European Alps each year. Avalanche fatalities in settlements and on transportation corridors have considerably decreased since the 1970s. In contrast, the number of avalanche fatalities during recreational activities away from avalanche-secured terrain doubled between the 1960s and 1980s and has remained relatively stable since, despite a continuing strong increase in winter backcountry recreational activities.

Data complementing Figure 2 in:
_"Avalanche fatalities in the European Alps: long-term trends and statistics"_, by Techel, F., Jarry, F., Kronthaler, G., Mitterer, S., Nairz, P., Pavšek, M., Valt, M., and Darms, G.

Data description:
please refer to section 2 (Data and Methods) in the mentioned publication

avalanche-accident

display_name: avalanche-accident

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-fatalities

display_name: avalanche-fatalities

avalanche-statistics

display_name: avalanche-statistics

long-term-trends

display_name: long-term-trends

educ

en: Education, culture and sport

en: Avalanche fatalities in the European Alps (1969/1970 - 2014/2015)


total-stem-number-by-cause-of-damage

title_for_slug: total-stem-number-by-cause-of-damage

state: active

type: dataset

format: SERVICE

en:
Number of all living and dead trees and shrubs starting at 12 cm dbh where a particular cause of damage (including no damage, dead or lying) was determined. One tree may have damage with more than one type of origin, which means it may contribute to the total number of stems with damage with several different types of origin.

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

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

cause-of-damage-of-single-tree

display_name: cause-of-damage-of-single-tree

economic-region

display_name: economic-region

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

total-stem-number

display_name: total-stem-number

total-stem-number-by-cause-of-damage

display_name: total-stem-number-by-cause-of-damage

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Total stem number by cause of damage


en: switzerland river climate-change lake hydrology hydro-ch2018 climate-change-scenarios

en: This report presents past observations and projects the future development of water temperature in Swiss lakes and rivers. Projections are made until the end of the 21st century using the CH2018 climate scenarios. Besides climate change effects on temperature, we also discuss effects on discharge for rivers, and effects on the thermal structure, and specifically the seasonal mixing regime and ice cover of lakes.

climate-change

display_name: climate-change

climate-change-scenarios

display_name: climate-change-scenarios

hydrology

display_name: hydrology

lake

display_name: lake

river

display_name: river

switzerland

display_name: switzerland

en: Hydro-CH2018 Evolution of stream and lake water temperature under climate change


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

en: Data-variability-SPG-response

en: Data used in paper: Antoniazza, G., Nicollier, T., Wyss, C.R., Boss, S., Rickenmann, D., 2020. Bedload Transport Monitoring in Alpine Rivers: Variability in Swiss Plate Geophone Response. Sensors 2020, 20, 4089; doi:10.3390/s20154089

en: Hydrological drivers of bedload transport in Avançon de Nant

en: Data used in paper: Antoniazza, G., Nicollier, T., Boss, S., Mettra, F., Badoux, A., Schaefli, B., Rickenmann, D., Lane, S.N., 2022. Hydrological Drivers of Bedload Transport in an Alpine Watershed. Water Resources Research, 58, e2021WR030663. https://doi.org/10.1029/2021WR030663

media_type: application/zip

en: Data used in paper: Antoniazza, G., Dietze, M., Mancini, D., Turowski, J.M., Rickenmann, D., Nicollier, T., Boss, S., Lane, S.N. (2023). Anatomy of an Alpine bedload transport event: A watershed-scale seismic-network perspective. Journal of Geophysical Research: Earth Surface, 128, e2022JF007000. https://doi.org/10.1029/2022JF007000

format: ZIP

en: Data used in paper: Rickenmann, D., Ammann, L., Nicollier, T., Boss, S., Fritschi, B., Antoniazza, G., Steeb, N., Chen, Z., Wyss, C., Badoux, A. (2022). Comparison of calibration characteristics of different acoustic impact systems for measuring bedload transport in mountain streams. Earth Surface Dynamics Discussions, https://doi.org/10.5194/esurf-2022-7

en: SPG field calibration data

en:
SPG field calibration data from the Erlenbach (since 2009), the Albula (2018), the Navisence (2019) and the Avançon de Nant (2019-2020) monitoring stations. Data used in following papers:
"Toward a General Calibration of the Swiss Plate Geophone System for Fractional Bedload Transport" submitted to Earth Surface Dynamics on the 14th of January 2022.
"Improving the Calibration of Impact Plate Bedload Monitoring Systems by Filtering Out Acoustic Signals from Extraneous Particle Impacts" submitted to Earth and Space Science on the 13th of August 2021.

format: XLS

en: Data used in paper: Rickenmann, D., 2020. Effect of Sediment Supply on Cyclic Fluctuations of the Disequilibrium Ratio and Threshold Transport Discharge, Inferred from Bedload Transport Measurements over 27 Years at the Swiss Erlenbach stream. Water Resources Research, 56, e2020WR027741. https://doi.org/10.1029/2020WR027741

en: SPG flume experiment data

en:
SPG data from controlled flume experiments conducted at the Obernach outdoor flume facility (TU Munich) between 2018 and 2020. Data used in following papers:
"Toward a General Calibration of the Swiss Plate Geophone System for Fractional Bedload Transport" submitted to Earth Surface Dynamics on the 14th of January 2022.
"Improving the Calibration of Impact Plate Bedload Monitoring Systems by Filtering Out Acoustic Signals from Extraneous Particle Impacts" submitted to Earth and Space Science on the 13th of August 2021.

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

bedload-transport

display_name: bedload-transport

mountain-stream

display_name: mountain-stream

sediment-transport

display_name: sediment-transport

surrogate-measurement

display_name: surrogate-measurement

torrent

display_name: torrent

en: Sediment transport observations in Swiss mountain streams


lwf-lageren-long-term-research-site

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

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

en: LWF plot Lägeren. Photo: Peter Waldner, 2019

en:
![alt text](https://www.envidat.ch/dataset/b763c4e1-2de3-4e8f-9bb7-2ca533624060/resource/b6e747c6-7a85-43e0-958b-3522f370bbad/download/lwf_laegeren.jpg "LWF Lägeren")

This research site is located on the southern slope of the Lägern, which forms the eastern most part of the Jura mountains, within a managed mixed deciduous forest. The forest is highly diverse, dominated by beech, but also including ash, maple, spruce and fir trees. Eddy covariance flux measurements were started in April 2004. The site was part of the international CarboEurope IP network and currently part of the following national networks:

* National Air Pollution Monitoring Network ([NABEL](https://www.empa.ch/web/s503/nabel))
* [TreeNet](https://treenet.info/switzerland/laegeren): The biological drought and growth indicator network
* Long-term Forest Ecosystem Research ([LWF](https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/laegeren.html))
* [Swiss FluxNet](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae/)

The site measurements are jointly run by the Swiss Federal Laboratories for Materials Science and Technology ([EMPA](https://www.empa.ch)), the groups [Grassland Sciences](https://www.gl.ethz.ch) and [Land-Climate Dynamics](https://iac.ethz.ch/group/land-climate-dynamics.html) from the Swiss Federal Institute of Technology Zurich, the unit [Soil Science & Biogeochemistry](https://www.geo.uzh.ch/en/units/2b.html) from the University of Zurich, and the Swiss Federal Research Institute ([WSL](https://www.wsl.ch)).

LWF Plot Lägeren

- Community: Wettingen / canton AG
- Date of installation: 1.05.2012
- Size of the plot: 1.34 ha
- Altitude: 643 - 718 m
- Mean slope: 37 %
- Geology (in German): Kettenjura; Jura: Malm, Molassehangschutt
- Soil types (WSL) : calcareous brown soil, chromic luvisol, mixed rendzina
- Woodland association after Ellenberg and Klötzli's classification (1972): Galio odoratio-Fagetum typicum bis - Pulmonario-Fagetum typicum
- Main tree species: fagus sylvatica
- Management system: high forest
- Silvicultural system: forest reserve
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 72.18 cm
- Number of trees BHD >= 12 cm (2011): 503
- Maximum tree age: picea abies: 120-170 years, fagus sylvatica: ca. 150 years

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-fluxnet

display_name: swiss-fluxnet

swiss-forest-lab

display_name: swiss-forest-lab

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: LWF Lägeren long-term research site


en: plot-size monitoring europe national-forest-inventory biodiversity species-richness

en: Mega-plot data from Norway, Spain, Slovakia, Switzerland

en:
The mega-plot data consist in a single .RDS file containing 10 columns, where each row represents a mega-plot:
- "Country": refers to either "Spain", "Norway", "Switzerland" or "Slovakia"
- "Single_plot_size": refers to the size of the plots used in the aggregation process to make the mega-plots. The largest value within each country refers to the orignial plot size of each NFI, while the smaller values within each country refers to the downscaled datasets. The analyses were performed independently for each "Country" and "Single_plot_size", so these should not be analysed together.
- "n_plots": refers to the number of plots aggregated
- "A_megaplot": area of the mega-plots (sum of the area of all the aggregated plots). It equals "Single_plot_size" multiplied by "n_plots".
- "SR": Species richness of the mega-plot
- "Hclim": EH variable, corresponds to the climate heterogeneity value of the mega-plot, based on mean annual temperature and annual precipitation
- "Htopo": EH variable, corresponds to the topographic heterogeneity value of the mega-plot, based on slope and Beers aspect
- "Hsoil": EH variable, corresponds to the soil heterogeneity value of the mega-plot, based on soil pH
- "Hba": EH variable, corresponds to the basal area heterogeneity value of the mega-plot
- "Hres": residual environmental heterogeneity

To fit our models, all EH variables have been standardised. In this dataset, EH variables are provided before standardisation (raw).

en:
The data file refers to the data used in Portier et al. "Plot size matters: towards comparable species richness estimates across plot-based inventories" (2022) *Ecology and Evolution*.

This paper describes a methodoligical framework developed to allow meaningful species richness comparisons across plot-based inventories using different plot sizes. To this end, National Forest Inventory data from Switzerland, Slovakia, Norway and Spain were used. NFI plots were aggregated into mega-plots of larger sizes to build rarefaction curves. The data stored here correspond to the mega-plot level data used in the analyses, including for each country the size of the mega-plots in square meters (A), the corresponding species richness (SR) as well as all enrionmental heterogeneity measures described in the corresponding paper. Mega-plots of country-specific downscaled datasets are also provided.

The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). Contact details for data requests from all NFIs can be found in the ENFIN website (http://enfin.info/).

biodiversity

display_name: biodiversity

europe

display_name: europe

monitoring

display_name: monitoring

national-forest-inventory

display_name: national-forest-inventory

plot-size

display_name: plot-size

species-richness

display_name: species-richness

en: Towards comparable species richness estimates across plot-based inventories - data


maintainer: Daniela Brito Melo

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

en: LES-LSM code

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

en: Data in Figures

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

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

drifting-snow

display_name: drifting-snow

large-eddy-simulations

display_name: large-eddy-simulations

mixed-grain-transport

display_name: mixed-grain-transport

saltation

display_name: saltation

snow-cohesion

display_name: snow-cohesion


maintainer: EnviDat Support

en: uas snow remote-sensing swe snow-depth

en: HS map Brämabül, 14. April 2015

media_type: image/tiff

en:
10 cm resolution
15 cm accuracy

format: TIFF

mimetype: image/tiff

en: .tfw file

en: coordinates for the .tif raster

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

A detailed description is given here:

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

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

remote-sensing

display_name: remote-sensing

snow

display_name: snow

snow-depth

display_name: snow-depth

swe

display_name: swe

uas

display_name: uas

en: UAS based snow depth maps Brämabüel, Davos, CH


maintainer: Matthias Haeni

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

en: Data plot of the forest station (VSB)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Data plot of the field station (VSF)

en: Site photo of the field station (VSF)

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

mimetype: image/jpeg

en: Site photo of the forest station (VSB)

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

en: Forest station data (VSB)

media_type: text/csv

en: Data file.

format: CSV

mimetype: text/csv

en: Site aerial photo (Visp)

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

en: Field station data (VSF)

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 Visp in Switzerland where one station is located within a natural mixed forest stand (VSB) with Scots pine (_Pinus sylvestris_; 40-80 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VSF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Visp is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Axel Volkwein

en: experiment rockfall trajectory mass-movements field-test experiments field-data field-tests natural-hazards

en: Overview of all tests

en:
This table gives an overview on all tests performed including
- block used
- start & end position
- number of jumps
- etc.
-

en: These data contain the point clouds obtained from laser scanning the test slope and the six test blocks used for the experiments. The coordinates of the slope are given in CH-1903 format. The coordinates of the block point clouds are kept in a lokal x/y/z coordinate system.

mimetype: application/zip

en: Jumps and impacts

en: This table contains data of more than 2200 of each jumps and impacts of the test blocks on a grassy slope. It gives there durations in combination with acting accerlartions and the rotational speed of the blocks. The data were retrieved using the block internal accelerometer and rotation sensors. The combination of these data with the measurement from the local positioning system allows the deduction of jump lengths, slope gradient etc.

en: Block trajectories recorded from local positioning system

en: This file contains more than 80 rockfall trajectories projected onto the terrain, i.e. the positions of the single blocks over time during the single tests that were recorded by the local positioning system with about 10Hz. It contains the detected positions as well as a smoothened path (see Volkwein et al., Geosciences, 2018). The table also contains the derived velocities and accelerations along the slope (attention: these accelerations are the once that occur during an impact).

en:
In summer 2014, 6 rock blocks between 20 and 80kg have been thrown in total 111 times down a slope at the Swiss Oberalppass close to the village Tschamut. The slope was mainly covered by grass and its lower part was flat and large enough to provide natural runouts of the single trajectories.

An extensive measurement program has been set up to measure the block trajectories: With surveyor's instruments the slope and the six used rock blocks were scanned and the start and end positions of each test were recorded. During the single events two cameras filmed the trajectories. A special sensor device located within the blocks recorded the acting accelerations and rotational speeds over time. Further, the device emitted a Wifi signal that got detected from eight receivers around the slope. Based on this signal the block position has been recorded over time.

The dataset contains all data that were gathered through above field campaign.

experiment

display_name: experiment

experiments

display_name: experiments

field-data

display_name: field-data

field-test

display_name: field-test

field-tests

display_name: field-tests

mass-movements

display_name: mass-movements

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

trajectory

display_name: trajectory

en: Repetitive trajectory testing in Tschamut 2014


en: plant-available-water-storage-capacity water-balance physiological-drought root-water-uptake european-summer-drought

en: water balance fluxes

en: Average yearly simulated water fluxes (mm) for the Swiss forest at 500m spatial resolution.

en: available water storage capacity (AWC)

en: Posterior LWF-Brook90 model parameters

en: Sensitive above and belowground parameters of LWF-Brook90 model are provided for best runs of the calibration against soil matric potential data.

en: Uncertainties of the available water storage capacity (mm)

en: Calculated for 1m soil depth and the potential rooting depth (mrd).

en: monthly covariance measurements

en: Data from Davos and Lägeren from 2013-2019

en: Ratio of actual to potential transpiration

en: Simulated monthly values at 500m spatial resolution.

en: Gravitational water capacity (mm)

en: Calculated for 1m soil depth and the potential rooting depth at 500m spatial resolution.

en: Root distribution and root water uptake

en: Root density distribution derived from 44 soil profiles and simulated daily root water uptake (mm) aggregated for deciduous and coniferous trees across Switzerland.

en: Soil water pools with soil depth

en: Average plant available - (AWC), gravitational (GWC) and residual (RES) water capacity per soil depth layer of the Swiss forest.

en: Soil matric potential (kPa)

en: Mean simulated values for August 2018 with 500m spatial resolution.

en: measured soil matric potential

en: In daily resolution for a site near Neunkirch and Hohtenn from 2014 to 2018.

en:
The Swiss forests' water availability during the 2015 and 2018 droughts was modelled by implementing the mechanistic Soil-Vegetation-Atmosphere-Transport (SVAT) model LWF-Brook90 taking advantage of regionalized depth-resolved soil information and measured soil matric potential and eddy covariance data. Data include
1) csv of soil matrix potential and eddy covariance data, 2) csv of posterior model parameters, 3) geotiffs of plant-available water storage capacity until 1m soil depth and the potential rooting depth, 4) geotiffs of yearly average (2014-2019) of precipitation (P), actual evapotranspiration (ETa), evaporation as the sum of soil, snow and interception evaporation (E), actual transpiration (Ta), runoff (F) and total soil water storage (SWAT), 5) csv of simulated root water uptake aggregated for different soil depths per deciduous and coniferous trees across Switzerland at daily resolution and cumulative root fraction per soil depth for coniferous and deciduous sites, 6) geotiffs of the ratio of actual to potential transpiration (-) as mean of non-drought years 2014, 2016, 2017, 2019 and 2015 and 2018 for the month June, July, August, September and October, 7) geotiff of mean soil matric potential in the rooting zone in August 2018, 8) geotiffs of gravitational water capacity (mm) until 1 m soil depth and the maximum rooting depth (mrd), 9) geotiffs of uncertainties of the available water storage capacity (AWC) until 1m soil depth and the mean maximum rooting depth (mrd), 10) csv of average plant available - (AWC), gravitational (GWC) and residual (RES) water capacity per soil depth layer of the Swiss forest.

european-summer-drought

display_name: european-summer-drought

physiological-drought

display_name: physiological-drought

plant-available-water-storage-capacity

display_name: plant-available-water-storage-capacity

root-water-uptake

display_name: root-water-uptake

water-balance

display_name: water-balance

en: Water availability of Swiss forests during the 2015 and 2018 droughts


maintainer: Christoph Marty

en: CH2014 REPORT

en:
CH2014-Impacts: Toward Quantitative Scenarios of Climate Change Impacts in Switzerland

The CH2014-Impacts report presents the results of a concerted effort of 15 Swiss research institutions to describe impacts of climate change in Switzerland quantitatively. This dataset contributes to the Crosphere/Snowcover section.

en: Aare input

en: Grid input data for the 'Aare' CH2014 simulation.

mimetype: application/x-tar

en: Dataset

en: Datasets are grouped under experiment "Aare" and "Graubunden".Data is also made available in netCDF format. A range of tools are available to [read netCDF](http://www.unidata.ucar.edu/software/netcdf/software.html).

en: Graubunden input

en: Grid input data for the 'Graubunden' CH2014 simulation.

en:
 Overview

The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change.

 Experimental Setup

Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graubünden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m × 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999–2012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085).

 Data

Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5–9 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400–800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average.

climate-change

display_name: climate-change

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Alpine3D simulations of future climate scenarios CH2014


deadwood-generator

title_for_slug: deadwood-generator

en: piled-up-logs ramms windthrow lying-deadwood rockfall

en: Authomatic Deadwood Generator (ADG)

en: Complete ADG, including the calling script explaining the parameters to use, a sample DEM and a corresponding sample tree file, and sample outputs. All files are zipped together. Input DEM needs to be a .TIF file while the script uses python 3 and has several dependencies to the rasterio-python package (https://rasterio.readthedocs.io/en/latest/).

en: Zipped Folder containing the shape files (.shp) of the mapped trees, their bases, the control area and the used rock release areas for both windthrow areas W1 and W2.

en: The used Orthophoto with a 4 cm resolution, based on the flight survey with the eBee-UAV from the 24 Jan. 2020.

format: GeoTIFF

en: The here presented code generates discrete three-dimensional, RAMMS::ROCKFALL readable deadwood log files (.pts-format) of windtrown forests, including the pilling effect due to slightly different throw directions.

lying-deadwood

display_name: lying-deadwood

piled-up-logs

display_name: piled-up-logs

ramms

display_name: ramms

windthrow

display_name: windthrow

en: Deadwood Generator


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

en: ECT data (Norway, Switzerland)

en: Rutschblock data (Switzerland)

en: avalanche occurrence data (Norway, Sitzerland) - largest avalanche size per day and warning region

en: avalanche occurrence data (Norway, Sitzerland) - avalanche sizes per day and warning region

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

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland

en: Stability tests, avalanche observations, Switzerland, Norway


en: elevation parasitoids pests gradient bark-beetles predators pinus insects

en: Gradients

en:
The column headers refer to:

- SamplingCode: WSL fauna database code referring to year and trap type (not relevant)
- SamplingDate: Date when the emergence traps were emptied, corresponds roughly to the date of emergence
- TreeNumber: Tree ID
- Defoliation: percentage of needle loss or crown transparency, according to international standards, of the trees prior to felling
- BarkSurface: bark surface area of stem parts exposed in emergence traps
- YearOfAttack: Year of colonization. Technically, the colonization window opened on 1st of January and closed in June, when sections of the tree stems were transported to the institute
- YearOfEmergence: Emergence year of a given cohort of insects in the greenhouse
- YearAfter Attack: Temporal difference between year of colonization and year of emergence of a given cohort of insects, corresponding to total development time
- Region: AO Aosta (ITA), GR Grisons (CH), VS Valais (CH)
- Location: locality nearest to the gradient
- Elevation: Site where trees were felled and exposed to colonization
- Aspect: north (N) or south (S) facing slope
- Species: taxonomic species according to Fauna Europaea
- Family: taxonomic family
- Order: taxonomic order: Col. Coleoptera, Hym. Hymenoptera, Dipt Diptera, Raph Raphidioptera
- Guild: Phyto phytophagous, Pred predatory, para parasitoid
- Number: number of sampled individuals of a given species at a given date.
- Density: number of emerged insects per m2 bark surface
- Identifyer: specialist who identified the insects

en:
The colonization of cut pine stems by wood-inhabiting insects was investigated at various elevations. The study sites were located in the regions of Aosta Valley (Italy), Valais (Switzerland), and Grisons (Switzerland). In each region, there were two gradients in pine (Pinus sylvestris) forests, with three study sites at 900 m, 1200 m, and 1600 m a.s.l. each. Vital trees were felled in late autumn and the stems were colonized by pioneering xylophagous insects and their natural enemies next spring. Pieces of these stems were cut and exposed in emergence traps in a greenhouse. In each region the survey was done in two consecutive years.

Please contact author for terms of use.

bark-beetles

display_name: bark-beetles

elevation

display_name: elevation

gradient

display_name: gradient

insects

display_name: insects

parasitoids

display_name: parasitoids

pests

display_name: pests

pinus

display_name: pinus

predators

display_name: predators

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Pine insects along elevational gradients


en: ice laboratory-study chemistry snow-atmosphere-interactions atmosphere

en:
The table lists all NEXAFS raw data with the following columns:

* name:						raw file name
* xRayEkinRange_eV: 		Smallest and largest X-ray photon energy in eV
* scientaPassEnergy_eV: 	Pass energy of electron analyzer in eV
* scientaDwellTime_ms: 		Dwell time of electron analyzer in ms
* regionName:				O K-edge for all data
* scientaAcquisitionMode: 	Fixed energy (snapshot) mode
* scientaEkinRange_eV: 		Smallest and largest energy of the kinetic energy window used for detection in this work
* scientaEkinStep_eV		Not defined in fixed energy mode
* scientaLensMode:			Tune table
* scientaRegionIterations: 	Iterations (sweeps)
* scientaSequenceIterations: Iterations (sweeps)
* sampleTemp_dC: 			Temperature as monitored at the sample holder in °C
* cellPressure_mbar: 		Total pressure in measurement cell in mbar
* iceTemp_dC: 				Temperature of ice sample calculated based on vapour pressure (assuming total pressure equals partial pressure of water) in °C
* smplX_mm: 				positon of measurement spot, x dimension in mm
* smplY_mm: 				positon of measurement spot, y dimension in mm
* smplZ_mm: 				positon of measurement spot, z dimension in mm
* sealingTemp:				temperature at base of sample manipulator
* lastModifiedDatestr: 		time and date when data aquisition was finished
* sample: 					sample description

en: Near Ambient Pressure partial Auger-Meitner electron yield X-ray absorption spectra and photon flux data. X-ray energy in eV in column 1, analyzer electron count intensity in arb. units in column 2.

en: Processed data as shown in Figure 4 of publication. X-ray energy in eV in column one, electron count intensity in arb. units in column 2.

en: Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice.

atmosphere

display_name: atmosphere

chemistry

display_name: chemistry

ice

display_name: ice

laboratory-study

display_name: laboratory-study

snow-atmosphere-interactions

display_name: snow-atmosphere-interactions

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: An ice oxygen K-edge NEXAFS spectroscopy data set on gas-phase processing


en: sustainability potential-assessment bioenergy biomass-resources availability

en: Biomass Potentials Switzerland Cantons

en: Biomass Supply Costs

en: Biomass Potential Switzerland Municipalities

en: Domestic biomass potential based on 2014 data at the municipality level for the theoretical and sustainable potential for 10 types of biomass.

en: Biomass Potentials Switzerland

en: Switzerland has a reliable and cost efficient energy system. Due to phase out of nuclear energy it is necessary to find new options to maintain this powerful energy system. The Swiss energy strategy 2050 aims to reduce CO2-emissions, increase efficiency and promote renewable energies. The Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) examined relevant woody and non-woody biomass quantities (cubic meters, fresh-, dry weight) and their energy potentials (in Petajoules: primary energy and biomethane) with a similar methodological approach. The work was done within the frame of the Swiss Competence Centers for Energy Research (SCCER) especially in line with the SCCER Biomass for Swiss energy future (Biosweet). With a uniform and consistent approach for the current potentials ten biomass categories were estimated and aggregated for the whole of Switzerland. In this context solutions for the technical, social and political challenges are promoted. First, considering the different biomass resources characteristics and available data, appropriate methods at the finest scale possible were elaborated to estimate the annual quantities which could theoretically be collected (theoretical potential). Then, explicit and rational restrictions for sustainable bio-energy production were defined according to the current state of the art and subtracted from the theoretical potential to obtain the sustainable potential. The main restrictions are competing material utilizations, environmental factors and supply costs. Finally, the additional sustainable potential was estimated considering the current bioenergy production. Our main purpose was to provide potentials for developing conversion technologies as well as a detailed and comprehensive basis of the Swiss biomass potentials for energy use for economic and political decision makers. The complete report is available under https://www.dora.lib4ri.ch/wsl/islandora/object/wsl%3A13277/datastream/PDF/view

availability

display_name: availability

bioenergy

display_name: bioenergy

biomass-resources

display_name: biomass-resources

potential-assessment

display_name: potential-assessment

sustainability

display_name: sustainability

en: Potentials of domestic biomass resources for the energy transition in Switzerland


photogrammetric-drone-data-dorfberg

maintainer: Yves Bühler

title_for_slug: photogrammetric-drone-data-dorfberg

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

en: Data Access

en:
- 3cm-resolution Orthophoto
- 10cm-resolution DSM
- 10cm-resolution snow depth raster
- original RGB images
- Agisoft Metashape Report

All products are also available as a cloud optimized geotiff.

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).

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

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Photogrammetric Drone Data Dorfberg


en: landscape-planning urban-landscape land-change urban-planning concur

en:
Book of abstracts from the virtual conference "From Plans to Land Change: Dynamics of Urban Regions"

Cities and urban regions are among the most dynamic land-use systems in the world, with dramatic consequences for the provision of ecosystem services and the livelihood of people. Planning is a multifaceted activity with extensive experience in the management of these urbanization processes. However, our understanding of planning’s contribution to shaping urban land use, form and structure is still incomplete, with serious consequences for the efficacy of urban planning and land change models.

This international conference aims to bring together the community of scholars working on planning evaluation and urban modelling. The participants are offered the opportunity to present their current research and to discuss how theoretical developments, data sources, comparative studies and modelling approaches might advance the field.

The conference was financially supported by the CONCUR project and sustained by Swiss Federal Research Institute WSL.

concur

display_name: concur

land-change

display_name: land-change

landscape-planning

display_name: landscape-planning

urban-landscape

display_name: urban-landscape

urban-planning

display_name: urban-planning

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: From Plans to Land Change: Dynamics of Urban Regions. Book of Abstracts


en: century climate minimum-temperature climate-layers free-climate-data precipitation maximum-temperature timeseries air-temperature

en: CHELSA Technical Documentation

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

en:
CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum. Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.

CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

air-temperature

display_name: air-temperature

century

display_name: century

climate

display_name: climate

climate-layers

display_name: climate-layers

free-climate-data

display_name: free-climate-data

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

timeseries

display_name: timeseries

en: CHELSAcruts -  High resolution temperature and precipitation timeseries for the 20th century and beyond


en: dvm npp growth assimilation forest

en: NPP anomalies

en: Spatially resolved yearly anomalies of NPP simulated by 3-PG model relative to the 1961–1990 period.

en:
Simulated net primary productivity (NPP) anomalies (percent deviation) in 1961-2018 years relative to the 1961–1990 reference period for _Picea abies_ and _Fagus sylvatica_. NPP was simulated for the species' potential distribution range in Switzerland on a 1 × 1 km grid using 3-PG model.
We first assimilated nearly 800 observation years from 271 permanent long-term forest monitoring plots across Switzerland, obtained between 1980 and 2017, into the 3-PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from.
We then estimated the NPP anomalies by first simulating the growth of _P. abies_ and _F. sylvatica_ monocultures with the average climate observed during the 1961–1990 period, until the age of 40 years (spin-up). The stands were simulated starting as 2-year-old plantations with an initial density of 10,000 trees/ha. Thinning was performed at age 20 and 35 to reach a final density of ca. 1,000 trees/ha at age 40. We then simulated 30 years forced by monthly resolved climatic data from either the 1961–1990 (reference, according to MeteoSwiss) or the 1991–2018 period. We neglected the first 40 years of simulations due to high variation in productivity caused by early stage stand development. To study the impact of climate extremes on NPP, we focused on the deviation in NPP (expressed in percentage difference from the reference period) during the 30 year period (age 41–70).

assimilation

display_name: assimilation

dvm

display_name: dvm

forest

display_name: forest

growth

display_name: growth

npp

display_name: npp

en: Net primary productivity (NPP) anomalies simulated by 3-PG model for Switzerland


en: machine-learning avalanche-danger-level avalanche-forcasting avalanche numerical-avalanche-forecasting

en: This subset of data includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the quality-controlled danger ratings for dry-snow avalanche conditions forecast in Switzerland.

en: This data set includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the danger ratings for dry-snow conditions assigned in the Swiss avalanche bulletin to the location of the weather station.

en: README

en: This read-me file contains the descriptions of the variables

en:
Each set includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the danger ratings for dry-snow conditions assigned to the location of the station. The data set of RF 1 contains the danger ratings published in the official Swiss avalanche bulletin, and the data set of RF 2 is a quality-controlled subset of danger ratings.
These data are the basis of the following publication: Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, 2022.

avalanche

display_name: avalanche

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forcasting

display_name: avalanche-forcasting

machine-learning

display_name: machine-learning

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting

educ

en: Education, culture and sport

en: Weather, snowpack and danger ratings data for automated avalanche danger level predictions


maintainer: James W. Kirchner

en: groundwater streamflow diel-cycle snowmelt sap-flow ecohydrology evapotranspiration diurnal-cycle

en: MODIS NDSI script

media_type: text/plain

en: Google Earth Engine script for computing MODIS normalized difference snow index (NDSI) values and averaging them over subcatchment areas.

format: TXT

en: 30-minute time series

en: 30-minute hydrometeorological and ecohydrological time series.

en: Metadata

en: Column descriptions for both data files, and map coordinates for all measurement locations.

en: MODIS EVI2 script

en: Google Earth Engine script for computing MODIS two-band enhanced vegetation index (EVI2) values and averaging them over subcatchments.

en: Subcatchment polygons

en: Subcatchment polygons for use in the Google Earth Engine scripts.

en: Daily time series

en: Daily hydrometeorological and ecohydrological time series

en:
Hydrometerological and ecohydrological time series from Sagehen Creek and Independence Creek, Sierra Nevada, USA, illustrating hydrological responses to daily cycles in snowmelt and evapotranspiration forcing.

Data include 30-minute time series of

- weather variables,

- sap flow fluxes,

- groundwater levels (in two riparian transects of shallow groundwater wells),

- and stream stages (at 12 sites spanning a 500-meter elevation gradient),

and daily time series of

- temperature, precipitation, and snow water equivalent at three nearby snow telemetry stations

- diel cycle index values for groundwater levels and stream stages,

- and MODIS normalized difference snow index (NDSI) and enhanced vegetation index (EVI2) values averaged over selected subcatchments.

Google Earth Engine scripts for extracting the MODIS data are also provided.

diel-cycle

display_name: diel-cycle

diurnal-cycle

display_name: diurnal-cycle

ecohydrology

display_name: ecohydrology

evapotranspiration

display_name: evapotranspiration

groundwater

display_name: groundwater

sap-flow

display_name: sap-flow

snowmelt

display_name: snowmelt

streamflow

display_name: streamflow

envidat

en: EnviDat: the Environmental Data Portal

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

en: Daily cycles in solar flux, snowmelt, transpiration, groundwater, and streamflow at Sagehen and Independence Creeks, Sierra Nevada, USA


maintainer: Lisa Hülsmann

en: empirical-mortality-models inventory-data tree-mortality forest-reserves tree-growth

en: Calibration data

en:
A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory.

 Meta data
Reserve: Name of the forest reserve

Plot: Permanent plot number

 Tree data
Species: Latin species name (or species group, cf. _Quercus_ spp.)

y: Status of the tree with 0 = alive and 1 = dead

dt: Length of the mortality period (years)

DBH: Diameter at breast height (mm) of the second inventory

relBAI: Annual relative basal area increment (-) calculated based on the first and the second DBH measurement as the compound annual growth rate of the trees basal area

 Stand data
_All stand variables were calculated for the second inventory._

mDBH: Mean diameter at breast height (mm)

iqrDBH: Interquartile range of diameter at breast height (mm)

BA: Basal area per hectare (m2 ha-1)

N: Number of trees per hectare (ha-1)

 Climate data
_Both climate variables were calculated for the period between the second and the third inventory._

P: Mean annual precipitation sum (mm)

mT: Mean annual air temperature (°C)

en:
The dataset comprises > 90 000 records from inventories in 54 strict forest reserves in [Switzerland](https://www.wsl.ch/de/wald/biodiversitaet-naturschutz-urwald/naturwaldreservate.html) and [Lower Saxony / Germany](http://naturwaelder.de/) along a considerable environmental gradient. It was used to develop parsimonious, species-specific mortality models for 18 European tree species based on tree size and growth as well as additional covariates on stand structure and climate.

 Inventory data
Measurements had been conducted repeatedly on up to 14 permanent plots per reserve for up to 60 years with re-measurement intervals of 4 - 27 years. The permanent plots vary in size between 0.03 and 3.47 ha. The inventories provide diameter measurements at breast height (DBH) and information on the species and status (alive or dead) of trees with DBH ≥ 4 cm for Switzerland and ≥ 7 cm for Germany.

 Data selection
We excluded three permanent plots where at least 80 % of the trees died during an interval of 10 years, and mortality could be clearly assigned to a disturbance agent. Mortality in the remaining stands was rather low, with a mean annual mortality rate of 1.5 % and strong variation between plots from 0 to 6.5 % (assessed for trees of all species with DBH ≥ 7 cm).
We only used data from permanent plots with at least 20 trees per species to obtain reliable plot-level mortality rates even for species with low mortality rates (about 5 % during 10 years), and selected tree species occurring on at least 10 plots to cover sufficient ecological gradients. This led to a dataset of 197 permanent plots and 18 tree or shrub species: _Abies alba_ Mill., _Acer campestre_ L., _Acer pseudoplatanus_ L., _Alnus incana_ Moench., _Betula pendula_ Roth, _Carpinus betulus_ L., _Cornus mas_ L., _Corylus avellana_ L., _Fagus sylvatica_ L., _Fraxinus excelsior_ L., _Picea abies_ (L.) Karst, _Pinus mugo_ Turra, _Pinus sylvestris_ L., _Quercus pubescens_ Willd., _Quercus_ spp. (_Q. petraea_ Liebl. and _Q. robur_ L.; not properly differentiated in the Swiss inventories), _Sorbus aria_ Crantz, _Tilia cordata_ Mill. and _Ulmus glabra_ Huds..

 Predictors of tree mortality
We considered tree size and growth as key indicators for mortality risk. Radial stem growth between the first and second inventory and DBH at the second inventory were used to predict tree status (alive or dead) at the third inventory. To this end, the annual relative basal area increment (relBAI) was calculated as the compound annual growth rate of the trees basal area. Additional covariates on stand structure and climate comprise mean annual precipitation sum (P), mean annual air temperature (mT), the mean and the interquartile range of DBH (mDBH, iqrDBH), basal area (BA) and the number of trees (N) per hectare.

 Further information
For further information, refer to Hülsmann _et al_. (in press) How to kill a tree – Empirical mortality models for eighteen species and their performance in a dynamic forest model. _Ecological Applications_.

empirical-mortality-models

display_name: empirical-mortality-models

forest-reserves

display_name: forest-reserves

inventory-data

display_name: inventory-data

tree-growth

display_name: tree-growth

tree-mortality

display_name: tree-mortality

educ

en: Education, culture and sport

en: Calibration data for empirical mortality models of 18 European tree species


maintainer: Chunhong Zhao

en: urban-morphology land-dynamics landscape austin land-use

en: Land use dynamics in Austin since 1992

en: The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006–2011 and 2011–2016. The study also aims to understand to what extent the changes in the built environment (in terms of ‘efficient growth’ versus ‘inefficient growth’) from the 1990s to 2016 in the Austin metropolitan area corresponded with ‘compact and efficient growth’ planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 × 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package.

austin

display_name: austin

land-dynamics

display_name: land-dynamics

land-use

display_name: land-use

landscape

display_name: landscape

urban-morphology

display_name: urban-morphology

en: Land use/cover dynamics in Austin metropolitan area since 1992


en: perceived-landscape-quality energy survey renewable-energy-meanings hybrid-choice-model stated-choice-model landscape-meanings landscape-technology-fit landscape renewable-energy mixed-renewable-energy-landscapes

en: ENERGYSCAPE choice model attributes and tasks

en: This data provides overview about the choice model design, the choice tasks and the choice attributes.

en: ENERGYSCAPE base data variable description

en: This file contains descriptions (variable name, variable description, scale) of all variables of the base data.

en: ENERGYSCAPE exemplary choice task

en: This file contains an exemplary choice task.

en: Energyscape RAW data

en: This dataset contains the raw data of the questionnaire including headers (column names). One line represents one respondent. Details to each variable (including scales) can be found in an attached PDF file.

en: ENERGYSCAPE Landscape-technology fit example

en: This file contains an exemplary illustration on how the landscape-technology fit concept was measures within the survey.

en: ENERGYSCAPE Landscape attribute of choice model

en: This file illustrated the 7 landscapes applied in the choice model. The numbers appear are identical to the numbers in the base data.

en: We present stated preference data based on a national representative Swiss online panel survey for the preference of renewable energy infrastructure in landscapes. The data was collected between November 2018 to March 2019 using an online questionnaire and resulted on 1026 responses. The online questionnaire consisted of two main parts – (1) questions covering meanings related to landscapes, nature and renewable energy infrastructure, including the “fit” of landscape/REI combinations and (2) online choice experiment. While in the first part of the questionnaire we asked respondents about their personal connection to certain landscapes, to nature and to specific renewable energy infrastructures, we also asked them to evaluate the fitting of seven different Swiss landscapes (near natural alpine areas, northern alps, touristic alpine areas, agricultural plateau, urban plateau, jura ridges, urban alpine valley) with five different REI (wind, PV ground, PV roof, power lines) combinations. In the second part of the questionnaire, the stated choice experiment confronted respondents with 15 consecutive choice tasks, with each task involving a choice between two “energy system transformation” options and an opt-out option (none). Each choice option (beside the opt-out option) included four unlabeled attributes (landscape, wind energy infrastructure, photovoltaic energy infrastructure, high voltage overhead power line infrastructure) with varying levels. Due to data cleaning procedures (item nonresponse) the number of responses used within hybrid choice modelling and analysis was n=844 (12660 choice observations). An analysis of the hybrid choice model and further insights are presented in the article “How landscape-technology fit affects public evaluations of renewable energy infrastructure scenarios. A hybrid choice model.”

energy

display_name: energy

hybrid-choice-model

display_name: hybrid-choice-model

landscape-meanings

display_name: landscape-meanings

landscape-technology-fit

display_name: landscape-technology-fit

mixed-renewable-energy-landscapes

display_name: mixed-renewable-energy-landscapes

perceived-landscape-quality

display_name: perceived-landscape-quality

renewable-energy

display_name: renewable-energy

renewable-energy-meanings

display_name: renewable-energy-meanings

stated-choice-model

display_name: stated-choice-model

survey

display_name: survey

en: Hybrid choice modelling dataset for the effects of landscape-technology fit on public evaluations


napf-ert-monitoring-data

title_for_slug: napf-ert-monitoring-data

en: early-warning monitoring soil-moisture shallow-landslides ert

en: ERT filtered monitoring data

en: ERT filtered monitoring data in dat format

en: ERT raw monitoring data

en: ERT raw monitoring data in bin format

en:
The dataset contains the electrical resistivity tomography (ERT) monitoring data from the publication Wicki and Hauck (2022). It contains the unprocessed monitoring data and the filtered monitoring data prior to the inversion process.

The data is organized in two zip-files:

*  Napf_Raw_BIN.zip: Raw monitoring data in bin-format
*  Napf_Filtered_DAT.zip: Filtered monitoring data in dat-format including topography of the monitoring line

The zip files contain the apparent resistivity measurements (ohm m) of the individual measurements. The naming convention of the files is according to following convention: site_profile_configuration_date_time.format

The file names contain following abbreviations:

*  Site: Napf
*  Profile: Hor (horizontal profile), Ver (vertical profile)
*  Configuration: WS (Wenner-Schlumberger configuration)
*  Date: Format YYYY-MM-DD
*  Time: Format hhmm

early-warning

display_name: early-warning

ert

display_name: ert

monitoring

display_name: monitoring

shallow-landslides

display_name: shallow-landslides

soil-moisture

display_name: soil-moisture

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Napf ERT monitoring data


digitizing-historical-plague

maintainer: Christian Ginzler

title_for_slug: digitizing-historical-plague

en: palaeo-epidemiology plague-outbreaks plague-inventory black-death historical-plague yersinia-pestis plague-mapping bubonic-plague

en: We present newly digitized data on 6,929 plague outbreaks that occurred between 1347 and 1900 AD across Europe. The data base on an inventory initially published 1976. For georeferencing the information of Tele Atlas 2009 was used. The coordinates are in the reference systems ETRS89 and WGS84.

black-death

display_name: black-death

bubonic-plague

display_name: bubonic-plague

historical-plague

display_name: historical-plague

palaeo-epidemiology

display_name: palaeo-epidemiology

plague-inventory

display_name: plague-inventory

plague-mapping

display_name: plague-mapping

plague-outbreaks

display_name: plague-outbreaks

yersinia-pestis

display_name: yersinia-pestis

en: Digitizing historical plague


basal-area

title_for_slug: basal-area

en:
Sum of the stem cross-section areas of all living trees and shrubs starting at 12 cm dbh (standing and lying) at a height of 1.3 m (dbh measurement height).

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

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

avalanches-silvaprotect

display_name: avalanches-silvaprotect

basal-area

display_name: basal-area

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

diameter-classes

display_name: diameter-classes

economic-region

display_name: economic-region

forest-type-and-stage-of-development

display_name: forest-type-and-stage-of-development

giants

display_name: giants

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

rockfall-silvaprotect

display_name: rockfall-silvaprotect

site-quality

display_name: site-quality

stage-of-stand-development

display_name: stage-of-stand-development

tree-species-woody-species

display_name: tree-species-woody-species


en: observations snow shapefile alps avalanches

en: Selected wet snow avalanche activity data Davos, Switzerland (2011-2014)

en: Zip archive with shapefiles for the selected wet snow avalanche polygons, including metadata.

en:
Polygons of wet snow avalanches in the Davos area, as documented by the Swiss avalanche warning service. The georeferenced outlines of the avalanches contain both the release as well as the deposit area, but without separating between both.

The dataset is a subset of the total record of 1615 avalanches classified as wet snow avalanches from October 2011 - September 2014, containing those 255 avalanches exceeding 0.0125 km^2. Every polygon comes with meta data, including the date of occurrence.

This dataset is the underlying dataset to:
Wever, N., Vera Valero, C. and Techel, F. (2018) _Coupled snow cover and avalanche dynamics simulations to evaluate wet snow avalanche activity_. Submitted to J. Geophys. Res., in review.

alps

display_name: alps

avalanches

display_name: avalanches

observations

display_name: observations

shapefile

display_name: shapefile

snow

display_name: snow


total-timber-volume

title_for_slug: total-timber-volume

en:
Volume of stemwood with bark of all living and dead trees and shrubs (standing and lying) starting at 12 cm dbh. This corresponds to the sum of the volumes of growing stock and deadwood. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.

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

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

total-timber-volume

display_name: total-timber-volume

tree-state-alive-dead

display_name: tree-state-alive-dead

tree-status

display_name: tree-status

en: Total timber volume


maintainer: Achille Capelli

en: liquid-water-content gnss gps lwc swe snow-water-equivalent snow

en: Snow and meteo data from automatic sensors for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: GNSS derived SWE, LWC and HS from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-20

en: Snow and meteo data from automatic sensors for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: GNSS derived SWE, LWC and HS from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-20

en: Snow profiles data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Daily data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Daily observer data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Snow and meteo data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Daily observer data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: GNSS derived data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: Snow and meteo data from automatic sensors from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: Daily observer data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: GNSS derived SWE, HS, and LWC for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Daily data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Manual profiles

en: Data of the traditional manual profiles for the locations Weissfluhjoch, Klosters and Küblis. For the location of Küblis there are no traditional manual profile data. We provided the data as both PDF and .CAAML format (See https://niviz.org/ or http://caaml.org/Schemas/V5.0/Profiles/SnowProfileIACS/CAAMLv5_SnowProfileIACS.pdf). Moreover, we added LWC and density profiles when available (all locations).

en: Snow profiles data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Snow profiles data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: Daily data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Webcam images

en: Webcam images showing some typical differences in the snow conditions at the four sites (Küblis, Klosters, Laret and Weissfluhjoch) for two time periods (14-18.3.2019 and 27.1-11.2.2020). Both time periods included a precipitation event with partial rain-on–snow occurring at the lower locations.

en: Daily data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Profile data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: This database contains GNSS derived snow water equivalent (SWE), liquid water content (LWC), and snow height (HS) and reference data collected during the two winter 2018-2020 at 4 sites Weissfluhjoch (2540 m asl, 46°49’47’’ N, 9°48’34’’E), Laret (1515 m asl, . 46°50’2’’N, 9°52’17’’E), Klosters (1200 m asl, 46°51’49’’N, 9°53’17’’E), and Küblis (815 m asl, 46°54’48’’N, 9°46’54’’E).

gnss

display_name: gnss

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

lwc

display_name: lwc

snow-water-equivalent

display_name: snow-water-equivalent

en: Snow water equivalent measurements with low-cost GNSS receivers along a steep elevation gradient in the East-ern Swiss Alps


en: economic-efficiency forest-accessibility forest-roads switzerland timber-transport wood-harvesting wood-extraction forest

en: Data Request "Forest Accessibility"

en: Raster map (10m resolution) of the overall suitability of the Swiss forest for economic wood extraction and transport

en: Data Request "Extraction Method"

en: Raster map (10m resolution) of the most suitable extraction method for wood in the Swiss forest

en:
Two raster maps (10m resolution) of: I) the most suitable extraction method for wood in the Swiss forest, and II) the overall suitability of the Swiss forest for economic wood extraction and transport.
A modern forest road system is important for the efficient management of forests. In order to assess the current forest accessibility in Switzerland on a comprehensive basis, the entire Swiss forest was investigated using a consistent methodology. In our model, wood extraction from the stand to the road and on-road transport are analysed in combination.
Suitable extraction methods for each forest parcel (10m x 10m) were determined using an approach in which ground-based, cable-based and air-based transport are distinguished. First, the areas for ground- and cable-based extraction were delineated. The trafficability of the forest areas was assessed based on the terrain and soil characteristics; trafficable areas also had to be connected to a forest road. To evaluate the suitability for cable-yarding (up to a maximum distance of 1500 m), terrain and possible obstacles (e.g., power lines) were considered. The remaining forest area, which was not suitable for either ground-based or cable-based methods, was assigned to the "helicopter" category. As a result of this analysis, a map of the most suitable skidding method for each plot could be created. When several methods were possible for a parcel, the priority was ground-based over cable-based over air-based.
Road transport was investigated using network analysis, based on the data set "Forest access roads 2013" from the Swiss National Forest Inventory (NFI), which contains information on width and weight limits of roads in the forest and up to the superordinate main road network. Thus, in addition to the distance, the largest type of vehicle allowed on the respective removal route could also be taken into account.
Based on the extraction method and the weight limits for on-road transport, the forest area was divided into three categories: 1) meets the requirements for efficient forest management (all forest parcels with ground-based extraction method or mobile cable-yarding, transport weight limit at least 28 tons); 2) limited suitability for efficient forest management; and 3) not suitable for efficient forest management (forest parcels in the "helicopter" category or transport with trucks under 26 tons).
The resulting maps cannot provide an accurate classification for each forest parcel. Missing or incorrect roads in the road dataset, insufficient information on ground trafficability or other local factors, the limitation to only three possible extraction systems, and failure to account for anchor trees, extraction methods changing over small distances, and unrealistically short cable-yarding distances can cause the model results to deviate from the assessment by an expert with knowledge of the local conditions. Also, protected areas were not excluded and harvesting intensity was not taken into account. The advantage of the method is that consistent criteria are used for the entire Swiss forest, making the results comparable throughout Switzerland.
The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available to third parties on request. (NFI data policy: https://www.lfi.ch/dienstleist/daten.php)
Input data used:
- Forest road dataset of the NFI4 (only truck roads from 3.0 m width and 26 t carrying capacity) (2016).
- NFI forest mask, 10 m resolution (2015)
- Digital elevation model, 10m resolution (based on swissALTI3D 2016)
- Slope map, 10m resolution (based on swissALTI3D 2016)
- Soil suitability map, 10m resolution (based on soil suitability map BFS 2000)
- Obstacles for cable lines, 10m resolution (buildings, major roads, power lines, railroads, based on swissTLM3D 2016)

economic-efficiency

display_name: economic-efficiency

forest

display_name: forest

forest-accessibility

display_name: forest-accessibility

forest-roads

display_name: forest-roads

switzerland

display_name: switzerland

timber-transport

display_name: timber-transport

wood-extraction

display_name: wood-extraction

wood-harvesting

display_name: wood-harvesting

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Accessibility of the Swiss forest for economic wood extraction (2021)


regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

label: geocat.ch permalink

de: AGIS Direktdownload

de: Daten der amtlichen Vermessung im Datenmodell Aargau (DM.01-AV-AG). Fixpunkte: Die Datenebene enthält die Lagefixpunkte der Kategorie 1 (Punkte der Landesvermessung), der Kategorie 2 (Punkte des Kantons) und der Kategorie 3 (Punkte der amtlichen Vermessung) sowie die Höhenfixpunkte der Kategorie 1 (Punkte des Landesnivellement), der Kategorie 2 (Punkte des kantonalen Nivellement) und der Kategorie 3 (Punkte von kommunalen Nivellementen). Die Punkte stellen den Bezug zum Koordinatensystem her. Die Höhen bzw. Gebrauchshöhen stützen sich auf das offizielle Höhensystem des Landesnivellement von 1902 (LN02) ab. Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lage- und Höhengenauigkeit der Fixpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 28). Die äussere Zuverlässigkeit eines jeden Fixpunktes wird durch geeignete statistische Kenngrössen nachgewiesen (TVAV Art. 34). Bodenbedeckung: Die Datenebene enthält die Bodenbedeckungsflächen (Gebäude, Strassen, Gewässer, Wald, etc.), welche flächendeckend als Gebietsaufteilung definiert sind, die Gebäude- bzw. Assekuranznummern und die Objektnamen (z.B. Gebäudebezeichnungen, Gewässernamen, etc.). Die Genauigkeitsanforderungen der Bodenbedeckungsflächen sowie die aufzunehmenden Minimalflächen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit von Einzelpunkten wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 29). Einzelobjekte: Die Datenebene enthält Einzelobjekte (z.B. Mauern, Brunnen, Masten, Brücken, etc.), welche als Punkt-, Linien- oder Flächenelemente dargestellt werden. Die Genauigkeitsanforderungen der Einzelobjekte richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Einzelpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 29). Nomenklatur: Die Datenebene enthält die Flur-, Orts- und Geländenamen. Die Flurnamen beziehen sich auf Geländeteile und sind flächendeckend als Gebietsaufteilung abgebildet. Die Ortsnamen beziehen sich auf abgegrenzte Geländeteile und überlagern die Flurnamen. Die Geländenamen stehen für einzelne Geländepunkte, welche nicht Orts- und Flurnamen sind und weisen keine Abgrenzung auf. Liegenschaften: Die Datenebene enthält die Grenzpunkte, die Grundstücke bzw. Parzellen und die selbständigen und dauernden Rechte soweit diese flächenmässig ausgeschieden werden können. Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Grenzpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Die Äussere Zuverlässigkeit der Grenzpunkte wird durch geeignete Kenngrössen nachgewiesen (TVAV Art. 34). Rohrleitungen: Die Datenebene enthält die Hochdruckleitungen für Gas. Die Genauigkeitsanforderungen der Rohrleitungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Einzelpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Gebäudeadressen: Die Datenebene enthält die Gebäudeadressen bestehend aus Haus- bzw. Polizeinummern sowie Strassen- und Platznamen. Hoheitsgrenzen: Die Datenebene enthält die Hoheitsgrenzpunkte und Hoheitsgrenzen (Gemeinde-, Bezirks-, Kantons- und Landesgrenze). Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Hoheitsgrenzpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Die Äussere Zuverlässigkeit der Hoheitsgrenzpunkte wird durch geeignete Kenngrössen nachgewiesen (TVAV Art. 34). administrative Einteilungen: Die Datenebene enthält die administrativen Einteilungen einer Gemeinde bzw. eines Operates (Nummerierungsbereiche, Planeinteilungen und Toleranzstufeneinteilung). Datenformat INTERLIS (1 File pro Gemeinde) beinhaltet die folgenden Topics des Datenmodells der amtlichen Vermessung Aargau (DM.01-AV-AG), Version 25: FixpunkteKategorie1, FixpunkteKategorie2, FixpunkteKategorie3, Bodenbedeckung, Einzelobjekte, Hoehen, Nomenklatur, Liegenschaften, Rohrleitungen, Nummerierungsbereiche, Gemeindegrenzen, Bezirksgrenzen, Kantonsgrenzen, Landesgrenzen, Planeinteilungen, TSEinteilung, Rutschgebiete, Gebaeudeadressen, PLZOrtschaft, Planrahmen, Waldfeststellung (kantonale Option) und Mehrzweckkataster (kommunale Option). Datenformat Shape beinhaltet die obig aufgeführten Topics des Datenmodells der amtlichen Vermessung bzw. die folgenden Shapedateien (In Klammer ist der Geometrietyp angegeben): FP_Lagefixpunkte (Punkt), FP_Hoehenfixpunkte (Punkt), HGR_Hoheitsgrenzpunkte (Punkt), HGR_Gemeindegrenzen (Flaeche), HGR_Hoheitsgrenzabschnitte (Linie), GEB_Lokalisationsnamen (Punkt), GEB_Gebaeudeadressen (Punkt), AE_Mehrzweckkataster (Punkt), BB_ProjBoFlaechen (Flaeche), BB_ProjGebaeudenummern (Punkt), BB_ProjObjektnamen (Punkt), BB_BoFlaechen (Flaeche), BB_Gebaeudenummern (Punkt), BB_Objektnamen (Punkt), EO_Flaechenelemente (Flaeche), EO_Linienelemente (Linie), EO_Punktelemente (Punkt), EO_Objektnamen (Punkt), EO_Objektnummern (Punkt), RL_Rohrleitungen (Linie), LS_Grenzpunkte (Punkt), LS_ProjLiegenschaften (Flaeche), LS_ProjSelbstRechte (Flaeche), LS_Liegenschaften (Flaeche), LS_SelbstRechte (Flaeche), NK_Flurnamen (Flaeche) und NK_Nomenklatur (Punkt). Datenformat DXF Geobau beinhaltet die obig aufgeführten Topics des Datenmodells der amtlichen Vermessung in der Layerstruktur gemäss Datenreferenzmodell SN 612020.

de: AGIS Service Center Kanton Aargau

de: Das AGIS Service Center unterstützt als kantonale Koordinationsstelle die Bereitstellung von Geoinformationen und Metadaten der kantonalen Verwaltung. Die zuständigen kantonalen Fachstellen stellen die öffentlichen Geodaten des Kantons Aargau mittels der Infrastruktur des AGIS Service Center in der Informatik Aargau als Webservices und zum Download bereit.

political_level: canton

value: Aargau-Kt - Geocat Harvester

de: AV: Daten der amtlichen Vermessung, Datenmodell Aargau


de: Daten der amtlichen Vermessung im Datenmodell Bund (DM.01-AV-CH). Fixpunkte: Die Datenebene enthält die Lagefixpunkte der Kategorie 1 (Punkte der Landesvermessung), der Kategorie 2 (Punkte des Kantons) und der Kategorie 3 (Punkte der amtlichen Vermessung) sowie die Höhenfixpunkte der Kategorie 1 (Punkte des Landesnivellement), der Kategorie 2 (Punkte des kantonalen Nivellement) und der Kategorie 3 (Punkte von kommunalen Nivellementen). Die Punkte stellen den Bezug zum Koordinatensystem her. Die Höhen bzw. Gebrauchshöhen stützen sich auf das offizielle Höhensystem des Landesnivellement von 1902 (LN02) ab. Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lage- und Höhengenauigkeit der Fixpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 28). Die äussere Zuverlässigkeit eines jeden Fixpunktes wird durch geeignete statistische Kenngrössen nachgewiesen (TVAV Art. 34). Bodenbedeckung: Die Datenebene enthält die Bodenbedeckungsflächen (Gebäude, Strassen, Gewässer, Wald, etc.), welche flächendeckend als Gebietsaufteilung definiert sind, die Gebäude- bzw. Assekuranznummern und die Objektnamen (z.B. Gebäudebezeichnungen, Gewässernamen, etc.). Die Genauigkeitsanforderungen der Bodenbedeckungsflächen sowie die aufzunehmenden Minimalflächen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit von Einzelpunkten wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 29). Einzelobjekte: Die Datenebene enthält Einzelobjekte (z.B. Mauern, Brunnen, Masten, Brücken, etc.), welche als Punkt-, Linien- oder Flächenelemente dargestellt werden. Die Genauigkeitsanforderungen der Einzelobjekte richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Einzelpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 29). Nomenklatur: Die Datenebene enthält die Flur-, Orts- und Geländenamen. Die Flurnamen beziehen sich auf Geländeteile und sind flächendeckend als Gebietsaufteilung abgebildet. Die Ortsnamen beziehen sich auf abgegrenzte Geländeteile und überlagern die Flurnamen. Die Geländenamen stehen für einzelne Geländepunkte, welche nicht Orts- und Flurnamen sind und weisen keine Abgrenzung auf. Liegenschaften: Die Datenebene enthält die Grenzpunkte, die Grundstücke bzw. Parzellen und die selbständigen und dauernden Rechte soweit diese flächenmässig ausgeschieden werden können. Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Grenzpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Die Äussere Zuverlässigkeit der Grenzpunkte wird durch geeignete Kenngrössen nachgewiesen (TVAV Art. 34). Rohrleitungen: Die Datenebene enthält die Hochdruckleitungen für Gas. Die Genauigkeitsanforderungen der Rohrleitungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Einzelpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Gebäudeadressen: Die Datenebene enthält die Gebäudeadressen bestehend aus Haus- bzw. Polizeinummern sowie Strassen- und Platznamen. Hoheitsgrenzen: Die Datenebene enthält die Hoheitsgrenzpunkte und Hoheitsgrenzen (Gemeinde-, Bezirks-, Kantons- und Landesgrenze). Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Hoheitsgrenzpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Die Äussere Zuverlässigkeit der Hoheitsgrenzpunkte wird durch geeignete Kenngrössen nachgewiesen (TVAV Art. 34). administrative Einteilungen: Die Datenebene enthält die administrativen Einteilungen einer Gemeinde bzw. eines Operates (Nummerierungsbereiche, Planeinteilungen und Toleranzstufeneinteilung). Datenformat INTERLIS (1 File pro Gemeinde) beinhaltet die folgenden Topics des Datenmodells der amtlichen Vermessung Bund (DM.01-AV-CH), Version 24: FixpunkteKategorie1, FixpunkteKategorie2, FixpunkteKategorie3, Bodenbedeckung, Einzelobjekte, Hoehen, Nomenklatur, Liegenschaften, Rohrleitungen, Nummerierungsbereiche, Gemeindegrenzen, Bezirksgrenzen, Kantonsgrenzen, Landesgrenzen, Planeinteilungen, TSEinteilung, Rutschgebiete, Gebaeudeadressen, PLZOrtschaft und Planrahmen. Datenformat Shape beinhaltet die obig aufgeführten Topics des Datenmodells der amtlichen Vermessung bzw. die folgenden Shapedateien (In Klammer ist der Geometrietyp angegeben): FP_Lagefixpunkte (Punkt), FP_Hoehenfixpunkte (Punkt), HGR_Hoheitsgrenzpunkte (Punkt), HGR_Gemeindegrenzen (Flaeche), HGR_Hoheitsgrenzabschnitte (Linie), GEB_Lokalisationsnamen (Punkt), GEB_Gebaeudeadressen (Punkt), AE_Mehrzweckkataster (Punkt), BB_ProjBoFlaechen (Flaeche), BB_ProjGebaeudenummern (Punkt), BB_ProjObjektnamen (Punkt), BB_BoFlaechen (Flaeche), BB_Gebaeudenummern (Punkt), BB_Objektnamen (Punkt), EO_Flaechenelemente (Flaeche), EO_Linienelemente (Linie), EO_Punktelemente (Punkt), EO_Objektnamen (Punkt), EO_Objektnummern (Punkt), RL_Rohrleitungen (Linie), LS_Grenzpunkte (Punkt), LS_ProjLiegenschaften (Flaeche), LS_ProjSelbstRechte (Flaeche), LS_Liegenschaften (Flaeche), LS_SelbstRechte (Flaeche), NK_Flurnamen (Flaeche) und NK_Nomenklatur (Punkt). Datenformat DXF Geobau beinhaltet die obig aufgeführten Topics des Datenmodells der amtlichen Vermessung in der Layerstruktur gemäss Datenreferenzmodell SN 612020.

de: AV: Daten der amtlichen Vermessung, Datenmodell Bund