maintainer: Elisabeth Hafner

en: avalanche satellite natural-hazard snow hazard-mapping

en: SPOT6 Avalanche Outlines 16 January 2019

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

format: SHP

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

The generation of the data is described in:

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

The data was comprehensivly validated in a subset area in

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

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow

en: SPOT6 Avalanche outlines 16 January 2019


maintainer: Francesco Comola

en: cohesion sand saltation snow wind sediment

state: active

type: dataset

en: Output of the model simulations

media_type: application/zip

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

format: ZIP

mimetype: application/zip

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

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

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

cohesion

display_name: cohesion

saltation

display_name: saltation

sand

display_name: sand

sediment

display_name: sediment

wind

display_name: wind

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Saltation of cohesive granular materials


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

en: Registration in DORA

format: API

en: Corrigendum / Korrigendum

media_type: application/pdf

format: PDF

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

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

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

forest

display_name: forest

lfi

display_name: lfi

national-forest-inventory

display_name: national-forest-inventory

nfi

display_name: nfi


amount-of-dead-wood

title_for_slug: amount-of-dead-wood

format: SERVICE

en:
Wood volume of all deadwood recorded according to the NFI3 method. For standing trees and shrubs starting at 12 cm dbh, the volume of stemwood reduced due to stem breakage is recorded, and for lying deadwood the merchantable wood ( starting at 7 cm in diameter). Heaps of branches are not included.  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

amount-of-dead-wood

display_name: amount-of-dead-wood

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

economic-region

display_name: economic-region

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

ownership

display_name: ownership

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

en: Amount of dead wood


en: soil-solution-chemistry wdb long-term-forest-ecosystem-research swiss-forest-lab long-term-monitoring

en: Data request

media_type: text/csv

format: CSV

en:
Since 1997 soil solution samplers are installed in certain plots of the Intercantonal Forest Observation Program *[Intercantonal Forest Observation Program](https://www.envidat.ch/dataset/interkantonale-walddauerbeobachtung-wdb)*. There solution is analysed monthly at the moment (2023) in 46 Swiss forest sites. The sites cover a uniquely broad range of Swiss forest soils, with gradients in elevation (290-1870 m), precipitation (915-2211 mm), base saturation averaged over the top 40 cm (6-100%) and total N deposition (10-81 kg ha⁻¹a⁻¹).
For each site and soil depth, eight soil solution samplers (ceramic suction cup) are installed in the topsoil and five in the subsoil. Actual depths varied according to soil condition but a frequent sampling design is 20, 50 and 80 cm. Following the monthly sampling of the soil solution, the samples from the same location and from the same depth are pooled. Monthly data about following chemical soil solution indicators are measured:

 * pH and conductivity
 * Anions: Cl⁻, NO₃⁻, SO₂⁻
 * Kations: Ca²⁺, Mg²⁺, K⁺, Na⁺, Mn²⁺, NH4⁺, Altot, Alorg
 * DOC

More information about the analytical methods and results can be found in *[Braun & Tresch (2020)](https://doi.org/10.1371/journal.pone.0227530)*.
The long-term measurements of the soil solution provide the basis for revealing changes in forest soil quality. The chemical composition of the soil solution allows comparison and assessment of internationally agreed critical values and criteria on acid-base status (indicators: BC/Al ratio, pH value, N concentrations in the soil solution) as well as the leaching of N, Al and basic cations.

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

long-term-monitoring

display_name: long-term-monitoring

soil-solution-chemistry

display_name: soil-solution-chemistry

swiss-forest-lab

display_name: swiss-forest-lab

wdb

display_name: wdb

en: Soil solution monitoring of the Intercantonal Forest Observation Program


root-traits

maintainer: Ivano Brunner

title_for_slug: root-traits

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

en: Root-traits

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

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

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

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

belowground-biomass

display_name: belowground-biomass

drought

display_name: drought

fine-root-traits

display_name: fine-root-traits

ingrowth-cores

display_name: ingrowth-cores

soil-coring

display_name: soil-coring


carabid-beetles-in-forests

title_for_slug: carabid-beetles-in-forests

en: trapping forest carabidae running-beetles coleoptera insects arthropoda

media_type: text/plain

en: Tab-delimited data of the according sql query.

format: TXT

en: SQL query used to extract the data from fauna database.

en: Data extract in TAB-delimted format. First row is header, first column is counter.

en: Data query used on Fauna Database to retrieve the submitted data.

en:
Carabidae data from all historic up to the recent projects (21.10.2019) of WSL, collected with various methods in forests of different types.

Version 2 ('FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae') contains additional data field PROJ_FALLENBEZEICHNUNG.

Data are provided on request to contact person against bilateral agreement.

arthropoda

display_name: arthropoda

carabidae

display_name: carabidae

coleoptera

display_name: coleoptera

forest

display_name: forest

insects

display_name: insects

running-beetles

display_name: running-beetles

trapping

display_name: trapping

educ

en: Education, culture and sport

en: Carabid beetles in forests


maintainer: EnviDat Support

en: endemism madagascar geology landscape-evolution flora

en: Data v1.o

en: used for paper submission only

en: Escarpment evolution drives the diversification of the Madagascar flora

en: This upload contains the data (species checklist, species range maps, time-calibrated phylogenies, topographic change, etc.) generated, analyzed, and presented in the paper .

en: Although much of the endemic biodiversity of Madagascar can be attributed to its isolation as an island in the Indian Ocean, the high rates of speciation throughout its geologic history suggest an influence of local-scale landscape dynamics. The topographic evolution of Madagascar is dominated by the formation of high-relief continental rift escarpment and we argue that the erosion and landward retreat of this topography creates habitat heterogeneity that has served as a speciation pump for the island. The highest plant richness is found along the escarpment and is characterized by steady diversification rates over the last 45 Ma. Modeled landscape evolution by escarpment retreat demonstrates opportunities for allopatric speciation by transient habitat fragmentation through multiple mechanisms, including catchment expansion, isolation of highland remnants and formation of topographic and river barriers The segregation of floral phylogenetic turnover parallel to the escarpment is consistent with these mechanisms and indicates the importance of erosion-driven landscape dynamics on speciation.

endemism

display_name: endemism

flora

display_name: flora

geology

display_name: geology

landscape-evolution

display_name: landscape-evolution

madagascar

display_name: madagascar

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: climate-change water-supply water-scarcity reservoirs water-demand

en: Shapefile of 307 catchments in Switzerland

en: Shapefile of 307 catchments for which estimates of water supply, demand, and reservoir volumes are provided. The OBJECTID entry corresponds to the catchment ID which is used as a reference in the other datasets provided.

en: Water supply

en: Simulated discharge series for a reference period 1981-2010 and till the end of the century. Data are provided for 39 climate model chains and a control run based on the observed meteorology. Data are provided at daily resolution (mm/d).

en: Water demand (deprecated)

en:
Warning: please use the corrected version 2, since this file contains errors for certain categories such as Livestock, Industry or Houshold.

Water demand estimates for the 307 medium-sized catchments for different categories for current and future conditions. One text file is provided per demand category. One column corresponds to one catchment (IDs compare to shapefile), The data are provided at monthly resolution in mm/d and can be converted to volumes by using the catchment area. E.g.
Q[mm/d] * catchment area [m²] /1000 = Q[m³/d]
or to obtain discharge in m³/s
Q[mm/d] * catchment area [m²]/1000/24/3600 = Q [m³/s].
Future irrigation and hydropower water demand are provided per climate model chain.

en: Reservoir volumes

en: Reservoir volume for the 307 medium-sized catchments for different storage categories: natural lakes, artificial reservoirs, reservoirs for artificial snow production, drinking water reservoirs, and usable volumes of natural lakes. Unit: Mio m³. One column per catchment (see shapefile).

en: Water demand version 2 (corrected version)

en:
The dataset Hydro-CH2018 reservoirs provides estimates of current and future water supply, water demand, and storage volumes for 307 medium-sized catchments in Switzerland.
Water supply for current (1981-2010) and future (2070-2099) climate conditions was simulated using the hydrological model PREVAH. For modeling current water supply, observed meteorological time series were used as input, while simulated meteorological time series derived from 39 model chains of the CH2018 initiative were used as an input for simulating future climate conditions.

Water demand was estimated for six categories:

- 1) Drinking water (households and tourism),
- 2) industry (second and third sector),
- 3) artificial snow production,
- 4) agriculture (irrigation and livestock feeding),
- 5) ecology (residual flows), and
- 6) hydropower.

Future estimates consider changes in demand related to population growth and changes in the hydrological conditions.
Storage volumes are provided for natural lakes (storage capacities and usable volumes), artificial reservoirs, reservoirs for artificial snow production, and drinking reservoirs.

A detailed description of the simulation and estimation procedures can be found in

*  Brunner, M.I., Björnsen Gurung, A., Zappa, M., Zekollari, H., Farinotti, D., Stähli, M., 2019. Present and future water scarcity in Switzerland: Potential for alleviation through reservoirs and lakes. Sci. Total Environ. 666, 1033–1047. https://doi.org/10.1016/j.scitotenv.2019.02.169.

This dataset contains the following information:

1.	Shapefile with the 307 medium-sized Swiss catchments.
2.   Textfiles with the water supply simulations for the control run and the 39 climate model chains (one file per chain) at daily resolution for the 307 catchments.
3.	Textfiles with the current and future demand estimates per category at monthly resolution for the 307 catchments.
4.	Textfiles with the storage volumes per category and catchment.

climate-change

display_name: climate-change

reservoirs

display_name: reservoirs

water-demand

display_name: water-demand

water-scarcity

display_name: water-scarcity

water-supply

display_name: water-supply

en: Hydro-CH2018 reservoirs


maintainer: Irmi Seidl

en: community-energy cooperatives renewable-energy energy-cooperatives switzerland

en: Codebook

en: Description of variable attributes and valid n

mimetype: application/pdf

en: Raw date (SPSS)

en: Raw data in sav. format (SPSS). For comprehension of the data and variable structure, please consult the modified questionnaire with variable names.

en: Raw data (.csv)

en: Raw data in csv. format. For comprehension of the data and variable structure, please consult the codebook and the modified questionnaire with variable names.

mimetype: text/csv

en: Questionnaire with variable names

en: Modified questionnaire with variable names for each item. Should be used together with the data files (.csv or .sav) to reconstruct the data and variable structures.

en:
 Topic of Survey
The data at hand on energy cooperatives in Switzerland were collected in 2016 as part of the project "Collective financing of renewable energy projects in Switzerland and Germany" of the National Research Programme 71 "Managing Energy Consumption". The cooperatives were surveyed on their organizational structure, their activities in electricity and heat generation, their finances, the political context and their assessments of the future.

 Survey Method
The survey was targeted at all energy cooperatives in Switzerland (this is the basic population). The Swiss Commercial Register was searched for cooperatives and specific keywords in order to determine this basic population and collect addresses. This search in May 2016 resulted in a total of 304 energy cooperatives, to which a questionnaire was sent in July 2016. A pre-test with 8 persons had been carried out before the questionnaire was sent out. The questionnaire was provided in German and French. It was sent by mail and an attached letter referred to a link for the digital version if preferred. The online version was designed with the software "Sawtooth". After three weeks, a first, and after six weeks a second reminder letter was sent to those cooperatives that had not yet completed the questionnaire. The returned hardcopy questionnaires were manually entered into the database and then combined with the electronic data from the online survey. In the course of the survey, the total population was reduced from 304 to 289: in 4 cases the survey was not deliverable, 4 cooperatives had dissolved, 6 were not actually energy cooperatives, 1 case had recently changed its legal form. With a response rate of 47%, the final data set comprises 136 responses (from 77 digital and 59 hardcopy questionnaires). However, not all 136 of the returned questionnaires were filled out completely. We checked for answers that seemed contradictory or incomprehensible. If an error could be clearly identified and the correct answer derived, the answer was adjusted, otherwise the answer was replaced by "missing data".

 Anonymization
Participating cooperatives have been assured that their information will be kept confidential and will only be made public anonymously. For this reason, the data have been anonymized in in order to prevent any identification of individual cooperatives.

 How to Use the Data
* The data are available in CSV and SPSS (sav.) format.
* A codebook and a modified version of the used questionnaire are provided to illustrate the data and variable structure. In the questionnaire, the variable names are assigned to the corresponding questions. In the codebook, further information on these variables (valid n, answer categories) can be found. This information (of the codebook) is already integrated in the SPSS file.

 Current Embargo on Data
These data are currently under embargo and will only be released when the project is completed (not before 2020).

#Additional Information
* The used questionnaire is provided in German and French.
* Descriptive results of the survey were published in a WSL report: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:18943

community-energy

display_name: community-energy

cooperatives

display_name: cooperatives

energy-cooperatives

display_name: energy-cooperatives

renewable-energy

display_name: renewable-energy

switzerland

display_name: switzerland

educ

en: Education, culture and sport

en: Energy Cooperatives in Switzerland: Survey Results // Energiegenossenschaften in der Schweiz: Befragungsergebnisse


intratrait

maintainer: Christian Rixen

title_for_slug: intratrait

en: elevation-range plant-traits cold-adapted-plants elevation-gradient neighbour-interactions climate-change

en: Plant trait data along elevation gradients on mountains

en:
This data set was used to test whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries (Switzerland, Australia, New Zealand, China) in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species’ height ratio, its height relative to its nearest neighbour.
The following traits and parameters were measured and are available in this data set:
As an indication of plant stature, we measured vegetative and generative height, where vegetative height was distance from soil to highest vegetative leaf and generative height was distance to the highest point on the reproductive shoot. As a measure of reproductive investment, we noted the presence of flowers on the randomly chosen individuals (see below). As a measure of individual and genet basal area, we measured individual plant and patch diameters, in two dimensions (along the largest diameter and perpendicular to it). In clonal plant species, plant diameter was equivalent to an individual rosette, whereas patch diameter referred to the whole genet and could represent the size of a tuft, tussock or cushion. For genera with more singular growth forms (e.g., some Gentiana species) plant and patch diameter were the same. The two diameter measurements were made at right angles, allowing estimates of patch and plant areas to be calculated as an ellipse (i.e., area = 0.5 a 0.5 b Π). All traits were measured on ten randomly selected individuals per site. Flower count data was considered in a binary fashion on a per individual basis (because for some species individuals only produce one flower when flowering) so that the presence or absence of flower(s) was a nominal value between 0 and 10 for each species at each site. We then collected at least three leaves (up to 30 for small and light leaves) from each of the first three individuals selected from each species for determination of leaf dry matter content (LDMC) and specific leaf area (SLA). For calculations of LDMC and SLA, fresh leaves were scanned on a flatbed scanner to determine leaf area. Leaves were then weighed on a balance to a precision of +/- 0.001g, prior to being air dried and reweighed with a balance to a precision of +/- 0.0001g. LDMC was calculated by dividing dry leaf mass by fresh leaf mass. SLA was calculated by dividing leaf area by dry leaf mass. Additionally, within an area of 10 cm diameter around the target individual, we determined the tallest neighbouring species and measured its vegetative and generative height, and estimated the percent cover of the target species, other vegetation, rock, and bare soil.
For more details see Rixen et al. 2022, Journal of Ecology.

cold-adapted-plants

display_name: cold-adapted-plants

elevation-gradient

display_name: elevation-gradient

elevation-range

display_name: elevation-range

neighbour-interactions

display_name: neighbour-interactions

plant-traits

display_name: plant-traits

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: intratrait


maintainer: Dominique Weber

en: use-intensity grassland land-use management remote-sensing sentinel-2 landsat mowing

en: Preview number of grassland management events 2020

media_type: image/png

en: Spatial distribution of the number of management events for Switzerland and for three zoomed-in regions (a, b, c). The three zoomed-in regions show different grassland-use intensities, which are reflected in the distribution of management frequency (Weber et al., 2023; IN REVIEW)

format: PNG

en: Grassland-use intensity map 2018

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2018. See readme file for more details.

format: GeoTIFF

en: Grassland-use intensity map 2019

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2019. See readme file for more details.

en: Grassland-use intensity map 2021

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2021. See readme file for more details.

en: Readme

en: Metadata and description of the individual raster layers

en: Preview time of 1st grassland management event 2020

en: Spatial distribution of the timing (day of year; DOY) of the first management event for Switzerland and for three zoomed-in regions (a, b, c). The three zoomed-in regions show different grassland-use intensities, which are reflected in the timing of the first event of the year (Weber et al., 2023; IN REVIEW)

en: Grassland-use intensity map 2020

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2020. See readme file for more details.

en:
A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018–2021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework.

The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m × 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons.

We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see Weber et al., 2023; IN REVIEW).

grassland

display_name: grassland

land-use

display_name: land-use

landsat

display_name: landsat

management

display_name: management

mowing

display_name: mowing

remote-sensing

display_name: remote-sensing

use-intensity

display_name: use-intensity

en: Grassland-use intensity maps for Switzerland


maintainer: Stephanie Mayer

en: failure-layer snowpack-simulation snow-stratigraphy snow-instability snowpack snow-profiles rutschblock

en: DAV data set: Properties of observed snow profiles and simulated failure layers

en:
For each profile pair from the DAV data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. The content of the columns is explained in Table 1 of the ReadMe file. Only those profile pairs which fulfilled predefined similarity criteria (column
"similarity_obs_sim" = 1) and which belonged to the stability classes "stable" and "unstable" (column "stability_class"
= 0 or 1) were used to train the RF model. Please note that for each of the two snow profiles with RB score 7 (profile
IDs 7228 and 9526), three additional layers were included in this data set to obtain a balanced set of training data (73
unstable and 73 stable profiles).

en: Simulated snow profiles

en: Simulated snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding observed profile in the "observed_profiles" folder. One row corresponds to one simulated snow layer. The content of the columns 1-7 is described in Table 3 of the ReadMe file. The remaining columns (8-31) contain all features that were used in the development of the RF model and are described in Table 1 of the ReadMe file.

en: ReadMe

en: ReadMe file explaining data structure and content

en: Observed snow profiles

en: Observed snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding simulated profile in the "simulated_profiles" folder. One row corresponds to one observed snow layer. The content of the columns is described in Table 2 of the ReadMe file.

en: SWISS data set: Properties of observed snow profiles and simulated failure layers

en: For each profile pair from the SWISS data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. Please note that only those profile pairs which fulfilled the similarity criteria (column similarity_obs_sim = 1, 121 profiles) were used to validate the RF model. The content of the columns is explained in Table 1 of the ReadMe file.

en:
This data set includes information on all observed and simulated snow profiles that were used to train and validate the
random forest model described in Mayer et al. (2022). The RF model was trained to assess snow instability from simulated snow stratigraphy. The data set contains observed snow profiles from the region of Davos (DAV subset, 512 profiles) and from all over Switzerland (SWISS subset, 230 profiles). For each observed snow profile, there is a corresponding simulated profile which was obtained using meteorological input data for the numerical snow cover model SNOWPACK. The information on the observed snow profile contains a Rutschblock test result including the depth of the failure interface. As part of the study described in Mayer et al. (2022), each observed snow profile was manually compared to its simulated counterpart and the simulated layer corresponding to the Rutschblock failure layer was identified.

The data are provided in the following form: one file each per observed and simulated snow profile (2x512 files DAV, 2x230 files SWISS), two files (1 file DAV, 1 file SWISS) containing the observed information on snow instability, the allocation between observed and simulated failure layer, and all features extracted from the simulated weak layers that were used to develop the RF model.

failure-layer

display_name: failure-layer

rutschblock

display_name: rutschblock

snow-instability

display_name: snow-instability

snow-profiles

display_name: snow-profiles

snow-stratigraphy

display_name: snow-stratigraphy

snowpack

display_name: snowpack

snowpack-simulation

display_name: snowpack-simulation

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Observed and simulated snow profile data


maintainer: Yves Bühler

en: ramms release avalanche hazard-mapping hazard-indication-mapping snow

en: RAMMS simulations NHESS

en: Simulated maximum pressures for the scenarios frequent and extreme taking the forest layer into account

en: Potential Release  Area  NHESS

en: This folder contains the output of the different investigated algorithms plus the reference dataset, used for training and validation.

en:
This dataset contains the output and reference data published in the paper "Automated snow avalanche release area delineation - validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping" Yves Bühler, Daniel von Rickenbach, Andreas Stoffel, Stefan Margreth, Lukas Stoffel, Marc Christen (2018) Natural Hazards And Earth System Sciences.

Abstract:
Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such area-wide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-the-art numerical avalanche simulations.

In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 – 30 years return period) and extreme (100 – 300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and snow data are available.

avalanche

display_name: avalanche

hazard-indication-mapping

display_name: hazard-indication-mapping

hazard-mapping

display_name: hazard-mapping

ramms

display_name: ramms

release

display_name: release

snow

display_name: snow

en: Automated Avalanche Release Area (PRA) Delineation Davos


en: snow-water-equivalent snow-density snow-depth model-calibration-data model-validation-data

en: swe2hs calibration and validation data

en:
 Contents of this archive

 README.md

 A readme file containing a description of the other files.

 automatic_stations_validation_data.csv

 Daily data from the automatic weather stations used for validation.

 Data sources and providers are listed in the SWE2HS paper, Table 1.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * site_id: code for the measurement site
  - HS_interpolated: boolean flag for interpolated values
  - SWE_interpolated: : boolean flag for interpolated values

 coordinates_swe2hs_paper_stations.csv

 Coordinates of all measurement sites used in the SWE2HS paper.

 Columns:

  * site_id: code for the measurement site
  * location_name: location name of the measurement site
  * lon_[wgs84]: longitude in WGS84
  * lat_[wgs84]: latitude in WGS84
  * elevation_[m]: atitude of the measurement site in [m]
  * dataset: dataset in which the station is included

 manual_station_calibration_data.csv

 Calibration data from the Swiss manual observer stations. Long station records
 trmmed to 15 random water-years.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * SWE_from_profile_[m]: biweekly measurement from the SWE in the measurement
   pit. Corrected with the bulk density in order to match the snow depth from
   the measurement stake. Please refer to the paper for further details.
  * site_id: code for the measurement site

 manual_stations_validation_data.csv

 Validation data from the Swiss manual observer stations. Remaining water-years
 from the long sttaion record not used for the calibration data.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * SWE_from_profile_[m]: biweekly measurement from the SWE in the measurement
   pit. Corrected with the bulk density in order to match the snow depth from
   the measurement stake. Please refer to the paper for further details.
  * site_id: code for the measurement site

 manual_stations_used_deltasnow_parameters.csv

 Site specific DeltaSNOW model parameters used for the calculation of the daily
 SWE series in 'manual_stations_calibration_data.csv' and
 'manual_stations_validation_data.csv'.

 Parameters were optimized by reducing RMSE to the biweekly snow pit SWE measurements.
 Please refer to the paper for further details.

 A python implementation of the DeltaSNOW model was used for the calculation:
 pydeltasnow (https://pypi.org/project/pydeltasnow/)

 For a detailed description of the DeltaSNOW model parameters please see the
 respective publication:

 Winkler, M., Schellander, H., & Gruber, S. (2021). Snow water equivalents
 exclusively from snow depths and their temporal changes: the Δ snow model.
 Hydrology and Earth System Sciences, 25(3), 1165-1187.

 Columns:

  * site_id: code for the measurement site
  * rho_max: Maximum density of an individual snow layer produced by the DeltaSNOW model in [kg/m3]
  * rho_null: Fresh snow density for a newly created layer [kg/m3
  * c_ov: Overburden factor due to fresh snow [-]
  * k_ov: Defines the impact of the individual layer density on the compaction due to overburden [-]
  * k: Exponent of the exponential-law compaction [m3/kg]
  * tau: Uncertainty bound [m]
  * eta_null: Effective compactive viscosity of snow for “zero-density” [Pa s]

en:
The data in this repository was used for the calibration and validation of the
SWE2HS model in the following publication:
Aschauer, J., Michel, A., Jonas, T., & Marty, C. (2023). An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development Discussions, 1-19. https://doi.org/10.5194/gmd-2022-258
Contains daily snow water equivalent and snow depth timeseries from stations in the European Alps.

model-calibration-data

display_name: model-calibration-data

model-validation-data

display_name: model-validation-data

snow-density

display_name: snow-density

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

en: SWE2HS model calibration and validation data


title_for_slug: yield-of-live-bole-wood

en:
Volume of stemwood at least 7 cm in diameter (limit for coarse wood) without the bark and stump that were living trees or shrubs starting at 12 cm dbh in the pre-inventory and were cut between two inventories.

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)_

biogeographical-region

display_name: biogeographical-region

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

main-type-of-harvest

display_name: main-type-of-harvest

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

yield-of-live-bole-wood

display_name: yield-of-live-bole-wood

en: Yield of live bole wood


lwf-visp-long-term-research-site

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

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

en: LWF plot Visp. Photo: Peter Waldner, 2017

en: Visp Meteorological Data LWF

en:
![alt text](https://www.envidat.ch/dataset/1a43f9fa-e36c-46b9-a409-367fce3ce48b/resource/757c846b-c266-4fd4-b6ad-5f5f327ffcb4/download/lwf_visp.jpg "LWF Visp")

LWF Plot Visp

- Community: Visp / canton VS
- Date of installation: 13 March 1996
- Size of the plot: 2 ha
- Altitude: 657-733 m
- Mean slope: 80%
- Geology (in German): Penninikum, Jura, Bündnerschiefer; Kalkphyllite, Hängeschutt
- Provisional soil type (WSL): Calcaric Cambisol
- Woodland association after EK72: =~= 38: Arabidi turritae-Quercetum pubescentis
- Main tree species: Pinus sylvestris
- Management system: high forest
- Silvicultural system: unmanaged
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 53.9 cm
- Number of trees BHD >= 12 cm (2011): 650
- Maximum tree age: Pinus sylvestris uneven-aged 40-80 yr

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

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: LWF Visp long-term research site


deadwood-generator

title_for_slug: deadwood-generator

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

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.

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: 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

rockfall

display_name: rockfall

windthrow

display_name: windthrow

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: Deadwood Generator


en: forest-snow uav snow-modelling climate albedo

en: Sodankylä (FIN) pine 2019

en: UAV-based LSA data collected in Sodankylae (FIN) between April and May 2019. The zip file contains one csv file per waypoint location, please refer to the following paper on further information on waypoint locations etc. (to be updated once published): Malle et al. (2021): Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements in Journal of Geophysical Research: Atmospheres (under review).

en: Davos Laret 2018

en: UAV-based LSA data collected in Davos Laret between January and May 2018. The zip file contains one csv file per waypoint location, please refer to the following paper on further information on waypoint locations etc. (to be updated once published): Malle et al. (2021): Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements in Journal of Geophysical Research: Atmospheres (under review).

en:
This data-set contains Land Surface Albedo (LSA) data obtained via a UAV sytem with up and downlooking shortwave radiation sensors, as described in the JGR-Atmospheres paper "Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements", by Malle et al. (2021, under review). This publication must be cited when using the data.

Data was collected across a large range of forest structures and solar angles in Switzerland (Davos Laret) and in Finland (Sodankylä). For each waypoint location at each site, data includes measured LSA, incoming SWR, reflected SWR and sunlit snow-view fraction alongside zenith angle, azimuth angle and measurement time (local time). Please refer to the abovementioned article for more detailed explanation.

albedo

display_name: albedo

climate

display_name: climate

forest-snow

display_name: forest-snow

snow-modelling

display_name: snow-modelling

uav

display_name: uav

en: Wintertime UAV-based land surface albedo data over forested environments


meteorological-measurements-lwf

title_for_slug: meteorological-measurements-lwf

en: lwf swiss-forest-lab icp-forests meteorological-measurements long-term-forest-ecosystem-research

en: Data Request

en:
Continuous measurement of air temperature, relative humidity, wind speed and direction, global radiation, photosynthetic active radiation, UVB radiation, and precipation in an open field very close to the LWF plot as well as air temperature, relative humidity, wind speed, photosynthetic active radiation, and precipitation in the forest below the canopy.

 Purpose:

Recording meteorological conditions

 Manual Citation:

 * Martine Rebetez, Gustav Schneiter, 1997: Meteorologie. In: Brang P. (ed.) Aufnahmeanleitung LWF. Langfristige Waldökosystem-Forschung LWF, 4 S.

 * Raspe S, Beuker E, Preuhsler T, Bastrup-Birk A, 2016: Part IX: Meteorological Measurements. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest Ecosystems, Eberswalde, Germany, 14 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)

 Paper Citation:

 * Martine Rebetez, Georg von Arx, Arthur Gessler, Elisabeth Graf Pannatier, John L. Innes, Peter Jakob, Markéta Jetel, Marlen Kube, Magdalena Nötzli, Marcus Schaub, Maria Schmitt, Flurin Sutter, Anne Thimonier, Peter Waldner, Matthias Haeni, 2018: Meteorological data series from Swiss long-term forest ecosystem research plots since 1997. Annals of Forests Science 75: 41: 1-7. [doi: 10.1007/s13595-018-0709-7](https://doi.org/10.1007/s13595-018-0709-7)

 * Haeni, Matthias; von Arx, Georg; Gessler, Arthur; Graf Pannatier, Elisabeth; Innes, John L; Jakob, Peter; Jetel, Markéta; Kube, Marlen; Nötzli, Magdalena; Schaub, Marcus; Schmitt, Maria; Sutter, Flurin; Thimonier, Anne; Waldner, Peter; Rebetez, Martine (2016): Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF) in Switzerland, from 1996-2016. PANGAEA, [doi: 10.1594/PANGAEA.868390](https://doi.org/10.1594/PANGAEA.868390)

 * Gustav Schneiter, Peter Jakob, Martine Rebetez, 2004: Sieben Jahre meteorologische Datenerfassung im Schweizer Wald. Infoblatt Forschungsbereich Wald, Vol 17: 4-6 [>>>](https://www.parcs.ch/snp/pdf_public/2011_schneiteretal_datenerf_wald_wsl_2004.pdf)

 * Jakob P, Sutter F, Waldner P, Schneiter G (2007) Processing remote gauging-data. In: Gomez J. M., Sonnenschein M., Müller M., Welsch H., Rautenstrauch C. (ed.) Information Technologies in Environmental Engineering ITEE 2007, Third International ICSC Symposium, Springer, Berlin, Heidelberg, 211-220.

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Meteorological measurements LWF


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

en: Data in NetCDF4 CF-1.6 format

en: Link to Related Publication

en: Explanatory Document

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

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

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

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

antartic

display_name: antartic

climate-change

display_name: climate-change

cryospheric-science

display_name: cryospheric-science

forcing

display_name: forcing

freezing

display_name: freezing

freshwater-flux

display_name: freshwater-flux

mass-balance

display_name: mass-balance

melting

display_name: melting

physical-oceanography

display_name: physical-oceanography

polar

display_name: polar

salinity

display_name: salinity

salt

display_name: salt

satellite

display_name: satellite

sea-ice

display_name: sea-ice

sea-ice-divergence

display_name: sea-ice-divergence

sea-ice-volume

display_name: sea-ice-volume

southern-ocean

display_name: southern-ocean

transport

display_name: transport

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


en: biomass protection-forest national-forest-inventory carbon volume biodiversity

en: Lanz 2014

en: A short overview over stump assessment (field protocol) in some European NFIs

en: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI3

en: Data on dead standing and downed trees for the NFI1

en: Artikel - Annals of Forest Science

en: Site Description

en: information on NFI sites throughout the five NFIs considered in this study

en: Data on dead standing and downed trees for the NFI2

en: Files and contents

en: Variable definitions for the datasets NFI1, NFI2, NFI3, NFI4 and NFI5

en: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI4

en: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI5

en:
Based on the detailed tree stump inventory implemented in the Swiss NFI5 (https://www.lfi.ch/lfi/lfi.php), a study was conducted to obtain an accurate assessment of the stumps pool in the Swiss NFI over the last 30 years and to identify its significance for the total dead wood (DW) pool. The NFI5 includes a detailed stump inventory to improve accuracy and completeness of the above-ground DW- pool. Based on available data, stump volume estimates were derived at different accuracies to evaluate the contribution to the total DW-pool over time.
The study found that in Swiss Forests the contribution of stumps to total DW-pool is approximately 25%, and that applying simplifying assumptions to estimate stump volume can result in a significant underestimation of the true size of this pool. This study demonstrates that stumps can be a significant proportion of DW in forests, which should be accounted for in order to improve accuracy and completeness of NFI estimates and derived data such as C stocks for greenhouse gas reporting.

The study is published in Annals of Forest Science (2022) 79:34, https://doi.org/10.1186/s13595-022-01155-7 (open access).
The data can be obtained from the authors upon reasonable request.

biodiversity

display_name: biodiversity

biomass

display_name: biomass

carbon

display_name: carbon

national-forest-inventory

display_name: national-forest-inventory

protection-forest

display_name: protection-forest

volume

display_name: volume

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: Stumps as a dead wood resource in forests - data based on the Swiss National Forest Inventory


en: climatol long-term-trends interpqm snow-depth extreme-values homogenisation

en: data impact analyses

en:
 data frames containing results from the impact analyses:
* Homer
* Climatol
* interpQM
* original
* trends
* HSavg and HSmax
* R50maxHS

en:
 Input data for the following research article: Impact assessment of homogenised snow depth series on trends
The data consists of separate output files from the following homogenisation methods:
* Climatol
* HOMER
* interpQM
The variable is seasonal mean snow depth (HSavg)

plot.data is an additional data frame containing trends of HSavg (station, method, value, pvalue, altitude)

climatol

display_name: climatol

extreme-values

display_name: extreme-values

homogenisation

display_name: homogenisation

interpqm

display_name: interpqm

long-term-trends

display_name: long-term-trends

snow-depth

display_name: snow-depth

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Input data for impact assessment of homogenised snow series


en: scolytinae spruce antagonists predators bivoltine parasitoids univoltine ips-typographus

en: Bark beetle infestation spots

en: see ReadMe

en: In an expanding bark beetle (Ips typographus) infestation spot emergence traps were installed on the stems of newly infested spruce trees capturing all emerging insects during several consecutive years. Two locations were sampled on elavations with univoltine and bivoltine generations, respectively. Bark beetles and their insect predators and parasitoids were identified to species level by specialists.

antagonists

display_name: antagonists

bivoltine

display_name: bivoltine

ips-typographus

display_name: ips-typographus

parasitoids

display_name: parasitoids

predators

display_name: predators

scolytinae

display_name: scolytinae

spruce

display_name: spruce

univoltine

display_name: univoltine

en: Emergence dynamics of natural enemies of spruce bark beetles


en: natural-tracer weissfluhjoch post-depositional-processes paleoclimate sublimation stable-water-isotopes latent-heat snow

en: Folder containing the scripts and data used in "Surface snow isotopic composition: post-depositional processes in an alpine snowpack" (Sadowski et al., 2022)

en: Stable water isotopes in snow

en: Stable water isotopes in snow measurements

en: Weather

en: Meteorological data, snow height measurement, and SNOWPACK simulations covering the different field campaigns.

en:
Details on the data collected during the different field campaigns and available in this dataset.
Information on the sampling procedure of the snow isotopes samples.

en: Stable water isotopes in vapor

en:
Stable water isotopes in vapor measurements. Processed (calibrated) and raw data for February - June 2017. Raw data for January and February 2020.
For the time in between July 2017 and April 2020, raw vapor isotopic measurements are available, but no calibrations were performed during that time. For data delivery, please contact one of the authors.
This folder also contains the post-processing scripts for stable water isotopes in vapor measurements.

en: Details about the different folders

en:
Notice: Changes to the dataset are still possible. Please do not use this dataset until the final publication with a DOI. Contact the authors if you have questions about this.

This dataset contains measurements of stable water isotopes in snow and vapor on the Weissfluhjoch from different field campaigns (Winter 2017 (Trachsel, 2019), January 2020, December 2020, and March 2021 (Sadowski et al., 2022).
Snow profiles and surface samples are available at different frequencies for each campaign. Please see "Data_description.pdf" for details.

Scripts and SNOWPACK simulations used in (Trachsel, 2019) and (Sadowski et al., 2022) are also provided.

latent-heat

display_name: latent-heat

natural-tracer

display_name: natural-tracer

paleoclimate

display_name: paleoclimate

post-depositional-processes

display_name: post-depositional-processes

snow

display_name: snow

stable-water-isotopes

display_name: stable-water-isotopes

sublimation

display_name: sublimation

weissfluhjoch

display_name: weissfluhjoch

en: Stable Water Isotopes in snow and vapor on the Weissfluhjoch


en: remote-sensing swe uas snow-depth snow

en: .tfw file

en: coordinates for the .tif raster

en: HS map Brämabül, 14. April 2015

media_type: image/tiff

en:
10 cm resolution
15 cm accuracy

format: TIFF

mimetype: image/tiff

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-depth

display_name: snow-depth

swe

display_name: swe

uas

display_name: uas

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: UAS based snow depth maps Brämabüel, Davos, CH


mosaic-snow-on-sea-ice-data

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

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

en: ALPINE3D setup for MOSAiC

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

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

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

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

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

en: SNOWPACK setup for MOSAiC

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

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

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

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

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

arctic-sea-ice

display_name: arctic-sea-ice

drifting-and-blowing-snow

display_name: drifting-and-blowing-snow

snow-distribution

display_name: snow-distribution

spatial-variability

display_name: spatial-variability

surface-mass-balance

display_name: surface-mass-balance

en: MOSAIC Snow on Sea Ice Data


lwf-schanis-long-term-research-site

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

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

en: Schänis Meteorological Data LWF

en: LWF plot Schänis. Photo: Anne Thimonier Rickenmann, 2019

en:
![alt text](https://www.envidat.ch/dataset/b21e7c90-7d1f-4940-82ab-d29c0dcf5fcf/resource/5b41f2a1-847f-4a0f-aa48-cd530ace3827/download/lwf_schaenis.jpg "LWF Schänis")

LWF Plot Schänis

- Community: Schänis / canton SG
- Date of installation: 17 September 1997
- Size of the plot: 2 ha
- Altitude: 693-773 m
- Mean slope: 60%
- Geology (in German): Tertiär. Subalpine Molasse, Oligocaen, Chattien, Kalknagelfluh
- Soil types (WSL) : n.d.
- Woodland association after EK72: 13: Cardamino-Fagetum tilietosum
- Main tree species: Fagus sylvatica
- Management system: high forest
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 55.8 cm
- Number of trees BHD >= 12 cm (2011): 611
- Maximum tree age: Abies alba130-150 yr - Fraxinus excelsior 130-150 yr - Fagus sylvatica 130-150 yr

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

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

en: LWF Schänis long-term research site


en: swiss-forest-lab forest-growth-model 3-pg callibration forest-productivity forest-biomass

en: link to 3PG github R package

en: A package source code (development version).

en: r3PG source files v 0.1.2

en: link to 3PG cran R package

en: A package source code (latest release).

en: An R Computing Software package which provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG, a new Fortran implementation of 3-PG, serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) <doi:10.1016/S0378-1127(97)00026-1> and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) <doi:10.1016/j.ecolmodel.2015.07.010>.

callibration

display_name: callibration

forest-biomass

display_name: forest-biomass

forest-growth-model

display_name: forest-growth-model

forest-productivity

display_name: forest-productivity

en: r3PG – An r package for simulating forest growth


maintainer: EnviDat

en: nead csv metadata-convention meteorological-data-format research-data-management

en: MeteoIO (future reference implementation)

media_type: text/html

format: HTML

mimetype: text/html

en: NEAD on GitHub

en: PyNEAD (future reference implementation)

en: NEAD syntax v0.1 (EBNF)

mimetype: text/plain

en:
Acknowledgement: The NEAD format includes NetCDF metadata and is proudly inspired by both SMET and NetCDF formats. NEAD is designed as a long-term data preservation and exchange format.

The NEAD specifications were presented at the "WMO Data Conference 2020 - Earth System Data Exchange in the 21st Century" (Virtual Conference).

-----------------------

Summary:
The Non-Binary Environmental Data Archive (NEAD) format is being developed as a generic and intuitive format that combines the self-documenting features of NetCDF with human readable and writeable features of CSV. It is designed for exchange and preservation of time series data in environmental data repositories.

License: The NEAD specifications are released to the public domain under a Creative Commons CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

csv

display_name: csv

metadata-convention

display_name: metadata-convention

meteorological-data-format

display_name: meteorological-data-format

nead

display_name: nead

research-data-management

display_name: research-data-management

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: Non-Binary Environmental Archive Data (NEAD) format


maintainer: Yaquan Chang

en: phytodiversity model-residuals habitat-heterogeneity hypervolume hengduan-mountains

en: Plant range map at Hengduan scale

en: This is the plant range map at the Hengduan mountain scale based on species distribution model, elevation, and county level distribution information

en: Eurasia plant family richness map

en: This is the plant family level richness map at the Eurasia scale calculated from https://www.nature.com/articles/s41597-021-00997-6

en: Hengduan plant family richness map

en: This is the family level plant richness map in the Hengduan mountain scale at 1km resoution

en: HDM heterogeneity scales

en: The Heterogeneity matrics in the Hengduan mountain scale

en: Eurasia SR and heteorgeneity

en: This table is used for the Eurasia scale analyses, including both family level species richness map and different heterogeneity predictors

en: This research data is used to re includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions. This research data includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions.

habitat-heterogeneity

display_name: habitat-heterogeneity

hengduan-mountains

display_name: hengduan-mountains

hypervolume

display_name: hypervolume

model-residuals

display_name: model-residuals

phytodiversity

display_name: phytodiversity

en: Phytodiversity is associated with habitat heterogeneity from Eurasia to Hengduan


maintainer: Robert Pazúr

en: sentinel-2 cropland machine-learning phenology switzerland grassland vegetation-indices agriculture habitat remote-sensing

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6  (v1.0)

en:
!!! Please check out the newer version in the repository !!!
Mask of unsealed surfaces of Switzerland based on 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6

en: Cropland and grassland map of Switzerland  (v.1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; other LC classes are masked out from the map

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.0)

en: !!! Please check out the newer version in the repository !!! The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6 (v1.5)

en: Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6.

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.5)

en: (current version) Cropland, grassland, and shrubland map of Switzerland (v.1.5)

en: (current version) Cropland and grassland map of Switzerland (v.1.5)

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.0)

en: !!! Please check out the newer version in this repository !!! Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.7.

en: Cropland, grassland, and shrubland map of Switzerland (v1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; (3) shrubland; other LC classes are masked out from the map

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.5)

en: The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland. Compare to the previous version the newer version includes the shadow masking procedure which might be important in the mountainous areas.

en: We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland, sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

agriculture

display_name: agriculture

cropland

display_name: cropland

grassland

display_name: grassland

habitat

display_name: habitat

machine-learning

display_name: machine-learning

phenology

display_name: phenology

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-indices

display_name: vegetation-indices

educ

en: Education, culture and sport

en: Cropland and grassland map of Switzerland based on Sentinel-2 data


photogrammetric-drone-data-dorfberg

title_for_slug: photogrammetric-drone-data-dorfberg

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

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

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: Photogrammetric Drone Data Dorfberg


maintainer: Nander Wever

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

en: Documentation SnowMicroPen measurements

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

en: Jupyter notebook for processing the SnowMicroPen files.

en: Manual snow pits and other snow density measurements

en: List of SnowMicroPen measurements

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

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

en: SnowMicroPen pts files and processed txt files

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

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

antarctica

display_name: antarctica

microstructure

display_name: microstructure

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen

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


en: strategic-planning land-change land-use-planning plan-evaluation copenhagen

en: The present dataset is the summary of each planning intention (name, type, development pattern, land use, and link with governance and supra regionel conditions), the explanation of interest, and the mapping from plan.

copenhagen

display_name: copenhagen

land-change

display_name: land-change

land-use-planning

display_name: land-use-planning

plan-evaluation

display_name: plan-evaluation

strategic-planning

display_name: strategic-planning

educ

en: Education, culture and sport

en: Planning intention in Copenhagen urban region


young-forest-with-browsing-damage

title_for_slug: young-forest-with-browsing-damage

en:
Number of regeneration trees where browsing of the shoots from the previous year was recorded in NFI’s regeneration survey.

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)_

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

avalanches-silvaprotect

display_name: avalanches-silvaprotect

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

economic-region

display_name: economic-region

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

main-tree-species-in-young-forest

display_name: main-tree-species-in-young-forest

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

stem-count-of-young-forest

display_name: stem-count-of-young-forest

total-number

display_name: total-number

young-forest-with-browsing-damage

display_name: young-forest-with-browsing-damage

young-plants

display_name: young-plants

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: Young forest with browsing damage


hymenoptera

title_for_slug: hymenoptera

en: urban forests arthropoda hymenoptera hautfluger agricultural

en: Data extract in TAB-delimted format. First row is header, first column is counter.

en: Data query used on Fauna Database to retrieve the submitted data.

en:
Hymenopteran data from all historic up to the recent projects (29.10.2019) of WSL, collected with various standardized methods in landscapes of different types.

Data are provided on request to contact person against bilateral agreement.

agricultural

display_name: agricultural

arthropoda

display_name: arthropoda

forests

display_name: forests

hautfluger

display_name: hautfluger

hymenoptera

display_name: hymenoptera

urban

display_name: urban

en: Hymenoptera


maintainer: Marcus Schaub

en: forest crown-condition dbh swiss-forest-lab irrigation scots-pine lwf diameter-at-breast-height tree drought tree-species

en: Site photo 1

en: Site photo 3

en: Shapefile	of measurement location

en: Dataset

format: XLS

en: Long-term Irrigation	Experiment Pfynwald homepage

en: Swiss Forest Health Inventory

en: Site photo 2

en: To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46° 18' N, 7° 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2°C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years.

crown-condition

display_name: crown-condition

dbh

display_name: dbh

diameter-at-breast-height

display_name: diameter-at-breast-height

drought

display_name: drought

forest

display_name: forest

irrigation

display_name: irrigation

lwf

display_name: lwf

scots-pine

display_name: scots-pine

swiss-forest-lab

display_name: swiss-forest-lab

tree

display_name: tree

tree-species

display_name: tree-species

en: Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland


en: nitrogen swiss-forest-lab passive-sampling ammonia ambient-air-quality icp-forests long-term-forest-ecosystem-research

en: Data Request

en:
NH₃ concentrations were measured at 11 LWF plots (1999/2000) with Zürcher passive samplers (Palmes-type diffusion tubes with an acidic solution as absorbent) during one year. Three samplers per site and period (usually 14 days) were installed in the open area of the LWF plots and, at Beatenberg, Novaggio and Vordemwald, under the forest as well (weather station). In 2014, NH₃ concentrations were measured again at 14 plots, using two Radiello samplers per site and period (usually 28 days). At Lausanne and Vordemwald, concentrations were also measured below the canopy.

 Purpose:

To assess air concentrations of ammonia (NH₃) and, using deposition velocities available from the literature, to quantify the dry deposition of NH₃ (alternative method to the throughfall method). The LWF plots were part of a larger network covering the main regions of Switzerland. One objective of this larger network was to compare measured and modelled concentrations.

ambient-air-quality

display_name: ambient-air-quality

ammonia

display_name: ammonia

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

nitrogen

display_name: nitrogen

passive-sampling

display_name: passive-sampling

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: Passive sampling of NH3 LWF


en: multifunctional-forestry integrative-forest-management europe biodiversity timber-production

en: Link to DORA WSL

en: How to balance forestry and biodiversity conservation? A view across Europe.

en:
Forests cover about a third of the European land area and provide a multitude of ecosystem services. They are subject to constant change and increasing demands with respect to the ecosystem services they provide. As well as provision of timber, non-timber forest goods (e.g. berries and mushrooms), clean water, carbon sequestration, and protection against natural disasters, there is increasing emphasis being placed on conservation and promotion of biodiversity within forests. However, the state of biodiversity in European forests varies widely. Because of the different biogeographic, sociocultural, political, and climatic conditions at small scales, there is a wide variety of situations, approaches, and legacies across the continent.
This book strives to show the challenges that forest managers are faced with to fulfil the societal demands with regard to forests, and especially to integrate the promotion of biodiversity. In a first section (12 chapters), 44 authors from science, policy, and practice describe the driving factors of forest management; these factors include national laws and legacies, ownership structures, forest history, and socio-structural conditions. In a second section, 113 authors present 32 case examples from 19 European countries that demonstrate the different approaches to integrate the locally requested ecosystem services. These experiences are synthesised in a final section, and a toolbox of integrative measures is presented that is intended to support forest owners and managers to choose appropriate measures for targeted integrated management in their forests.
Copyright reserved WSL and EFI.

biodiversity

display_name: biodiversity

europe

display_name: europe

integrative-forest-management

display_name: integrative-forest-management

multifunctional-forestry

display_name: multifunctional-forestry

timber-production

display_name: timber-production

en: How to balance forestry and biodiversity conservation? - A view across Europe


en: pests picea insects predators parasitoids spruce bark-beetles antagonists

en: See ReadMe file

en:
In 1994 a large area of mountain spruce forest was infested by the European spruce bark beetle (Ips typographus) in the Gandberg forest near Schwanden, canton Glarus, Switzerland (46.99145 N, 9.10768 E, 1300 m a.s.l.). In a perimeter of approx. 13 ha, 50 infested dead spruce trees were selected and labelled in 1994. The trees were randomly distributed across the whole perimeter and attributed to 5 groups of 10 trees of approx. 25-40 cm diameter each. In each of the following 5 years (1995-1999), the trees of one of these groups were cut in early spring and transported by helicopter to a vehicle-accessible road. Of each log, two bolts of 1.5 m length were cut, one from the base and one from the beginning of the crown. The bolts were transported by truck to the institute WSL and exposed in emergence eclectors (metal cabinets of approx. 2.0x0.5x0.5 m) in a greenhouse to let the insects emerge. Each tree was left 2 years in the eclectors to allow insects with more than 1 year development time to emerge. During 2 months in the winter between the two exposure years the bolts were removed from the eclectors and exposed to ambient winter temperatures for chilling. They were then moved back to the eclectors in the greenhouse.
Additionally, 18 living unattacked trees were provided with a pheromone lure in early spring 1995 to induce new bark beetle attack. 10 infested trees were then cut and processed as described above.
The water-filled emergence traps of the eclectors were emptied monthly-bimonthly and the insects were separated to taxonomic groups and eventually identified by specialists. Before disposing the logs, tree age was recorded by tree-ring-counting.

antagonists

display_name: antagonists

bark-beetles

display_name: bark-beetles

insects

display_name: insects

parasitoids

display_name: parasitoids

pests

display_name: pests

picea

display_name: picea

predators

display_name: predators

spruce

display_name: spruce

en: Dynamics of insect natural enemies of bark beetles


maintainer: Andrin Caviezel

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

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

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

en: Induced Rockfall Data, Tschamut, GR

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

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

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

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

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

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

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

Further information is found in

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

dem

display_name: dem

deposition-points

display_name: deposition-points

induced-rockfall

display_name: induced-rockfall

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

sensor-stream

display_name: sensor-stream

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata

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