en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh removed from the forest due to damage occurring (e.g. windthrow, avalanches, insects or rockfall), and not as the result of management planning. This feature is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).  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)_

avalanches-silvaprotect

display_name: avalanches-silvaprotect

biogeographical-region

display_name: biogeographical-region

cause-of-salvage-logging

display_name: cause-of-salvage-logging

economic-region

display_name: economic-region

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

main-type-of-harvest

display_name: main-type-of-harvest

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

rockfall-silvaprotect

display_name: rockfall-silvaprotect

salvage-logging

display_name: salvage-logging

educ

en: Education, culture and sport

en: Salvage logging


root-traits

maintainer: Ivano Brunner

title_for_slug: root-traits

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

state: active

type: dataset

en: Root-traits

media_type: application/zip

format: ZIP

mimetype: application/zip

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

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester


maintainer: Frank Hagedorn

en: phosphorus dissolved-organic-phosphorus seasons fertilization forest-topsoil beech zero-tension-lysimeters soil-temperature nutrient-fluxes nitrogen

format: XLS

en:
Data on dissolved organic and inorganic phosphorus and nitrogen concentrations in leachates and their corresponding
fluxes from the litter layer, the Oe/Oa horizon, and the A horizon of two German beech forest sites.
Leachate samples were taken in April 2018, July 2018, October 2018, Feb./Mar. 2019, and July 2019
with zero-tension lysimeters after artificial irrigation. Soil samples were taken in July 2019.
For more details please refer to the publication.

beech

display_name: beech

dissolved-organic-phosphorus

display_name: dissolved-organic-phosphorus

fertilization

display_name: fertilization

forest-topsoil

display_name: forest-topsoil

nitrogen

display_name: nitrogen

nutrient-fluxes

display_name: nutrient-fluxes

phosphorus

display_name: phosphorus

seasons

display_name: seasons

soil-temperature

display_name: soil-temperature

zero-tension-lysimeters

display_name: zero-tension-lysimeters

en: Phosphorus and nitrogen leaching from beech forest soils


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

en: Link to DORA WSL

format: SERVICE

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

media_type: application/pdf

format: PDF

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: slab-avalanche propagation-saw-test snow whumpf weak-layer dynamic-crack-propagation crack-speed

en: Readme

media_type: text/plain

en: Information how to interpret the data in "crack propagation speeds.zip".

format: TXT

en: crack propagation speeds

en: Please find the readme file.

en:
For the release of a slab avalanche, crack propagation within a weak snowpack layer below a cohesive snow slab is required. As crack speed measurements can give insight into the underlying processes, we analysed three crack propagation events that occurred in similar snowpacks and covered all scales relevant for avalanche release. For the largest scale, up to 400 m, we estimated crack speed from an avalanche movie, for scales between 5 and 25 meters, we used accelerometers placed on the snow surface, and for scales below 5 meters, we performed a Propagation Saw Test. The mean crack speeds ranged from 36 ± 6 to 49 ± 5 m s^{-1}, and did not exhibit scale dependence. Using the Discrete Element Method and the Material Point Method, we reproduced the measured crack speeds reasonably well, in particular the terminal crack speed observed at smaller scales.
This dataset includes raw data as well as crack speed estimates from the three crack propagation events.
Where possible, we reproduced these field experiments with numerical models based on Discrete Element Method (DEM, Bobillier and others, 2020 and 2021) and Material Point Method (MPM. Gaume and others, 2018 and Trottet and others, 2021). The input parameters of the models were estimated from the corresponding snow profiles conducted at each test site.

 The raw data include:
* Propagation Saw Test movie with mechanical fields derived from Digital image Correlation analysis of the recording
* Acceleration data recorded with wireless time synchronized accelerometers placed on the snow surface during crack propagation in a whumpf.
*Video of an artificially triggered avalanche with widespread crack propagation. The video was used to georeference surface cracks in order to estimate crack propagation time and distance, providing crack propagation speed estimates.
* Snow profile recorded at each test site

 Experimental crack speed estimates include:
* Crack speed evolution within the first meters derived from the Propagation Saw Test.
* Crack speeds estimated from the time delay of the collapse, observed between different accelerometers during crack propagation of a whumpf.
* Crack speed estimates from video analysis of the artificially triggered avalanche.

 Reproduced crack speeds using the DEM an MPM model:
* Modelled Propagation Saw Test using MPM (2D and 3D system) and DEM.
* Modelled whumpf using MPM (beam and areal configuration)
* Modelled avalanche using MPM (beam and areal configuration)

Beside the movies (mp4 format), all data is either provided as netCDF files or excel sheets (see readme file), depending on the amount of data. A detailed description of the three crack propagation events and how crack speed was derived, can be found in the related publication:

 References for applied models:
Bobillier, G., B. Bergfeld, A. Capelli, J. Dual, J. Gaume, A. van Herwijnen and J. Schweizer 2020. Micromechanical modeling of snow failure. The Cryosphere, 14(1): 39-49.

Bobillier, G., B. Bergfeld, J. Dual, J. Gaume, A. van Herwijnen and J. Schweizer 2021. Micro-mechanical insights into the dynamics of crack propagation in snow fracture experiments. Scientific Reports, 11: 11711.

Gaume, J., T. Gast, J. Teran, A. van Herwijnen and C. Jiang 2018. Dynamic anticrack propagation in snow. Nature Communications, 9(1): 3047.

Trottet, B., R. Simenhois, G. Bobillier, A. van Herwijnen, C. Jiang and J. Gaume 2021. From sub-Rayleigh to intersonic crack propagation in snow slab avalanche release. EGU General Assembly 2021, Online, 19-30 Apr 2021, EGU21-8253.

crack-speed

display_name: crack-speed

dynamic-crack-propagation

display_name: dynamic-crack-propagation

propagation-saw-test

display_name: propagation-saw-test

slab-avalanche

display_name: slab-avalanche

snow

display_name: snow

weak-layer

display_name: weak-layer

whumpf

display_name: whumpf

en: Crack propagation speeds in weak snowpack layers from three events: PST, whumpf and slab avalanche


en: records-database fungi-conservation national-data-center fungi biodiversity swissfungi

en: Data extract 20191122

media_type: text/csv

en: Each record consists of the record ID, species name (Taxon_ID,Latin name, German- French- and Italian name if available), information of the location (Swiss coordinates, precision of the coordinates, elevation above sea level, municipality and canton), the date of observation, the person that has found the fungus (leg.), the person that has identified the fungus (det), the functional group that the fungus belongs to, as well as information on the conservation status of the species (red list status, conservation priority status, status in the [Nature and Cultural Heritage Act](https://www.admin.ch/opc/en/classified-compilation/19660144/index.html) (Nature_Cultural_Heritage_Act)). Furthermore, data on the habitat according to Delarze (Delarze, R., Gonseth, Y., Galland, P., Lebensräume der Schweiz, Ott Verlag, Thun, ISBN 3-7225-6749-1) and substrate is given.

format: CSV

en:
This dataset provides distribution data of fungi in Switzerland of the National Data and Information Centre, called [SwissFungi](https://swissfungi.wsl.ch/en/index.html). SwissFungi is a partner of [InfoSpecies](https://www.infospecies.ch/de/), the network of Swiss data and information centres for [fauna](http://www.cscf.ch/cscf/de/home.html), [flora](https://www.infoflora.ch/en/) and [fungi](https://swissfungi.wsl.ch/en/index.html). One of its main objectives is to document the spatial and temporal distribution of species in Switzerland.

The SwissFungi database currently contains more than 670'000 geo-referenced fungi observations, distributed throughout Switzerland. The oldest observations date back to 1770. A large portion of the records are from the last two decades of the last century to the present day. The database is continuously updated with new fungi records. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature.

The data from the distribution atlas of fungi in Switzerland are available for research and practice (nature conservation projects, environmental impact assessments etc.) and can be obtained via an [application form](https://www.infospecies.ch/de/assets/content/documents/Formular_Datenanfrage20190625.pdf). Please note the tariffs for data requests and submit your request directly to the [InfoSpecies](https://www.infospecies.ch/de/) office. Applications are usually answered within two working weeks. Details on the use of data are regulated in the current guidelines of the national data centers. Please note that the data center SwissFungi is not able to verify all incoming fungal records completely for a correct identification or coordinate errors and therefore cannot guarantee the correctness of the information.

License under [InfoSpecies](https://www.infospecies.ch/de/). Data is free of charge for research projects and available on request.

fungi

display_name: fungi

fungi-conservation

display_name: fungi-conservation

national-data-center

display_name: national-data-center

records-database

display_name: records-database

swissfungi

display_name: swissfungi

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: SwissFungi - Records Database for the Fungi of Switzerland


seilaplan

title_for_slug: seilaplan

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

en: Seilaplan QGis Plugin v2.0

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

License: GNU, General Public License, Version 2 or newer.

Literature:
Bont, L., & Heinimann, H. R. (2012). Optimum geometric layout of a single cable road. European journal of forest research, 131(5), 1439-1448.

cable-planning

display_name: cable-planning

cable-yarding

display_name: cable-yarding

catenary-load-path

display_name: catenary-load-path

python

display_name: python

qgis

display_name: qgis

software

display_name: software

steep-terrain-harvesting

display_name: steep-terrain-harvesting

en: 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: Seilaplan


scolytidae

title_for_slug: scolytidae

en: forest bark-beetles dead-wood biodiversity saproxylic-insects coleoptera

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:
Scolytidae data from all historic up to the recent projects (29.10.2019) of WSL, collected with various methods in forests of different types.
Data are provided on request to contact person against bilateral agreement.

bark-beetles

display_name: bark-beetles

biodiversity

display_name: biodiversity

coleoptera

display_name: coleoptera

dead-wood

display_name: dead-wood

forest

display_name: forest

saproxylic-insects

display_name: saproxylic-insects

en: Scolytidae


en: numerical-avalanche-forecasting wet-snow-avalanche machine-learning avalanche avalanche-forcasting

en: Features description

en: This dataset contains forecast input and target data for winter 2021-2022. This dataset is the forecast test set for RF12. The column RF12_AvD_proba is the predicted probability for wet-snow avalanche day provided by the RF12 model.

en: This dataset contains input and target data for winters before September 2020. Dataset1 was used for training model RF12.

en: This dataset contains nowcast input and target data for winter 2021-2022. This dataset is the nowcast test set for RF12. The column RF12_AvD_proba is the predicted probability for wet-snow avalanche day provided by the RF12 model.

en: This dataset contains input and target data for winter 2020-2021. Dataset2 was used for training model RF12.

en:
Datasets used to implement the wet-snow avalanche activity model presented in the article:

Hendrick, M., Techel, F., Volpi, M., Olevski, T., Pérez-Guillén, C., van Herwijnen, A., Schweizer, J. (2023). Automated prediction of wet-snow avalanche activity in the Swiss Alps. Journal of Glaciology, under review

Each dataset includes the input variables (weather and snowpack features) and the target variable (wet-snow avalanche day or not) used to build the model. Additionally, Dataset3_nowcast and Dataset3_forecast contain the predictions provided by the RF12 model.

All input variables are described in the Appendix of the article and also in the read_me file. Further information on SNOWPACK variables is also available at https://models.slf.ch/p/snowpack/ .

avalanche

display_name: avalanche

avalanche-forcasting

display_name: avalanche-forcasting

machine-learning

display_name: machine-learning

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting

wet-snow-avalanche

display_name: wet-snow-avalanche

en: Weather, snowpack and avalanche occurrence data for automated prediction of wet-snow avalanche activity


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

en: Data Request

en:
Continuous stem radius measurements to investigate carbon balance and water relations of trees

 Purpose:

Assessment of growth and water related stem changes

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

stem-diameter

display_name: stem-diameter

swiss-forest-lab

display_name: swiss-forest-lab

tree-growth

display_name: tree-growth

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Automated point dendrometer measurements at LWF sites


en: vitis insect-vectors southern-switzerland orientus-ishidae egg-development scaphoideus-titanus grapevine-yellows

en: Primer sequences used for internal control of amplification of the insects (Cytochrome Oxydase I).

en: Table 1. Plot characteristics and results of molecular analysis, trap captures, and total hatchings.

en: Plot characteristics in terms of type of experiment (M = molecular analysis; T = traps for adult insects; H = hatching experiment), coordinates, region, mean slope, distance from nearest vineyard (DV), last year in which plot was registered as vineyard (Last_T); symptomatic expression of Grapevine Yellows (GY); result of molecular analysis for FDp detection on gone-wild grapevines (pos = positive detection with Cq < 35; neg = negative detection); number (nr.) of yellow sticky traps (if applicable) and Scaphoideus titanus and Orientus ishidae captures per single trap; absolute (N) and specific (N / kg wood) hatching events of S. titanus and O. ishidae per plot.

en: Results of molecular analysis on sampled leaf material.

en: Nymph hatching raw data.

en: Nymph hatching raw data used for the inversed Kaplan-Meier.

en: Table 2. Embryonic development time.

en: Embryonic development time expressed as degree-days and days for gone-wild grapevines (GWGV) and the control. N = number of hatched nymphs; EDdd_min and EDdd_max = degree-days accumulated at the first and last hatching, respectively; EDdd_mean = mean accumulated degree-days; DDA = degree-days accumulated between first and last hatching; EDt_min and EDt_max = minimal and maximal time (days) of embryonic development, respectively; EDt_mean = mean time (days) of embryonic development; SE = standard error; HD = hatching period duration in days.

en:
Dataset used to test the potential role of gone-wild grapevines (GWGV) in forests of Southern Switzerland as a source of Flavescence dorée phytoplasma (FDp) inoculum and as a habitat for its main and alternative vectors, Scaphoideus titanus and Orientus ishidae. In the first phase, GWGV were located and sampled to test their FDp status. In addition, a set of chromotropic traps were placed to monitor the presence and abundance of FDp vectors. In the second phase, wood from GWGV in forests was collected and placed in cages to test the potential oviposition activity by FDp vectors.
The results showed that GWGV in forests are a reservoir of FDp and that they can sustain the whole life cycle of both S.titanus and O.ishidae. Eventually, the need to adapt the current FD management strategies are highlighted.

egg-development

display_name: egg-development

grapevine-yellows

display_name: grapevine-yellows

insect-vectors

display_name: insect-vectors

orientus-ishidae

display_name: orientus-ishidae

scaphoideus-titanus

display_name: scaphoideus-titanus

southern-switzerland

display_name: southern-switzerland

vitis

display_name: vitis

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: Gone-wild grapevines in forests may act as a potential habitat for “Flavescence dorée” phytoplasma vectors and inoculum


en: ambient-air-quality swiss-forest-lab icp-forests ozone long-term-forest-ecosystem-research

en: Data Request

en:
Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone measurements are carried out at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone-induced visible symptoms and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling.

 Purpose:

Ozone risk assessment: Measurements of mean ozone concentrations with passive samplers (passam ag).

 Manual Citation:

 * Schaub M, Calatayud V, Ferretti M, Brunialti G, Lövblad G, Krause G, Sanz MJ, 2016: Part XV: Monitoring of Air Quality. 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, 11 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)

 Paper Citation:

 * Schaub M, Häni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu¨nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf)

 * Cailleret M, Ferretti M, Gessler A, Rigling A, Schaub M (2018) Ozone effects on European forest growth – towards an integrative approach. Journal of Ecology. [doi:10.1111/1365-2745.12941](http://doi.org/10.1111/1365-2745.12941)

 * Calatayud V, Diéguez JJ, Sicard P, Schaub M, De Marco A (2016) Testing approaches for calculating stomatal ozone fluxes from passive sampler. Science of the Total Environment. [doi:10.1016/j.scitotenv.2016.07.155](http://doi.org/10.1016/j.scitotenv.2016.07.155)

 * Calatayud V and Schaub M (2013) Methods for Measuring Gaseous Air Pollutants in Forests. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 375-384. [doi:10.1016/B978-0-08-098222-9.00019-4](http://doi.org/10.1016/B978-0-08-098222-9.00019-4)

ambient-air-quality

display_name: ambient-air-quality

ozone

display_name: ozone

en: Passive sampling of O3 LWF


en: infectious-disease conflict-resolution meaning-negotiation disease-control ontology-authoring

en:
 OWL files in RDF/XML
developed using Protégé 5.2
 Administrative ontology (appl:administrative) in ALCRIF
ontology-administrative.owl
 Epidemiology ontology (appl:epidemiology) in SRIF
ontology-epidemiology.owl
 Merged ontology (undecidable)
ontology-merged.owl
 Merged ontology (decidable, without transitivity of appl:epidemiology#partOf)
ontology-merged-functionality.owl
 Merged ontology (decidable, without functionality of appl:administrative#partOf and disjointness of roles in the administrative ontology)
ontology-merged-transitivity.owl

en:
Ontologies used for the case study in the publication.
Creative Commons (CC) license: CC BY-NC-SA <https://creativecommons.org/licenses/by-nc-sa/3.0/>

conflict-resolution

display_name: conflict-resolution

disease-control

display_name: disease-control

infectious-disease

display_name: infectious-disease

meaning-negotiation

display_name: meaning-negotiation

ontology-authoring

display_name: ontology-authoring

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal


title_for_slug: volume

en:
Volume of stemwood with bark of living trees and shrubs (standing and lying) starting at 12 cm dbh. This corresponds internationally to the "growing stock". 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

avalanches-silvaprotect

display_name: avalanches-silvaprotect

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

diameter-classes

display_name: diameter-classes

economic-region

display_name: economic-region

forest-type-and-stage-of-development

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

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

rockfall-silvaprotect

display_name: rockfall-silvaprotect

site-quality

display_name: site-quality

stage-of-stand-development

display_name: stage-of-stand-development

tree-species-woody-species

display_name: tree-species-woody-species

volume

display_name: volume

en: Volume


maintainer: EnviDat Support

en: dischma davos photogrammetry avalanches snow-hydrology snow-depth-distribution

en: Snowdepth maps

format: GeoTIFF

en:
The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.

Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height > 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0.
The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.html#links). High vegetation is also derived from the classification and the calculated object height from the point cloud.
Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.

An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.

The work is published in:

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology

en: Snow depth mapping by airplane photogrammetry (2017 -  ongoing)


twig-mass-of-live-trees

title_for_slug: twig-mass-of-live-trees

en:
Dry weight (mass) of branches with a diameter under 7 cm from living trees and shrubs starting at 12cm dbh.

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

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

twig-mass-of-live-trees

display_name: twig-mass-of-live-trees

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: Twig mass of live trees


en: ground-vegetation ozone symptoms-of-visible-injuries ambient-air-quality icp-forests swiss-forest-lab long-term-forest-ecosystem-research

en: Data Request

en:
Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone-induced symptoms are being assessed at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone concentrations and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling.

 Purpose:

Ozone risk assessment, i.e. to investigate relationships between ozone exposures and ozone-induced, visible symptoms

 Manual Citation:

 * Schaub M, Calatayud V, Ferretti M, Brunialti G, Lövblad G, Krause G, Sanz MJ, 2016: Part VIII: Monitoring of Ozone Injury. 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:

 * Schaub M, Häni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu¨nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf)

 * Schaub M and Calatayud V (2013) Assessment of Visible Foliar Injury Induced by Ozone. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 205-221. ISBN: 9780080982229. [doi: 10.1016/B978-0-08-098222-9.00011-X](https://doi.org/10.1016/B978-0-08-098222-9.00011-X)

ambient-air-quality

display_name: ambient-air-quality

ground-vegetation

display_name: ground-vegetation

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

ozone

display_name: ozone

swiss-forest-lab

display_name: swiss-forest-lab

symptoms-of-visible-injuries

display_name: symptoms-of-visible-injuries

en: Symptoms of O3 injuries LWF


foliar-analyses-lwf

title_for_slug: foliar-analyses-lwf

en: tree-nutrition swiss-forest-lab long-term-forest-ecosystem-research icp-forests foliar-analyses

en:
Foliage has been sampled every two years since 1995/1997 on 5-6 trees of the main species on all LWF plots. Concentrations of macronutrients (N, P, K, Ca, Mg, S), carbon (C) and micronutrients are determined on leaves and current and previous year needles. The dry mass of 100 leaves or 1000 needles is determined as well.

 Purpose:

To assess the nutrient status of the forest stands and detect possible deficiencies or imbalances, which are often indicative of processes at the ecosystem level.

 Manual Citation:

 * Brang, P., Hug, C., Thimonier, A., Zehnder, U., 1997. Kapitel D1.5 Nährstoffversorgung von Nadeln und Blättern, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-12.

 * Rautio P, Fürst A, Stefan K, Raitio H, Bartels U, 2016: Part XII: Sampling and Analysis of Needles and Leaves. 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,  19  p.  +  Annex  [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)

 Paper Citation:

 * Thimonier A, Graf Pannatier E, Schmitt M, Waldner P, Walthert L, Schleppi P, Dobbertin M, Kräuchi N (2010) Does exceeding the critical loads for nitrogen alter nitrate leaching, the nutrient status of trees and their crown condition at Swiss Long-term Forest Ecosystem Research (LWF) sites?. European Journal of Forest Research, 129 (3): 443-461. [doi: 10.1007/s10342-009-0328-9](http://doi.org/10.1007/s10342-009-0328-9)

foliar-analyses

display_name: foliar-analyses

tree-nutrition

display_name: tree-nutrition

en: Foliar analyses LWF


en: snow-cover vertical-wind-motion boundary-layer-winds saltation snow-depth

en: Data Compilation

en: Compressed TXT files

en:
The data correspond to the experiments presented and discussed in a paper regarding the interaction between turbulent wind fluctuations and snow saltation mass-fluxes (Paterna, 2016). Each of the nine data files corresponds to a different experiment presented in the paper and conducted in the winter 2014/2015 in the WSL/SLF cold wind tunnel in Davos. For each file the five columns indicate the time from the beginning of the experiment, the streamwise (u’) and the vertical (w’) wind velocity fluctuations, the streamwise (qx) and the vertical (qz) snow mass-flux components. From these time-series the scales of the snow saltation and of the turbulent flow are obtained with respect to the eddy-cycles and snow saltation cycles. From spectral analysis of the time-series a decoupling of the snow saltation from the turbulence forcing reveals two regimes of interaction: a turbulence-dependent regime occurring with weak saltation, and a turbulence-independent regime with strong saltation. Further details can be found at the link below.

Publication http://onlinelibrary.wiley.com/doi/10.1002/2016GL068171/abstract

boundary-layer-winds

display_name: boundary-layer-winds

saltation

display_name: saltation

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

vertical-wind-motion

display_name: vertical-wind-motion

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Wind tunnel measurement data of drifting snow and turbulent wind fluctuations


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

en: Dataset V1

en: This dataset is outdated and has been replaced with an updated version (see description).

format: GDB

en: Documentation

en: This data documentation is outdated and has been replaced with an updated version (see description).

mimetype: application/msword

en: Site photo

en: This site photo is outdated and has been replaced with an updated version (see description).

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

afforestation

display_name: afforestation

long-term-experiment

display_name: long-term-experiment

mountain-forest

display_name: mountain-forest

stillberg

display_name: stillberg

treeline

display_name: treeline

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


maintainer: Martin Schneebeli

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

en: Description of snowtypes, filenames and other metadata

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

en: Snow models in STL-format

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

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

depth-hoar

display_name: depth-hoar

new-snow

display_name: new-snow

rounded-snow

display_name: rounded-snow

snow

display_name: snow

snow-cast

display_name: snow-cast

weak-layer

display_name: weak-layer

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.


ensemble-hydrograph-separation-scripts

title_for_slug: ensemble-hydrograph-separation-scripts

en: runoff streamflow tracers hydrograph-separation catchment isotopes

en: MATLAB scripts and demonstration files (current version - build 20201119)

en:
The ensemble hydrograph separation functions themselves are in EHS_Fnew.m, EHS_profile.m, and EHS_TTD.m.

Build 20201119 has improved commenting, naming, and structuring, and also minor bug fixes in EHS_Fnew, EHS_TTD, and EHS_profile. Build 20200718 has been deprecated. Use build 20201119 instead.

EHS_demo_RUN_THIS.m is a demonstration script that calls the EHS functions to analyze the demo input files. The resulting output files are in the "demo output files" folder. These were generated by the R versions of the scripts but have been verified to be numerically equal to the MATLAB outputs, within machine precision.

en: *deprecated* MATLAB scripts and demonstration files (*deprecated version* - build 20200718 - do not use!)

en:
The ensemble hydrograph separation functions themselves are in EHS_Fnew.m, EHS_profile.m, and EHS_TTD.m.

EHS_demo_RUN_THIS.m is a demonstration script that calls the EHS functions to analyze the demo input files. The resulting output files are in the "demo output files" folder. These were generated by the R versions of the scripts but have been verified to be numerically equal to the MATLAB outputs, within machine precision.

en: R scripts and demonstration files

en:
The ensemble hydrograph separation functions themselves are in EHS_v1.4.R.

EHS_demo_v1.4.R is a demonstration script that calls EHS_v1.4.R to analyze the demo input files. The resulting output files are in the "demo output files" folder.

en:
Calculation scripts that perform ensemble hydrograph separation. Identical scripts in R and MATLAB are provided, along with demonstration input time series and the corresponding outputs. These scripts were tested on R version 3.6.2 (2019-12-12) and on MATLAB versions 2018b and 2109b.

These scripts are made publicly available under GNU General Public License 3; for details see https://www.gnu.org/licenses/.

ETH Zurich, WSL, James Kirchner, and Julia Knapp make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose. Users are solely responsible for determining the suitability and reliability of this software for their own purposes.

catchment

display_name: catchment

hydrograph-separation

display_name: hydrograph-separation

isotopes

display_name: isotopes

runoff

display_name: runoff

streamflow

display_name: streamflow

tracers

display_name: tracers

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Ensemble hydrograph separation scripts


maintainer: Henning Löwe

en: snow-mechanics unconfined-compression-experiments discrete-element-modelling 3d-ct-images microstructure spherical-model-snow

en: Data Structure (Python Notebook - view online)

mimetype: text/plain

en: Data Structure (Python Notebook)

en: Data Access

en:
For the investigation of microstructural and mechanical properties of snow unconfined compression experiments and 3D computed tomography (CT) imaging were performed on sintered rounded grain snow and spherical model snow. The spherical model snow was generated to create geometrically simplified, well-defined microstructures for calibration of numerical models, such as discrete element models (DEM) in which the microstructure is represented by spherical particles.
In the experiments, microstructural variation was created by varying the sintering time (contact size) and the density of the ice sphere samples (number of contacts). The 3D CT images allow for a complete reconstruction of the entire experimental sample (cylindrical sample dimension: diameter = 33.6 mm; height = 14 mm).

discrete-element-modelling

display_name: discrete-element-modelling

microstructure

display_name: microstructure

snow-mechanics

display_name: snow-mechanics

spherical-model-snow

display_name: spherical-model-snow

unconfined-compression-experiments

display_name: unconfined-compression-experiments

en: Unconfined compression experiments and 3D CT images of spherical model snow and RG snow samples


en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh removed from the forest between two inventories due to damage that occurred, in this case windthrow, and not due to silvicultural planning. This theme is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).  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)_

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

economic-region

display_name: economic-region

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

salvage-logging-due-to-wind

display_name: salvage-logging-due-to-wind

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: Salvage logging due to wind


napf-soil-wetness-monitoring-data

title_for_slug: napf-soil-wetness-monitoring-data

en: matric-potential shallow-landslides landslide-early-warning monitoring soil-moisture

en: Napf soil wetness monitoring data

en:
Soil wetness monitoring data (soil moisture, matric potential) and additional time series (air temperature, precipitation, ground temperature).
Quality-controlled and aggregated to hourly values.

en:
The dataset contains the soil wetness monitoring data from the publication Wicki et al. (2022). It was collected in Wasen i.E. (Napf area, Switzerland).
The monitoring data is quality-controlled and aggregated to hourly values and it is provided for the study period 2019-04-05 to 2022-04-30.

The following information is contained (by column):

* Timestamp (UTC+1 time zone)
* Site: Slope (47.02486 N, 7.81960 E), Flat (47.02302 N, 7.81760 E)
* Sensor type
* Measure: VWC = volumetric water content [m3 m-3], MP = matric potential [hPa], TEMP = temperature [°C], PREC = precipitation [mm]
* Sensor number (per site each sensor is provided a unique identifier)
* Installation depth [m]
* Homogenization flag: If the data is considered homogeneous, it is given the flag 1, else the flag 0 is given
* Sensor value
* Normalized value: Normalization was conducted for VWC (saturation) and MP values

Wicki, A., Lehmann, P., Hauck, C., and Stähli, M.: Impact of topography on in-situ soil wetness measurements for regional landslide early warning – a case study from the Swiss Alpine Foreland, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-211, in review, 2022.

landslide-early-warning

display_name: landslide-early-warning

matric-potential

display_name: matric-potential

monitoring

display_name: monitoring

shallow-landslides

display_name: shallow-landslides

soil-moisture

display_name: soil-moisture


en: soil-water-content soil-moisture long-term-forest-ecosystem-research icp-forests swiss-forest-lab

en: Data Request

en:
Continuous measurement of soil water content at 15, 50 and 70 cm depth in Visp (3 replications) with TDR soil moisture probes (Tektronix 1502 B) from 2001 until 25.04.2013

 Purpose:

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

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

soil-water-content

display_name: soil-water-content

swiss-forest-lab

display_name: swiss-forest-lab

en: TDR soil water content measurements LWF Visp


en: landslide python random-forest rainfall-thresholds illgraben debris-flow

en: Threshold code and example data

en: .zip file with code and example data. See the README.md for how to use it.

en:
This is the source code to compute rainfall thresholds for debris flows or landslides following Hirschberg et al. (2021).

 How to install and run the example

Pyhton has to be installed to run the codes. To make sure it works correctly, it is easiest to install Anaconda and create an environment with the right packages from the yml-file. To this end, in a command-line interpreter, change the working directory to where you saved this project and run the following:

`$ conda env create -f environment.yml`

`$ conda activate thresholds` or `$ source activate thresholds`

To run an example:

`$ python run_example()`

It will save a dat-file and a figure as Fig. 7 in Hirschberg et al. (2021), where more information can be found.

 License
GNU General Public License v3.0

debris-flow

display_name: debris-flow

illgraben

display_name: illgraben

landslide

display_name: landslide

python

display_name: python

rainfall-thresholds

display_name: rainfall-thresholds

random-forest

display_name: random-forest

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Source code for: Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment


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

en: DHM-Kacheln zusammenfügen

en: View video on youtube!

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. In this tutorial video, we show how to merge multiple DTM raster tiles into one file, using the QGIS tool ‘Virtual Raster’. This simplifies the digital planning of a cable line using the QGIS plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to Seilaplan website: https://seilaplan.wsl.ch

***************************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. In diesem Tutorialvideo zeigen wir, wie man mit dem QGIS-Plugin Virtuelles Raster mehrere DHM-Kacheln zu einem einzigen Rasterfile zusammenfügen und abspeichern kann. Für die Seillinienplanung mit Seilaplan muss nun nur noch eine Datei, mein neues virtuelles Raster, ausgewählt werden.

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geodata-processing

display_name: geodata-processing

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

raster-processing

display_name: raster-processing

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

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: Seilaplan Tutorial: Merge DTM tiles


en: naturalness protective-forest tree-species-composition nfi nais

en: Scherrer et al. 2023 - Scripts

en:
This archive contains all the R-scripts to run the analysis and reconstruct the figures from
Scherrer, D., Allgaier Leuch, B., Fischer, C., Frehner, M. & Wohlgemuth, T. (2023) Maintaining the protective function of mountain forests under climate change by the concept of naturalness in tree species composition. Frontiers in Forests and Global Change, in review.

en: Scherrer et al. 2023 - Data for analysis and figures

en:
This archive contains all the data needed to reconstruct the analysis and figures of
Scherrer, D., Allgaier Leuch, B., Fischer, C., Frehner, M. & Wohlgemuth, T. (2023) Maintaining the protective function of mountain forests under climate change by the concept of naturalness in tree species composition. Frontiers in Forests and Global Change, in review.

en: Data and scripts used by Scherrer et al. 2023 in the publication 'Maintaining the protective function of mountain forests under climate change by the concept of naturalness in tree species composition'. The analysis is based on data about the tree species composition of the canopy layer in the NFI4 and information about the potential natural forest of the sites based on the NaiS classification system.

nais

display_name: nais

naturalness

display_name: naturalness

nfi

display_name: nfi

protective-forest

display_name: protective-forest

tree-species-composition

display_name: tree-species-composition

en: Naturalness of tree species composition in protective forests


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

en: Data Request

en:
Continuous measurement of soil matrix potential at 15, 50 and 100 cm depth with Decagon MPS-2 sensors 1 m N, SE and SW from the stem of 3 threes within much and 3 trees within few shrubs

 Purpose:

Explore the effect of shrubs on the water availability for pine trees in Visp.

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

shrubs

display_name: shrubs

soil-water-matrix-potential

display_name: soil-water-matrix-potential

swiss-forest-lab

display_name: swiss-forest-lab

en: MPS-2 on LWF Visp to survey 2017 mortality wave


en: snow-water-equivalent source-code snow-depth subgrid-topography snow-cover-modeling hysteresis

en: Seasonal fractional snow-covered area algorithm

en: Release Version 1 from https://gitlabext.wsl.ch/snow-models/fractional-snow-covered-area

en:
This is the source code for computing the seasonal fractional snow-covered area. It is written in Fortran 90. The code reads snow depth (HS) and snow water equivalent (SWE) data from the provided example file HS_SWE.txt and writes the computed fractional snow-covered area (fSCA) to a file fSCA.txt.

The current version can be found in the WSL/SLF Gitlab repository:
https://gitlabext.wsl.ch/snow-models/fractional-snow-covered-area

hysteresis

display_name: hysteresis

snow-cover-modeling

display_name: snow-cover-modeling

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

source-code

display_name: source-code

subgrid-topography

display_name: subgrid-topography

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal


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

en: Precipitation

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

en: Soil moisture

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

en: Shapefile of 307 catchments in Switzerland

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

en: Discharge

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

en: Low-flow storage

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

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

discharge

display_name: discharge

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

precipitation

display_name: precipitation

prevah

display_name: prevah

soil-moisture

display_name: soil-moisture

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

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


en: antarctica terrestiral-laserscanning terrestrial-laser-scans depth-of-snowfall gridded-data

en: Scan from 11 January 2019, 10 cm resolution, version 2

en: Scan from 21 December 2018, 1 mm resolution

en: Scan from 4 January 2019, 1 mm resolution

en: Deprecated: Scan from 11 January 2019, 10 cm resolution, version 1

en: This resource is deprecated. It contains erroneous data.

en: Scan from 2 January 2019, 10 cm resolution

en: Scan from 27 December 2018, 10 cm resolution

en: Scan from 2 January 2019, 1 mm resolution

en: Scan from 21 December 2018, 10 cm resolution

en: Scan from 27 December 2018, 1 mm resolution

en: Scan from 4 January 2019, 10 cm resolution

en: Scan from 11 January 2019, 1 mm resolution

en:
Surface topography maps (spatial extent: 400 m x 400 m) obtained at approximately 300 m from the top of the Hammarryggen Ice Rise in Dronning Maud Land, East Antarctica, using a Riegl VZ-6000 Terrestrial Laser Scanner (TLS). Scans were obtained on 5 days in the 2018-2019 Austral summer: on December 21 and 27, and January 2, 4 and 11. By using reflectors installed on bamboo poles, scans were registered with respect to the reflectors, such that the difference between two successive scans reveals the spatial patterns of erosion and deposition of snow. On each scan day, we used multiple scan positions to create one combined point cloud. After applying an octree filter on the point cloud, a 3D surface was obtained. For each day, the dataset contains a 1 mm and a 10 cm octree filter resolution file, only including points in a 400 m x 400 m area centered around the scan positions.

Notes:

* All files in the dataset are in the same coordinate system. However, this coordinate system is arbitrary (i.e., not related to any global coordinate system).

* From the installed reflectors, 4 reflectors could be used over the full period. The scan accuracy is generally higher within the area enclosed by the reflectors.

* The scans from January 2 were found to have exhibited small tilt during the scan and are of lesser accuracy.

* By walking along fixed corridors, disturbance of the snow was limited.

antarctica

display_name: antarctica

depth-of-snowfall

display_name: depth-of-snowfall

gridded-data

display_name: gridded-data

terrestiral-laserscanning

display_name: terrestiral-laserscanning

terrestrial-laser-scans

display_name: terrestrial-laser-scans

en: Terrestrial laser scans on Hammarryggen Ice Rise, Dronning Maud Land, East Antarctica


en: restoration plants fungi soil-chemistry soil-biology topsoil-removal nematodes soil-biodiversity biodiversity soil grassland bacteria

en: Soil properties

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
N.pool	Soil nitrogen pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
Corg.pool	Soil organic carbon pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
pH	soil pH [CaCl2]
BD	Bulk density [g cm-3]
FC	Field capacity [%]
Veg.1	principle coordinate axis 1 (vegetation)
Veg.1	principle coordinate axis 2 (vegetation)
Nem.1	principle coordinate axis 1 (nematoda)
Nem.2	principle coordinate axis 2 (nematoda)
Fun.1	principle coordinate axis 1 (fungi)
Fun.2	principle coordinate axis 2 (fungi)
Prok.1	principle coordinate axis 1 (prokaryota)
Prok.2	principle coordinate axis 2 (prokaryota)
Veg.H	Shannon diversity (vegetation)
Nem.H	Shannon diversity (nematoda)
Fun.H	Shannon diversity (fungi)
Prok.H	Shannon diversity (prokaryota)

en: Fungi data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
OTU.ID	consecutive numbers for operational taxonomic units (OTUe 1-7794)
Taxonomic rank	Domain, Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	OTU.ID (see Taxonomy)

en: Vegetation data

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
column I-LW	Plant abundance per species (in total 327 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature according to Lauber & Wagner (1996)

en: Distance to target results

en:
Community	biotic community (vegetation, nematoda, fungi, prokaryota)
Treatment	Treatment groups; Y.4-30= restored grasslands (time since restoration); Initial=intensively managed grasslands (I)
Comparison	within- or between-community Bray-Curtis dissimilarity distance comparison
Label	within-community distances of initial/restored grasslands: treatment abbreviation; between-communities distances of initial/restored-to-target grasslands: treatment abbreviation plus Target (T; species-rich grasslands)
mean	Mean distance to Target (6 plots per treatment group)
sd	Standard deviation of distances to Target (6 plots per treatment group)

en: Ecosystem coupling_interactions

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic
Correlation	non-parametric Spearman rank correlations between biotic and abiotic parameters (ecological interactions)

en: Networks

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Correlation	non-parametric Spearman rank correlations
Vertex.1+2	Comparison between network components; N.pool = total soil nitrogen pool [kg ha-1 12 cm-1]; Corg.pool = soil organic carbon pool [kg ha-1 12 cm-1], pH = soil pH [CaCl2]; BD = bulk density [g cm-3]; FC = field capacity [%]
	PCoA 1 + 2 biotic communities [Veg = vegetation; Nem = nematoda; Fun = fungi; Prok = prokaryota]
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic

en: Nematode data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
ASV.ID	unit number.letter code per amplicon sequence variant (in total 904)
Taxonomic rank	Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	ASV.ID (see Taxonomy)

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

Paper Citation:

_Resch, M.C., Schütz, M., Ochoa-Hueso, R., Buchmann, N., Frey, B., Graf, U., van der Putten, W.H., Zimmermann, S., Risch, A.C. (in review). Long-term recovery of above- and belowground interactions in restored grassland after topsoil removal and seed addition. Journal of Applied Ecology_

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

Study area and experimental design
The study was conducted in and around two nature reserves, Eigental and Altläufe der Glatt, which were located approximately 5 km apart (47°27´ to 47°29´ N, 8°37´ to 8°32´ E, 417 to 572 m a.s.l., Canton of Zurich, Switzerland; Figure S1 and S2, Table S1). Mean annual temperature and precipitation are 9.8 ± 0.6 °C and 990 ± 168 mm (Kloten climate station 1988-2018; MeteoSchweiz, 2019). TFor this study, we used a space-for-time approach based on eight restoration sites that were between 3 and 32 years old. We measured recovery and restoration success by comparing the restored grasslands with intensively managed and semi-natural grasslands. Using a space-for-time approach requires high similarities in historical properties of the site, such as soil conditions and management regimes, to assure that temporal processes are appropriately represented by spatial patterns (Walker et al., 2010). This was the case in our study. The restored sites had similar soil conditions (i.e., soil type, structure, water availability) as the targeted semi-natural grasslands, while they shared the same agricultural legacy with intensively managed grasslands, i.e., biomass harvest and fertilization (manure and/or slurry) three to five times a year as well as tillage. We randomly established three 5 m x 5 m (25-m2) plots for plant identification and three 2 m x 2 m (4-m2) subplots for soil biotic and abiotic data collection at least 2 m away from the 25-m2 plots in each restoration site. Sites of similar age were grouped into four age classes: Y.4 (3 & 4 years after restoration), Y.18 (17 & 19 years), Y.24 (23 & 25 years), and Y.30 (27 & 32 years). Six intensively managed (Initial) and six semi-natural grassland (Target) sites complemented the experimental set-up, for a total of 36 plots. All plots were sampled under similar conditions, i.e., day of the year, air temperature, soil moisture, and time since last rain event, in June/July 2017 (intensively managed and semi-natural plots) and 2018 (restored plots).

Collection of plants and selected soil biota data
Plant species cover (in %) was visually estimated in each 25-m2 plot in mid-June (Braun-Blanquet, 1964; nomenclature: Lauber & Wagner, 1996). We calculated Shannon diversity and assessed plant community structure. We included soil microbial (fungi, procaryotes) and nematodes in our study as they represent the majority of soil biotic diversity and abundance (Bardgett & van der Putten, 2014), cover various trophic levels of the soil food web (Bongers & Ferris, 1999), and play key roles in soil functioning and ecosystem processes (Bardgett & van der Putten, 2014). In particular, soil nematodes were found to be well suited belowground indicators to evaluate recovery/development after restoration (e.g. Frouz, et al. 2008; Kardol et al., 2009; Resch et al., 2019). We randomly collected ten soil cores (2.2 cm diameter x 12 cm depths; sampler from Giddings Machine Company, Windsor, USA) in the 4-m2 subplots to assess soil nematode and microbial (fungal, prokaryotic) diversities and community structures. For soil nematodes, eight of the soil cores were combined and gently homogenized, placed in coolers and stored at 4 °C and transported to the laboratory (Netherlands Institute of Ecology, NIOO, Wageningen, Netherlands) within three days after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriators (Oostenbrink, 1960). After extraction, each sample was divided into three subsamples, two for molecular identification and one to determine nematode abundance (see Resch et al., 2019). For the molecular work, two subsamples were stored in 70% ethanol (final volume 10 mL each) and transported to the laboratory at the Swiss Federal Research Institute WSL (Birmensdorf, Switzerland). Each subsample was reduced to roughly 200 μL by centrifugation and removal of the supernatant. The remaining ethanol was vaporized (65 °C for 3 h). Thereafter, 180 μL ATL buffer solution (Qiagen, Hilden, Germany) was immediately added and samples were stored at 4 °C until further processing. From these samples, nematode metagenomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer`s protocol, except for the incubation step which was run at 56 °C for 4 h. PCR amplification of the V6-V8 region of the eukaryotic small-subunit (18S) was performed with 7.5 μL of genomic DNA template (ca. 1 ng/μL) in 25 μL reactions containing 5 μL PCR reaction buffer, 2.5 mM MgCL2, 0.2 mM dNTPs, 0.8 μM of each primer (NemF: Sapkota & Nicolaisen, 2015; 18Sr2b: Porazinska et al., 2009), 0.5 μL BSA, and 0.25 μL GoTaq G2 Hot Start Polymerase (Promega Corporation, Madison, USA). Amplification was using an initial DNA denaturation step of 95 °C for 2 min, followed by 35 cycles at 94 °C for 40 sec, 58 °C for 40 sec, 72 °C for 1 min, and a final elongation step at 72 °C for 10 min. Filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads was implemented using the DADA2 pipeline (v.1.12; Callahan et al., 2016) to finally assign amplicon sequence variants (ASVs) as taxonomic units.
We combined and homogenized the remaining two soil cores to assess soil microbes, placed them in coolers (4 °C) and transported them to the laboratory at WSL. Metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNAeasy PowerMax Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer´s protocol. PCR amplification of the V3-V4 region of the small-subunit (16S) of prokaryotes (i.e., bacteria and archaea) and the ribosomal internal transcribed spacer region (ITS2) of fungi was performed with 1 ng of template DNA using PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates, pooled and sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, USA). Quality filtering, clustering into operational taxonomic units (OTUs, 97% similarity cutoffs) and taxonomic assignment were performed as previously described (Resch et al., 2021).Taxonomic classification of nematode, prokaryotic and fungal sequences was conducted querying against the most recent versions of PR2 (v.4.11.1; Guillou et al., 2013), SILVA (v.132; Quast et al., 2013), and UNITE (v.8; Nilsson et al., 2019) reference sequence databases. Taxonomic assignment cutoffs were set to confidence rankings ≥ 0.8 (below ranked as unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as OTUs or ASVs assigned to other than Fungi or Nematoda were manually removed prior to data analysis. The three datasets were filtered to discard singletons and doubletons. Taxonomic abundance matrices were rarefied to the lowest number of sequences per community to achieve parity of the total number of reads between samples (Prokaryotes: 10,929 reads; Fungi: 18,337 reads; Nematodes: 6,662 reads). We calculated Shannon diversity and assessed community structures for soil nematodes, prokaryotes and fungi based on their relative abundances of ASV or OTU at the taxon level.

Collection of soil physical and chemical properties
We randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) per 4-m2 subplot using a steel cylinder that fit into the soil corer. The cylinders were capped to avoid disturbance during transport and used to measure field capacity, rock content and fine earth density as previously described (Resch et al., 2021). We randomly collected another three soil cores (5 cm diameter, 12 cm depths) in each 4-m2 subplot to determine soil chemical properties. The cores were pooled, dried at 60 °C for 48 h and passed through a 2 mm sieve. We measured soil pH (CaCl2) on dried samples, total nitrogen (N) and organic carbon (C) concentration on dried and fine-ground samples (≤ 0.5 mm; for details see Resch et al., 2021). We calculated total N and organic C pools after correcting its concentration for soil depth, rock content and fine earth density.

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

fungi

display_name: fungi

grassland

display_name: grassland

nematodes

display_name: nematodes

plants

display_name: plants

restoration

display_name: restoration

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

topsoil-removal

display_name: topsoil-removal

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: Long-term recovery of above-and belowground interactions in restored grasslands


en: model mars snow sediment-transport erosion

en: Datasets of Figure 4b

en: Datasets of Figure 2

en: Datasets of Figure 3a

en: Datasets of Figure 4a

en: Datasets of Figure 3b

en:
The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)).

 Manuscript Abstract:
Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.

erosion

display_name: erosion

mars

display_name: mars

model

display_name: model

sediment-transport

display_name: sediment-transport

snow

display_name: snow

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


lwf-chironico-long-term-research-site

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

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

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

en: Chironico Meteorological Data LWF

en:
![alt text](https://www.envidat.ch/dataset/67e14643-c7f2-4190-ae6e-c8c94f1c5f01/resource/1d23d53f-8572-47eb-b466-88dd8ad244c9/download/lwf_chironico.jpg "LWF Chironico")

LWF Plot Chironico

- Community: Chironico / canton TI
- Date of installation: 29 August 1995
- Size of the plot: 2 ha
- Altitude: 1342-1387 m
- Mean slope: 35%
- Geology (in German): Untergrund: Penninikum; Paragneisse u. Glimmerschiefer - Oberfläche: Quartär; karbonatfreie Moräne, Hängeschutt
- Provisional soil type (WSL) : Distric Cambisol
- Woodland association after EK72: 47: Calamagrostio villosae-Abietetum
- Main tree species: Picea abies
- Management system: high forest
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 54.1 cm
- Number of trees BHD >= 12 cm (2011): 750
- Maximum tree age: Picea abies: 160-180 yr - Abies alba: 140-160 yr

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


en: high-resolution complex-terrain meteoswiss cosmo-wrf numerical-simulation

en: MeteorologicalData

en: Scripts to process meteorological data (see Section 2 in COSMOWRFdocumentation.pdf).

en: Postprocessing

en: Postprocessing scripts to map WRF output on swiss coordinates (see Section 7 in COSMOWRFdocumentation.pdf)

en: WRFrun

en: Scripts to run COSMO-WRF simulation (see Section 6 in COSMOWRFdocumentation.pdf)

en: COSMO-WRF documentation v1.0

en:
Gerber, Franziska; Sharma, Varun (2018). Running COSMO-WRF on very-high resolution over complex terrain. EnviDat. doi:10.16904/envidat.35

Documentation about the procedure to run COSMO-WRF

mimetype: application/pdf

en: StaticData

en:
Scripts to process geographical/static data (see Section 3 in COSMOWRFdocumentation.pdf).
- ascii_to_bin_landuse.f90
- ascii_to_bin_soiltype.f90
- ascii_to_bin_topo_wgs84_ws2sf1.f90
- see_slopes.ncl
- soiltype_writing.py
- toposmooth_wgs84_cosmobound.ncl
- write_geogrid.o

en: WPSSetup

en: Scripts that need to be changed to compile WPS (see COSMOWRFdocumentation.pdf, Section 4)

en: WPSrun

en: Scripts to run the WRF pre-processing (WPS) for COSMO-WRF simulation (see Section 5 in COSMOWRFdocumentation.pdf)

en: This is a technical documentation of the procedure to run the Weather Research and Forecasting (WRF) model over complex alpine terrain using Consortium for Small-Scale Modeling (COSMO) reanalysis by the Federal Office of Meteorology and Climatology (MeteoSwiss) as initial and boundary conditions (COMSO-WRF). The setup is adapted for very high resolution simulations based on COSMO-2 (2.2 km resolution) reanalysis. This document gives an overview over steps to setup COSMO-WRF and adaptations needed to run COSMO-WRF. Additionally, the calculation of precipitation rate at a horizontal plane and remapping COSMO-WRF output on Swiss coordinates are documented.

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

high-resolution

display_name: high-resolution

meteoswiss

display_name: meteoswiss

numerical-simulation

display_name: numerical-simulation

educ

en: Education, culture and sport


en: snowmicropen snow-stratigraphy snow-temperature water-front ice-layers weissfluhjoch

en: README

en: WFJ_ICE_LAYERS dataset

en: The WFJ_ICE_LAYERS dataset contains multi-instrument snowpack measurements at high temporal resolution, which enable to monitor the formation of deep ice layers due to preferential water flow, at the Weissfluhjoch research site, Davos, Switzerland. It covers the winter 2016/2017, with a focus on the early melting season. This dataset includes traditional snowpack profiles (weekly resolution, 15/11/2016-29/05/2017), SnowMicroPen penetration resistance profiles (daily resolution, 01/02/2017-19/04/2017), snow temperatures measured at different heights in the snowpack (half-hourly resolution, 01/03/2017-15/04/2017) and the water front height derived from an upward-looking ground penetrating radar (3-hour resolution, 04/03/2017-08/04/2017). The measurements are complemented by initialization files for SNOWPACK model simulations with the ice reservoir parameterization at Weissfluhjoch for the winter 2016/2017.

ice-layers

display_name: ice-layers

snow-stratigraphy

display_name: snow-stratigraphy

snow-temperature

display_name: snow-temperature

snowmicropen

display_name: snowmicropen

water-front

display_name: water-front

weissfluhjoch

display_name: weissfluhjoch

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: WFJ_ICE_LAYERS: Multi-instrument data for monitoring deep ice layer formation in an alpine snowpack


biomass-of-live-trees

title_for_slug: biomass-of-live-trees

en:
Dry weight (mass) of living trees and shrubs starting at 12 cm dbh. This consists of the tree parts: roots, stemwood, branch coarse wood, brushwood/twigs and needles/leaves. 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

biogeographical-region

display_name: biogeographical-region

biomass-of-live-trees

display_name: biomass-of-live-trees

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

en: Biomass of live trees


en: biodiversity transition forest enabling-factors natural-hazards hindering-factors landscape

en: ANEZET Interview Transcriptions

en: Transcriptions of the interviews with experts in the fields of biodiversity, forests, landscape and natural hazards

en:
We have analysed past transformations in Switzerland in four environmental domains, with the aim to
draw conclusions for current challenges, such as the net‐zero transformation. The data comprise transcripts of interviews with experts in the field of biodiversity, forests, landscape and natural hazard research.

biodiversity

display_name: biodiversity

enabling-factors

display_name: enabling-factors

forest

display_name: forest

hindering-factors

display_name: hindering-factors

landscape

display_name: landscape

natural-hazards

display_name: natural-hazards

transition

display_name: transition

en: ANEZET: Analysing Net-Zero Transformations