en: winter-energy-production photovoltaic albedo shortwave-radiation snow

en: Data Forward Scattering

en: GROUNDEYE model for multiple forward scattering in snow-covered terrain. Meteorological and topographical input data set for the Meierhofertäli near Davos, Switzerland, September 2017 - August 2018. Output data for September 2017 - August 2018 at hourly resolution and every pixel. Post-processing scripts for plotting and analysis.

en:
 Overview
We present GROUNDEYE, a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow.
Embedded in the surface process model Alpine3D, the new terrain radiation model GROUNDEYE receives interpolated real weather data with diffuse and direct broadband shortwave radiation for each pixel as well as a spatially variable plane albedo from the module SNOWPACK.

 Format
The GROUNDEYE model is written in c++, as is the entire environment of Alpine3d. The input and output data sets are .xlsx or .txt format, pre- and postprocessing including the generation of all figures is in .R format.

 Structure
In Data_Forward_Scattering.zip you will find all necessary data and model details to
reproduce the results of the JGR publication "How forward-scattering snow and terrain change the Alpine radiation balance with application to solar panels"

 - 	Model Input Data contains the meteorological and topographic input data sets, the BRDF, and preprocessing scripts.
 - Model Code contains the full model Alpine3d including the radiative transfer module GROUNDEYE.
 - Model Output Data contains the results of the simulation of terrain irradiance and irradiance of solar panels; hourly resolution, 1. Sptember 2017 - 31. August 2018.
 - Measurements Solar Testsite contains information and measurements of the solar testsite at the Totalp near Davos, Switzerland.
 - Postprocessing contains all R-Scripts used for the analysis and
plotting of the corresponding data.

In each of these folders you will find detailed information in
the file 'About this Folder.txt'.

albedo

display_name: albedo

photovoltaic

display_name: photovoltaic

shortwave-radiation

display_name: shortwave-radiation

winter-energy-production

display_name: winter-energy-production

en: Data, Code Link and Metadata on Forward Scattering of Snow at Totalp


high-resolution-climate-data-for-europe

maintainer: EnviDat Support

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

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

state: active

type: dataset

en: Data Access

format: SERVICE

en: CHELSA EUR11 Technical Documentation

media_type: application/pdf

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

format: PDF

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

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

air-temperature

display_name: air-temperature

cordex

display_name: cordex

daily-climate

display_name: daily-climate

europe

display_name: europe

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: High resolution climate data for Europe


data-broedlin-cnp

title_for_slug: data-broedlin-cnp

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

en: Data Broedlin-CNP-Release

en: Raw data of C-N-P mobilization experiment from Oi, Oe/Oa, and A horizons of beech forest soils along a nutrient availability gradient.

format: XLS

en: Data Broedlin-CNP

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

carbon

display_name: carbon

dissolved-organic-matter

display_name: dissolved-organic-matter

drying-rewetting

display_name: drying-rewetting

forest

display_name: forest

mineralization

display_name: mineralization

nitrogen

display_name: nitrogen

phosphorus

display_name: phosphorus

soil

display_name: soil

stoichiometry

display_name: stoichiometry

en: Data Broedlin CNP


greenland-shrubs-and-microclimate

maintainer: Nathalie Chardon

title_for_slug: greenland-shrubs-and-microclimate

en: arctic accessible-shrub-forest sdm climate field-data validation

en: Kangerluarsunnguaq

en:
 Collected August 2020 (PI: Nathalie Isabelle Chardon)

These data collected August 2020 (PI: Nathalie Isabelle Chardon). Please contact N. I. Chardon (nathalie.chardon_at_gmail.com) for individual quadrat (i.e. 1 m x 1 m) data, which are averaged at the 90 m spatial grid here.

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

id: sampling zone and unique 90 m grid identifier

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

date: sampling day.month.year

 Species presence [1] or absence [0] at grid level

alcr: _Alnus crispa_

bena: _Betula nana_

dila: _Diapensia lapponica_

emni: _Empetrum nigrum_

hahy: _Harimanella hypoides_

lopr: _Loiseleuria procumbens_

oxpa: _Oxycoccus palustre_

phca: _Phyllodoce caerulea_

rhod: _Rhododendron_ sp.

saar: _Salix arctica_

sagl: _Salix glauca_

sahe: _Salix herbacea_

vaul: _Vaccinium uliginosum_

drin: _Dryas integrifolia_

thpr: _Thymus praecox_

 Habitat characteristics [% cover]

shrub: all shrub species

gram: graminoids

forb: forbs

bryo: bryophytes

lich: lichens

litt: litter

bare: bare ground

stone: stones (> 2 cm)

org: organic crust

 Shrub species height (maximum vegetative height) [mm] and cover (estimated in 1x1 m quadrat) [%]

alcr_c: _Alnus crispa_ cover

alcr_h: _Alnus crispa_ height

bena_c: _Betula nana_ cover

bena_h: _Betula nana_ height

dila_c: _Diapensia lapponica_ cover

dila_h: _Diapensia lapponica_ height

emni_c: _Empetrum nigrum_ cover

emni_h: _Empetrum nigrum_ height

hahy_c: _Harimanella hypoides_ cover

hahy_h: _Harimanella hypoides_ height

lopr_c: _Loiseleuria procumbens_ cover

lopr_h: _Loiseleuria procumbens_ height

oxpa_c: _Oxycoccus palustre_ cover

oxpa_h: _Oxycoccus palustre_ height

phca_c: _Phyllodoce caerulea_ cover

phca_height: _Phyllodoce caerulea_ height

rhod_c: _Rhododendron_ sp. cover

rhod_h: _Rhododendron_ sp. height

saar_c: _Salix arctica_ cover

saar_h: _Salix arctica_ height

sagl_c: _Salix glauca_ cover

sagl_h: _Salix glauca_ height

sahe_c: _Salix herbacea_ cover

sahe_h: _Salix herbacea_ height

vaul_c: _Vaccinium uliginosum_ cover

vaul_h: _Vaccinium uliginosum_ height

drin_c: _Dryas integrifolia_ cover

drin_h: _Dryas integrifolia_ height

 Community characteristics

rich_pa: shrub species richness at grid level

rich: shrub species richness averaged across 3 1x1 m sampling quadrats per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on _Betula nana_ (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on _Salix glauca_ (i.e. summed cover of non-_S. glauca_ species) [% cover]

ht_cwm: maximum shrub canopy height (i.e. community weighted mean by abundance of maximum shrub height) [mm]

 Topography and terrain wetness (calculated from)

tcws: Tasseled Cap Wetness component (Sentinal images)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

 Downscaled climate (time series 1984 – 2013)

insoljja: average June-August insolation [MJ cm-2 yr-1]

precipmam: cumulative March-May precipitation [mm]

tempcont: yearly maximum – minimum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

precipjja: cumulative June-August precipitation [mm]

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

en: Nuup Kangerlua

en:
 Collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen)

These data collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen). Please contact J. Nabe-Nielsen (jnn_at_ecos.au.dk) to request access to data on all surveyed shrub species. Available here are only data used in Chardon et al. 2022.

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

year: sampling year

alt: average altitude of grid as recorded during field surveys [m]

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

 Species presence [1] or absence [0] at grid level

pa_bena: _Betula nana_

pa_sagl: _Salix glauca_

 Shrub species cover (calculated from pin hits in 0.7 x 0.7 m frame, based on total of 25 hits) [%]

cov_bena: _Betula nana_ cover

cov_sagl: _Salix glauca_ cover

 Downscaled climate (time series 1984 – 2013)

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

tempcont: yearly maximum – minimum temperature [ºC]

precipmam: cumulative March-May precipitation [mm]

precipjja: cumulative June-August precipitation [mm]

insoljja: average June-August insolation [MJ cm-2 yr-1]

 Topography and terrain wetness (calculated from)

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

tcws: Tasseled Cap Wetness component (Sentinal images)

 Community characteristics

rich: shrub species richness averaged across pin-point frames per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on Betula nana (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on Salix glauca (i.e. summed cover of non-_S. glauca_ species) [% cover]

en: GitLab Repository

en: Zip drive of GitLab repository contents (https://gitlab.com/nathaliechardon/gl_microclim).

en:
 Study Aim

We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns.

 Data Description

Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables.

 Nuup Kangerlua (Godthåbsfjord)

_Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness

 Kangerluarsunnguaq (Kobbefjord)

Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height

 Methods

 Field survey in Nuup Kangerlua

We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022).

 Field survey in Kangerluarsunnguaq

We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022).

 Biotic variables

We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height.

 Climate variables

We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June – August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March – May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021).

 Topography and terrain wetness indicator variables

We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how ‘wet’ an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness.

 Computer code

Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim)

 Third-party data

Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).

accessible-shrub-forest

display_name: accessible-shrub-forest

arctic

display_name: arctic

climate

display_name: climate

field-data

display_name: field-data

sdm

display_name: sdm

validation

display_name: validation

en: Greenland shrubs and microclimate


disdrometer-data-laret

maintainer: Daniel Wolfensberger

title_for_slug: disdrometer-data-laret

en: raclets precipitation visibility disdrometer hydrometeors

en: Disdrometer Data Laret

media_type: application/zip

en: The zip folder contains one netCDF file per day for Laret from February 7 to March 29 2019 with a temporal resolution of 30 seconds (time in UTC). The naming convention for the files is Laret_YYYYMMDD.nc and the files include the following: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

format: ZIP

mimetype: application/zip

en:
A laser optical disdrometer (Parsivel² , OTT Hydromet) was used to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms.
The dataset contains information on precipitation amount and type for the period of February 7 to March 29 2019 in Laret.

disdrometer

display_name: disdrometer

hydrometeors

display_name: hydrometeors

raclets

display_name: raclets

visibility

display_name: visibility

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: aerodynamic-entrainment saltation-parameterizations drifting-snow snow-saltation splash large-eddy-simulation

en: Code and input files to reproduce the simulations in Melo et al. (2024), as well as the post-processing scripts used in the analysis.

en:
This dataset includes the necessary files to reproduce the results presented in Melo et al. (2024):
- code version used in the simulations (also available at the institutional [GitLab repository](https://gitlabext.wsl.ch/atmospheric-models/les-lsm) - tag 'r2024')
- input files to run the simulations
- post-processing scripts (MATLAB and FORTRAN)
- selected raw output files
- post-processed results (MATLAB data files).
 	**Note:** Direct model outputs are not provided because of their size, but they can be shared upon request. They can be reproduced by running the code. The post-processed results allow you to reproduce all figures in Melo et al. (2024).

aerodynamic-entrainment

display_name: aerodynamic-entrainment

drifting-snow

display_name: drifting-snow

large-eddy-simulation

display_name: large-eddy-simulation

saltation-parameterizations

display_name: saltation-parameterizations

snow-saltation

display_name: snow-saltation

splash

display_name: splash

en: Understanding snow saltation parameterizations - Reproducibility


maintainer: Francesco Comola

en: wind sand cohesion saltation snow sediment

en: Output of the model simulations

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

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

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

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

cohesion

display_name: cohesion

saltation

display_name: saltation

sand

display_name: sand

sediment

display_name: sediment

snow

display_name: snow

wind

display_name: wind

en: Saltation of cohesive granular materials


en: ecosystem-services artificial-lighting sustainability alan outdoor-lighting

media_type: text/csv

en: Raw data for flying insects analysis

format: CSV

en: Raw data Moran's I test

en: R code for PERMANOVA Analysis of ground-dwelling arthropods.

format: OCTET

en: R code for NMDS of flight-active insects

en: R code for NMDS of ground-dwelling arthropods

en: Raw data for ground-dwelling arthropod analysis

en: Raw data for ground-dwelling arthropod NMDS analysis

en: Raw data for flying insects NMDS analysis

en: Raw data for flying insects and ground-dwelling arthropod combined analysis

en: R code of flight-active insects analysis.

en: R code of ground-dwelling arthropod analysis.

en: R code for PERMANOVA Analysis of flight-active insects.

en: R code of flight-active and ground-dwelling arthropod combined analysis.

en: Raw data and R Code to do all the analysis performed for the paper "Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods". Including data for both flight-active insects and ground-dwelling arthropods combined (PT_TRF_all.csv), flight-active insects alone (ALANeX_PT_all_clean_control.csv) and ground-dwelling insects alone (ALANeX_TRF_all_clean_control.csv). For the NMDS analysis, there is a data set for the flight-active insects (ALANeX_PT_NMDS.csv) and the ground-dwelling arthropods (ALANeX_TRF_NMDS.csv). For the Morans I use the 'ALANeX_MoransI.csv' dataset.

alan

display_name: alan

artificial-lighting

display_name: artificial-lighting

ecosystem-services

display_name: ecosystem-services

outdoor-lighting

display_name: outdoor-lighting

sustainability

display_name: sustainability


en: dryocosmus-kuriphilus southern-switzerland population-evolution castanea-sativa torymus-sinensis biological-control pest-invasion

en:
Data has been collected from summer 2011 to 2016.

Main reasons for NA values:

-	D. kuriphilus still not arrived in the site.
-	No galls were found during the field sampling.
-	Site has not been sampled in the correspondent year because of time, weather or money constrains.
-    T. sinensis not found in the galls

Variable description:

Year = sampling year.

Coordinate, elevation, slope, aspect = average site characteristics.

ts_arrival = T. sinensis arrival year.

dk_arrival = D. kuriphilus arrival year.

dk_parasitism_by_ts = D. kuriphilus parasitism rate by T. sinensis. Formula: (no. of T. sinensis/ no. of D. kuriphilus chambers) * 100 at gall level.

dk_infestation = Percentage of attacked buds. Formula: no. of attacked buds / no. of total developed buds*100 (as presented in Kotobuki et al. 1985) at shoot level.

ts_per_bud = number of alive T. sinensis individual calculated at bud level.

dk_per_bud = number of alive D. kuriphilus individual calculated at bud level.

dk_pot_per_bud = overall number (dead and alive) D. kuriphilus individual calculated at bud level.

total_gall_nr_per_bud = average gall number per bud.

chamber_per_galls = average D. kuriphilus chamber number per gall.

For details see the submitted publication with the same name

References
Kotobuki, K., Mori, K. and Sato, Y. (1985) ‘2 methods to estimate the tree damage by chestnut gall wasp Dryocosmus-kuriphilus’, Bulletin of the fruit tree research station A (Yatabe), 2(12), pp. 29–36.

mimetype: text/csv

en:
This dataset contains the population evolution of a pest and its biocontrol agent in terms of presence proportion at gall level and absolute number of insects.

The study area extends from the Cuneo region (Piedmont, Italy) to southern Switzerland. In order to provide a complete range of data covering the entire process from the pest arrival to complete biological control by its natural enemy T. sinensis, a space-for-time substitution approach has been adopted so as to create a temporal gradient of the epidemic stages over the whole study area.

The southernmost Swiss sites roughly represent the arrival and establishment of the pest without the presence of the natural enemy, the central ones the early epidemic stage and the epidemic peak, whereas the northern ones the end of the epidemic with the beginning of the biocontrol. The Italian ones represent the beginning of the equilibrium between the two population as well as the situation with stable T. sinensis populations on the long term.

These data are used in the paper entitled: Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions

biological-control

display_name: biological-control

castanea-sativa

display_name: castanea-sativa

dryocosmus-kuriphilus

display_name: dryocosmus-kuriphilus

pest-invasion

display_name: pest-invasion

population-evolution

display_name: population-evolution

southern-switzerland

display_name: southern-switzerland

torymus-sinensis

display_name: torymus-sinensis

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.


historical-vegetation-height-model-nfi

maintainer: Mauro Marty

title_for_slug: historical-vegetation-height-model-nfi

en: nfi historical vhm photogrammetry forest dsm

en: 1985-1991 Vegetation Height Model

format: GeoTIFF

en: 1979-1985 Vegetation Height Model

en: 1998-2006 Vegetation Height Model

media_type: text/plain

en: Description of available data

format: TXT

en: 1990-1998 Vegetation Height Model

en:
The main datasets available are 4 nationwide **Digital Surface Models (DSMs)** and corresponding **Vegetation Height Models (VHMs)** with a spatial resolution of 1 m.
The Vegetation Height Models are calculated by subtraction of a Digital Terrain Model (swissAlti3d version 2017, swisstopo) from the generated DSMs. DSMs were derived by image matching of **scanned historical images** (panchromatic 8-bit) acquired with a RC10 frame camera, in the context of the swisswide image acquisition campaigns of swisstopo.
Nationwide products could be generated for the **four epochs**
 1979-1985 / 1985-1991 / 1990-1998 / 1998-2006.
Additional information about successful image matching vs. interpolation of pixels and about the year of image acquisition are available in the corresponding files (**matched_area_xxxx_xxxx_binay.tif** and **meta_acquisition_year_xxxx_xxxx.json**, respectively).
To separate vegetation from non-vegetation areas a mask derived from a combination of information of the Topographic landscape model - TLM (swisstopo) and a NDVI layer was used and is supplied as **"TLM_mask.tif"**
Due to the occurrence of unrealistic vegetation height values caused by the failure of the image matching process (mainly overexposed images and clouds or very steep rocky terrain), pixel values > 60 m were set to NoData and < 0 m to 0.
All datasets are available in the Reference System **LV95 (EPSG: 2056) LN02 (EPSG: 5728)** and share the common **extent definition** (xmin ymin xmax ymax): 2480000 1070000 2840000 1300000

dsm

display_name: dsm

forest

display_name: forest

historical

display_name: historical

nfi

display_name: nfi

photogrammetry

display_name: photogrammetry

vhm

display_name: vhm

en: Historical Vegetation Height Model NFI


maintainer: Andrin Caviezel

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

en: Induced Rockfall Data, Tschamut, GR

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

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

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

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

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

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

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

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

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

Further information is found in

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

dem

display_name: dem

deposition-points

display_name: deposition-points

induced-rockfall

display_name: induced-rockfall

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

sensor-stream

display_name: sensor-stream

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata

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


maintainer: Manuel R. Popp

en: species-observations photo plant-species vascular-plants testing

en:
Zipped directory containing approximately 5'600 photographs of vascular plants from the Swiss flora, taken at 153 sampling locations. Sampling locations were assigned a TypoCH habitat type.
Folder structure:
 - Each folder represents a plant population, defined by the same species, sampling date, and location.
 - Higher-level directories organize populations observed within the same sampling plot.
Naming convention:
Each image is names img_<index>_<plant organ>.jpg whereby <index> is the number of the photograph within the folder. For each observed population, images have consecutive integers as index. <plant organ> is a single letter indicating the depicted part(s) of the plant:
b = bark
i = inflorescense
f = infructescense
s = several parts
v = vegetative parts

en:
This data set contains roughly 5600 photographs of plants and plant parts from 153 sites recorded in all major biogeographic regions of Switzerland using smartphone cameras. Image names contain a letter referring to depicted plant parts: b = bark, i = inflorescence, f = infructescense, s = several parts, v = vegetative parts.
Photos are grouped in folders by observation (individual or population), i.e., photos in the same folder are of the same species, observed at the same time and sampling plot. At a higher organisational level, these folders are grouped by sampling plot (usually 3×3 m in open areas and 10×10 m in forests). The main folder contains metadata including taxon name, sampling time and location, as well as the TypoCH habitat type encoded as integers.

photo

display_name: photo

plant-species

display_name: plant-species

species-observations

display_name: species-observations

testing

display_name: testing

vascular-plants

display_name: vascular-plants

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Photos of vegetative and reproductive parts of 560 vascular plant species taken in all biogeographic regions of Switzerland


en: topography land-use-and-cover-change forest-gain urban-sprawl urban-morphology nature-conservation built-up-classification basic-services spatial-modelling urban-compactness planning-outcome forest-persistence

en: Native forest cover persistence from 2005 to 2015

en: Native forest cover gains from 2005 to 2015

en: Increase in waste collection service

en: Descricao dos dados em portiuguês

en: Portugues description

en:
Basic education in % per census sector (change per
year)

en: Master plan Ubatuba (zones)

en: infill

en: New built-up pixels classified as infill

en: Axial

en: New built-up pixels classified as axial

en: Increase in Normalized Difference Vegetation Index

en: Urban persistence: Built-up 2005 = Built-up 2015

en: Increase in sanitation service

en: slope

en: Slope °

en: Permanent housing density increase

en: Topographic Index Position (TPI)

en: Ecological-Economic Zoning (EEZ)

format: SHP

en: Population density per pixel (change per year)

en: Increase in clean water provision service

en: isolated

en: New built-up pixels classified as isolated

en: Variables description

en: Table of content. Variables description, source, and other important information

en: Mean income in Reais (R$) per census sector (change per year)

en: Human Development Index (HDI) per municipality (change per year)

en: Master plan Ihabela (zones)

en:
The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation.

We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality.

1)	Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase

We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software.

Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015).

To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication).

The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality.

2)	Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population:

The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication).

3)	Topographic drivers

To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).

basic-services

display_name: basic-services

built-up-classification

display_name: built-up-classification

forest-gain

display_name: forest-gain

forest-persistence

display_name: forest-persistence

land-use-and-cover-change

display_name: land-use-and-cover-change

nature-conservation

display_name: nature-conservation

planning-outcome

display_name: planning-outcome

spatial-modelling

display_name: spatial-modelling

topography

display_name: topography

urban-compactness

display_name: urban-compactness

urban-morphology

display_name: urban-morphology

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: Spatially explicit data to evaluate spatial planning outcomes in a coastal region in São Paulo State, Brazil


maintainer: Virginie Marques

en: marine environmental-dna indian-ocean edna teleo laperouse coral-reef fish-composition

en: Data access

en: Please use cyberduck to download for the moment

en:
Fish environmental DNA data set collected in the shallow seamount of La Pérouse located between Madagascar and Réunion Island

In 2019, we sampled the shallow seamount of La Pérouse, located between Madagascar and Réunion Island in the WIO. La Pérouse is an extinct volcano with a summit depth at 60 m below the sea surface. The plateau of the summit has a maximum length of 12 km and a maximum width of 4 km. One side of the seamount might have collapsed in the past, leading to a less common crescent-shaped summit. The abyssal plains that surround this isolated pinnacle are at a depth of 5000 m. Despite its proximity to Réunion Island (160 km northwest), La Pérouse and its surroundings are still poorly studied.
To sample eDNA in this location we used a submersible pump (Subspace, Geneva, Switzerland; nominal flow of ~ 1.0 L/min) to collect samples at various depths during close circuit rebreather dives. The pump has an internal battery and can be activated underwater allowing in situ filtration directly at the targeted habitat and depth. The filtration lasted for 30 min to collect a total water volume of ~ 30 L. During close circuit rebreather dives, water samples from various depths, ranging from 60 to 140 m below the sea surface, were collected using only the Submersible method (eight stations: no replicates). Out of the eight water samples, six were collected during the day, while two were collected at night (IS2_1 at 62 m and IS7_1 at 100–125 m). We applied this filtration protocols to collect and filter water throughout a VigiDNA® 0.2 μM cross-flow filtration capsule (SPYGEN, le Bourget du Lac, France) using disposable sterile tubing for each filtration capsule. After the filtration process, the remaining water in the capsules was emptied. The capsules were then filled with 80 ml of lysis conservation buffer (CL1 buffer SPYGEN, le Bourget du Lac, France) and stored at room temperature in the dark. At La Pérouse, a total of eight water samples were collected during the MONT LA PÉROUSE expedition (https://gombessa-expeditions.com/mont-la-perouse/) on board the ship La Curieuse to the seamount La Pérouse from October 27th to November 5th 2019. We followed a strict contamination control protocol in both field and laboratory stages. Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

coral-reef

display_name: coral-reef

edna

display_name: edna

environmental-dna

display_name: environmental-dna

fish-composition

display_name: fish-composition

indian-ocean

display_name: indian-ocean

laperouse

display_name: laperouse

marine

display_name: marine

teleo

display_name: teleo

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Environmental DNA Marine France LaPerouse 2019


modeling-snow-failure-with-dem

title_for_slug: modeling-snow-failure-with-dem

en: snow-avalanche discrete-element-modelling snow-failure snow snow-micro-mechanics

en: Python file

en: Python file to reproduce the figures in Bobillier et al. (2019)

mimetype: text/x-python

en: Dataset

en: The zip file contains the data underlying the figures presented in Bobillier et al. (2019). The figures can be reproduced with the python file, which is also provided.

en: Read-me

en: The read-me file contains a description of all the data provided in the zip file.

mimetype: application/pdf

en: This data set includes the modeling results described in the research article by Bobiller et al. (2020). All the figures in the article can be reproduced with the data provided.

discrete-element-modelling

display_name: discrete-element-modelling

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-micro-mechanics

display_name: snow-micro-mechanics

en: Modeling snow failure with DEM


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

en: Postprocessing

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

en: WPSSetup

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

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

en: WRFrun

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

en: WPSrun

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

en: MeteorologicalData

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

envidat

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


maintainer: Frank Hagedorn

en: forest plfa c13-labeling biogeochemical-cycling microbial-biomass drought

en: Gao_Frank_C13 pulse labeling.xlsx

en:
Data of manuscript by Gao t al. : Drought resistance and resilience of rhizosphere communities in forest soils from the cellular to ecosystem scale – insights from 13C pulse labeling
Inludes data on 13C pulse-labeling experiment in young beech model ecosystems and a mature pine forest. Data of soil climate, total PLFA, 13C label strength in PLFA of pulse-labeling carried out under drought and after rewetting

en: Data of 13C-pulselabeling experiments in a young beech model ecosystem and a mature pine forest exposed to drought and rewetting. Data include microclimate during the experiment, total PLFA contents, d13C signatures in PLFA , microbial biomass, soil-respired CO2 measure during 30 days following 3-4 hour long pulse labeling with highly 13C-enriched CO2.

biogeochemical-cycling

display_name: biogeochemical-cycling

drought

display_name: drought

forest

display_name: forest

microbial-biomass

display_name: microbial-biomass

plfa

display_name: plfa

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Gao_Drought resistance and resilience of rhizosphere communities_13C-PLFA


maintainer: Felix Neff

en: diversity functional-diversity insects restoration topsoil-removal grassland functional-traits long-term-experiment

en: Insect traits

en:
Functional traits of all herbivorous insect species sampled in the study.

- **Species**: Insect species name (following [Fauna Europaea](https://fauna-eu.org))
- **Author**: Author and year of species' description (following [Fauna Europaea](https://fauna-eu.org))
- **Group**: Insect group as used in the analyses
- **Body volume**: Body volume determined by body length, body width and body height (in mm$^3$)
- **Body_shape**: Ratio of body length to body width
- **Hind_femur_shape**: Ratio of hind-femur length to hind-femur width
- **Hind_front_leg_ratio**: Ratio of front-leg length to hind-leg length (leg length = femur length + tibia length)
- **Wing_length_rel**: Ratio of wing length to body length
- **Leg_length_rel**: Ratio of front-leg length to body length (leg length = femur length + tibia length)
- **Antenna_length_rel**: Ratio antenna length to body length
- **Eye_width_rel**: Ratio of eye width to head width (Auchenorrhyncha, Heteroptera) / to head height (Orthoptera)
- **Feeding_specialization**: Insect specialization on food source (*m*: monophagous, *o*: oligophagous, *p*: polyphagous)
- **Feeding_tissue**: Feeding tissue used for feeding (*c*: chewers, *x*: xylem, *p*: phloem, *m*: mesophyll, *m-p/r*: mesophyll as well as phloem and/or reproductive organs, *r*: reproductive organs, *se*: ripe seeds)
- **Hibernation**: Hibernation stage (*e*: egg, *e/n*: egg and nymph, *n*: nymph, *a*: adult)
- **Generations**: Number of generations per year (*1.5* stands for *1-2* generations per year)
- **Moisture_pref_mean**: mean of preferred habitat moistures (*1*: xerophilous, *2*: moderately xerophilous, *3*: moderately mesohygrophilous, *4*: mesohygrophilous, *5*: moderately hygrophilous, *6*: hygrophilous, *7*: moderately hydrophilous, *8*: hydrophilous)
- **Moisture_pref_range**: range of preferred habitat moistures (indicating degree of specialization)

en: Insect abundance

en:
Abundance of adult insects recorded in the samplings on all experimental sites

- **Plot**: ID of Plot
- **Treatment**: Experimental treatment
- **Species**: Insect species name (following [Fauna Europaea](https://fauna-eu.org))
- **Author**: Author and year of species' description (following [Fauna Europaea](https://fauna-eu.org))
- **Group**: Insect group as used in the analyses
- **Number_Adults**: Number of adult individuals recorded
- **Herbivore**: Logical indicating whether the species is feeding as a herbivore (*TRUE*) or not (*FALSE*)

en: Plot information

en:
Information on the experimental plots.

- **Plot**: ID of Plot
- **Treatment**: Experimental treatment
- **Landuse_history**: recent agricultural use for Initial; agricultural use before restoration for all restored sites
- **Last_tilled**: Last time the site was tilled
- **Intensity**: Frequency and intensity of mowing and manuring (*intensity1*: mowing once and no manure, i*ntensity2*: 2-3 times mowing and no manure, *intensity3*: 2-3 times mowing and manuring, *intensity4*: 4-5 times mowing and manuring)
- **Slope**: slope gradient according to classes defined by FAO (1990)
- **X**: X coordinate of the plot (CH1903)
- **Y**: Y coordinate of the plot (CH1903)
- **Cluster**: Spatial cluster of the plot

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

**Paper Citation**:

Neff, F., Resch, M. C., Marty, A., Rolley, J. D., Schütz, M., Risch, A. C, Gossner, M. M. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands: a functional approach. Ecological Applications, 30, e02133. [10.1002/eap.2133](https://doi.org/10.1002/eap.2133)

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

 Methods

 Study site
The study area is situated within and nearby to Eigental nature reserve (47°27’36” to 47°29’06” N, 8°37’12” to 8°37’44” E, 461 to 507 m a.s.l.) in the vicinity of Zurich airport (Canton Zurich, Switzerland). Mean annual precipitation and temperature is 903 ± 136 mm and 9.14°C ± 0.50°C (mean ± SD for 2007-2017 (*MeteoSchweiz 2018*)). In 1967, the Eigental nature reserve was established to protect small and isolated remnants of species-rich, semi-natural grasslands (roughly 12 ha), which were embedded in an otherwise intensively managed landscape. It is characterized by oligo- to mesotrophic Molinion (semi-wet, matrix species *Molinia caerulea*) and Mesobromion (semi-dry, matrix species *Bromus erectus*) meadows (*Delarze et al. 2015*), reflecting small-scale habitat heterogeneity, mainly due to site-specific groundwater levels and slope inclination. As in most Central European grasslands, management is necessary to prevent shrub and tree invasions as well as to secure low levels of available soil nutrients and thus to maintain these species-rich habitats ([*Poschlod and WallisDeVries 2002*](https://doi.org/10.1016/S0006-3207(01)00201-4)).
In 1990, the government of the Canton Zurich decided to enlarge the Eigental nature reserve as a counter measure against degradation and biodiversity loss in semi-natural grasslands due to overutilization and the excessive input of nutrients (mostly nitrogen). Eleven patches of adjacent intensively managed grassland (in total roughly 20 ha) were targeted to be transformed into semi-natural grasslands. As a first restoration measure, fertilization was ceased, and biomass harvested three times to remove excessive soil nutrients from the original system and thus benefit plant species with low competitive ability on the long run. In 1995, the restoration efforts were increased and a large-scale experiment comprising three restoration measures with increasing intervention intensities was implemented:

- **Harvest only**: Initial restoration measures were continued with mowing and removing of the aboveground biomass two times a year (early summer and autumn).
- **Topsoil**: Removal of topsoil, depending on the thickness of the A horizon the upper 10 to 20 cm, in four randomly selected areas within the eleven patches in late autumn 1995. The size of the restoration area depended on individual patch size (2700 to 7000 m2).
- **Topsoil + Propagules**: Plant propagules were added on half of the area where topsoil was removed via application of fresh, seed-containing hay and hand-collected propagules of target species originating from semi-dry and semi-wet species-rich grasslands with local and regional provenance (within radius of 7 to 30 km) (1995, 1996, 1997).

Management of *Topsoil* and *Topsoil + Propagules* started five years after treatment implementation and included yearly mowing and removing of aboveground biomass (late summer or early autumn). The experiment was complemented with intensively managed grassland sites that share the same agricultural history as the restored sites (**Initial**; swards dominated by *Lolium perenne*, *L. multiflorum* and *Trifolium repens*): mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes. Finally, sites were selected in target semi-dry and semi-wet grasslands (**Target**) located within the Eigental nature reserve and another nature reserve nearby (Altläufe der Glatt; 47°28’29” to 47°27’41” N, 8°31’56” to 8°32’26” E, 418 to 420 m a.s.l.). The selected target sites are mown and aboveground biomass removed once a year in late summer or early autumn. For each of the five treatments, we selected eleven plots (5 m × 5 m) spread across the sites. Altogether, the experiment included 55 plots.

 Arthropod sampling
Aboveground arthropods were sampled using suction sampling on four consecutive days in early July 2017 before the grasslands were mown. Arthropods were sampled in two locations on each 5 m × 5 m plot, once in the south-western and once in the north-eastern corner to account for possible spatial heterogeneity within the plots. Arthropods were sorted to order or lower taxonomic levels and individuals were identified to species level. We focused on three groups (Hemiptera: Auchenorrhyncha, Hemiptera: Heteroptera, Orthoptera),

 Functional traits
We used two sets of functional traits in this study.

**Morphometric traits**:
Body volume, body shape, hind femur shape, hind/front leg ratio, wing length, leg length, antenna length and eye width. We used trait measurements from [*Simons et al. (2016)*](http://dx.doi.org/10.1890/15-0616.1) and [*Neff et al. (2019)*](https://doi.org/10.1007/s10980-019-00872-1) and complemented them with measurements on study specimens. These measurements were conducted using a high-resolution measuring stereo microscope (Leica DVM6, Leica Microsystems) including automated high-resolution photo stacking with the software Leica Application Suite X (LAS X, © 2018 Leica Microsystems CMS GmbH) and Leica Map Premium (Leica Microsystems, © 1996-2017 Digital Surf) at WSL Birmensdorf. The eight morphometric traits were calculated from direct measurements of body parts on specimens of all sampled species. From each species, we measured at least one female and one male specimen. Additionally, for species that show wing dimorphism, we included the different wing morphs and weighted them by their prevalence reported in literature. For few species, of which not all wing morphs were available for measurements (10 cases), we estimated relative wing length from congeneric species or from the literature.

**Life-history traits**
Based on an existing data set collected by [*Gossner et al. (2015)*](http://dx.doi.org/10.1890/14-2159.1). We included traits describing different life-history characteristics of herbivore insect species, namely: feeding specialization, feeding tissue, hibernation stage and number of generations per year, which are related to insect species’ vulnerability to changes in plant community composition, microhabitat use and disturbance tolerance. To represent potential changes in habitat moisture with abandonment of intensive land use (e.g., change in ground-water level), we also included two traits related to preferred habitat moisture of the study species: moisture preference, describing species’ optimum habitat moisture, and moisture range, which describes the species’ range of preferable moisture conditions.

 References

Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten. 3rd ed. Ott, Bern.

Gossner, M. M., N. K. Simons, R. Achtziger, T. Blick, W. H. O. Dorow, F. Dziock, F. Köhler, W. Rabitsch, and W. W. Weisser. 2015. A summary of eight traits of Coleoptera, Hemiptera, Orthoptera and Araneae, occurring in grasslands in Germany. Scientific Data 2:150013.

MeteoSchweiz. 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Zürich.

Neff, F., N. Blüthgen, M. N. Chisté, N. K. Simons, J. Steckel, W. W. Weisser, C. Westphal, L. Pellissier, and M. M. Gossner. 2019. Cross-scale effects of land use on the functional composition of herbivorous insect communities. Landscape Ecology 34:2001–2015.

Poschlod, P., and M. F. WallisDeVries. 2002. The historical and socioeconomic perspective of calcareous grasslands—lessons from the distant and recent past. Biological Conservation 104:361–376.

Simons, N. K., W. W. Weisser, and M. M. Gossner. 2016. Multi-taxa approach shows consistent shifts in arthropod functional traits along grassland land-use intensity gradient. Ecology 97:754–764.

diversity

display_name: diversity

functional-diversity

display_name: functional-diversity

functional-traits

display_name: functional-traits

grassland

display_name: grassland

insects

display_name: insects

long-term-experiment

display_name: long-term-experiment

restoration

display_name: restoration

topsoil-removal

display_name: topsoil-removal

en: Grassland restoration: insects and insect traits


maintainer: Dirk Nikolaus Karger

en: precipitation climate-layers climate free-climate-data climatology bioclim temperature climate-change

en: CHELSA V1.2 (deprecated)

en: CHELSA Version 1.2 based on ERA-Interim forcings and CMIP5

en: CHELSA V2.1 (current)

en: CHELSA Version 2.1 based on ERA5 forcings and CMIP6

en: CHELSA Technical Documentation

en: Technical documentation document including the variable names, units, scaling, offset, and background.

en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled temperature and precipitation to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction.

CHELSA data published in EnviDat includes the deprecated version 1.2 (originally published under 10.5061/dryad.kd1d4). Please use the current 2.1 version.

Paper Citation:
 > _Karger DN. et al. Climatologies at high resolution for the earth’s land surface areas, Scientific Data, 4, 170122 (2017) [doi: 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._

bioclim

display_name: bioclim

climate

display_name: climate

climate-change

display_name: climate-change

climate-layers

display_name: climate-layers

climatology

display_name: climatology

free-climate-data

display_name: free-climate-data

precipitation

display_name: precipitation

temperature

display_name: temperature

envidat

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


maintainer: Yohann Chauvier

en: covariate-correction independent-dataset plant-species target-group cluster survey-effort point-process-model background-data random-stratified-sampling environmental-stratification

en: Non-copyright data, scripts, diversity outputs

en:
Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models.

Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1’900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.

Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r≃0.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.

background-data

display_name: background-data

cluster

display_name: cluster

covariate-correction

display_name: covariate-correction

environmental-stratification

display_name: environmental-stratification

independent-dataset

display_name: independent-dataset

plant-species

display_name: plant-species

point-process-model

display_name: point-process-model

random-stratified-sampling

display_name: random-stratified-sampling

survey-effort

display_name: survey-effort

target-group

display_name: target-group

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Novel methods to correct for observer and sampling bias in presence-only species distribution models


maintainer: Jana von Freyberg

en: catchment alptal hydrology stream-water precipitation stable-water-isotope

en: Data Set Documentation: alptal_Isotopes_daily_2015-2019_v2

en: Documentations contains meta data of the data columns and information about the study catchments.

en: Single comma-separated txt-file with daily data of stable water isotopes measured in streamwater and precipitation in the Swiss Alptal catchment and two of its tributaries (Erlenbach and Vogelbach), as well as daily data of key hydrometeorological variables from the Alptal valley

en:
Version 2 of the dataset contains additional information on:
- line-conditioned (lc) excess
- precipitation sample volumes
- isotope data quality

en: This dataset contains four years of daily measurements of the natural isotopic composition (2H, 18O) of precipitation and stream water at the Alp catchment (area 47 km2) in Central Switzerland and two of its tributaries (0.73 km2 and 1.55 km2). In addition, the dataset contains daily measurements of key hydrometeorological variables.

alptal

display_name: alptal

catchment

display_name: catchment

hydrology

display_name: hydrology

stable-water-isotope

display_name: stable-water-isotope

stream-water

display_name: stream-water

en: Four years of daily stable water isotope data in stream water and precipitation from three Swiss catchments


en: remote-sensing sentinel-2 time-series switzerland tree-species

en: Tree species profiles for 2020, CIre index

en: Tree species profiles for 2020, EVI index

en: Tree species profiles for 2019, SWIR-2 band

en: Tree species profiles for 2019, NIR band

en: Tree species profiles for 2020, Red band

en: Tree species profiles for 2019, Green band

en: Tree species profiles for 2020, Red-Edge-2 band

en: Tree species profiles for 2020, Green band

en: Tree species profiles for 2019, Blue band

en: Tree species profiles for 2020, NDVI index

en: Tree species profiles for 2020, CCI index

en: Tree species profiles for 2019, CIre Index

en: Tree species profiles for 2019, SWIR-1 band

en: Tree species profiles for 2020, Red-Edge-3 band

en: Tree species profiles for 2020, Blue band

en: Tree species profiles for 2019, Red-Edge-1 band

en: Tree species profiles for 2019, CCI index

en: Tree species profiles for 2019, Red-Edge-2 band

en: Tree species profiles for 2019, EVI index

en: Tree species profiles for 2019, NDMI/NDWI index

en: Tree species profiles for 2019, NDVI index

en: Tree species profiles for 2020, SWIR-2 band

en: Tree species profiles for 2019, Red band

en: Tree species profiles for 2020, NIR band

en: Tree species profiles for 2019, Red-Edge-3 band

en: Tree species profiles for 2020, Red-Edge-1 band

en: Tree species profiles for 2020, NDMI index

en: Tree species profiles for 2020, SWIR-1 band

en:
 Tree species profiles from Sentinel-2 time series
We obtained tree species-specific Sentinel-2 time series for the years 2019 and 2020. The Sentinel-2 time series originated from [here](https://www.envidat.ch/#/metadata/sentinel-2-time-series-of-switzerland), and the tree species information was derived from pure plots of the Swiss National Forest Inventory, spread all over Switzerland.
In addition to the 10 and 20 m bands from Sentinel-2 (Blue, Green, Red, Red-Edge-1, Red-Edge-2, Red-Edge-3, NIR, SWIR-1, SWIR-2), we calculated indices (CCI, CIre, EVI, NDMI/NDWI, NDVI).
In total, tree species profiles of 763 pixels for 2020 and 562 pixels for 2019 are available for seven tree species (*Abies alba*, *Castanea sativa*, *Fagus sylvatica*, *Fraxinus excelsior*, *Larix* spp., *Picea abies*, *Pinus sylvestris*).

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

time-series

display_name: time-series

tree-species

display_name: tree-species


maintainer: Axel Volkwein

en: experiments experiment field-tests field-test impact-testing rockfall-experiments rockfall field-data mass-movements natural-hazard

en: Video analysis

en: The zip contains five Excel-files that in turn contain the results of the performed video analyses. The sheet "Daten" contains the pixels of different positions on the blocks that were tracked by the software "Kinovea". The sheet "Auswertung" determines the block kinetics based on the pixel data over time.

en: Raw data (high speed videos, acceleration measurements, laboratory tests)

en:
This file contains

* the high speed videos showing the concrete blocks impacting the gallery roof.
* the raw data of the acceleration measurements
* the data of Tiefbauamt Graubünden containg the tension tests of gallery reinforcment, ME-testing the cushion layer and the tests on concrete samples.

en: Acceleration measurements

en: The zip file contains five excel files, one for each test conducted. The first sheet contains the raw data and a data set that went through a low-pass filter. The sheet "Allgemein" contains calibration factors for the sensors and time details of a test. The sheets "DiaDaten" and "DiaDatenGefiltert" show the measurements over time. The block and slab kinetics are determined in the sheets "ZeitintegrationBlock" and "ZeitintegrationPlatte". The sheet "VergleichBeschleunigungen" shows the influence of the low-pass-filter and a running average filter on the block accelerations.

en: Five full-scale field tests were conducted with concrete blocks weighting between 800 and 3200 kg being dropped onto the roof of a gallery structure made from reinforced concrete. The impacts were recorded using high-speed video and acceleration measurements at the falling blocks. The dataset contains the raw data as well as the analyses of the block trajectories, i.e. kinetics and dynamics. Setup of the measurements and the analyses conducted are published in Volkwein, A. "Durchführung und Auswertung von Steinschlagversuchen auf eine Stahlbetongalerie", WSL-Berichte, Heft 68, 2018.

experiment

display_name: experiment

experiments

display_name: experiments

field-data

display_name: field-data

field-test

display_name: field-test

field-tests

display_name: field-tests

impact-testing

display_name: impact-testing

mass-movements

display_name: mass-movements

natural-hazard

display_name: natural-hazard

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

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: Rockfall gallery testing Parde 2016


disdrometer-data-davos-wolfgang

title_for_slug: disdrometer-data-davos-wolfgang

en: disdrometer precipitation raclets davos fall-velocity

en: Disdrometer Data Davos Wolfgang

en:
The zip folder contains one netCDF file per day for Davos Wolfgang from February 8 to March 19 2019 with a temporal resolution of 30 seconds (time in UTC) and includes:

- characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...)
- precipitation rate
- radar reflectivity
- visibility range
- weather codes
- instrument performance

en: The dataset contains information on precipitation amount and type for Davos Wolfgang (LON: 9.853594, LAT: 46.835577) from February 8 to March 19 2019. It includes: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

davos

display_name: davos

disdrometer

display_name: disdrometer

fall-velocity

display_name: fall-velocity

precipitation

display_name: precipitation

raclets

display_name: raclets


en: phylogenetic-diversity european-alps conservation zonation plants species-distribution-models beta-diversity functional-diversity taxonomic-diversity global-change land-use-change migclim

en: Updated SDM outputs

en: Refined scenarios of plants distribution in the European Alps

en: Data Access

en:
This repository contains extensive data for the European Alps:
  - Observations of ~3,500 plant species
  - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal)
  - Flora migration rates (categorical) and ecological preferences (continuous indicator values)
  - Regional maps of barriers to migration and water bodies at 100-m resolution
  - Sampling effort, distance to roads and cities predictors at 100-m resolution
  - Present and future abundances over the study region at 1-km resolution (~2,000 species)
  - Present and future multifaceted and uniqueness of the European Alps' Flora at 1-km resolution
  - Present and future conservation recommendations at 1-km resolution (26 current and future strategies)
  - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees)
  - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them

beta-diversity

display_name: beta-diversity

conservation

display_name: conservation

european-alps

display_name: european-alps

functional-diversity

display_name: functional-diversity

global-change

display_name: global-change

land-use-change

display_name: land-use-change

migclim

display_name: migclim

phylogenetic-diversity

display_name: phylogenetic-diversity

plants

display_name: plants

species-distribution-models

display_name: species-distribution-models

taxonomic-diversity

display_name: taxonomic-diversity

zonation

display_name: zonation

en: Present and future multifaceted plant diversity, uniqueness and conservation in the European Alps


en: snow switzerland climate-change alpine3d-simulations graubunden

en: Simulation data

en: Reference data as well as the 2050 and 2099 scenarios for ARPEGE and ETHZ other RCMs available on demand)

en: Configuration file

en:
This is the simulation dataset from _"Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland"_, M. Bavay, T. Grünewald, M. Lehning, Advances in Water Resources 55, 4-16, 2013

A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graubünden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models.

The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095.

Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation.

climate-change

display_name: climate-change

graubunden

display_name: graubunden

snow

display_name: snow

switzerland

display_name: switzerland

en: Alpine3D simulations of future climate scenarios for Graubunden


maintainer: Matthias Haeni

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

en: Site photo of the forest station (NAB)

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

mimetype: image/jpeg

en: Forest station data (NAB)

en: Data file.

en: Data plot of the field station (NAF)

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

en: Site photo of the field station (NAF)

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

en: Field station data (NAF)

en: Data plot of the forest station (NAB)

en: Site aerial photo (Nationalpark)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Nationalpark in Switzerland where one station is located within a natural coniferous forest stand (NAB) with mountain pine (_Pinus mugo_; 210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Nationalpark is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


mountain-permafrost-hydrology

maintainer: Jeannette Noetzli

title_for_slug: mountain-permafrost-hydrology

en: permafrost-degradation monitoring-techniques mountain-permafrost-hydrology climate-change-impacts mass-movements ice-rich-permafrost

en: Synthesis report chapter of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN)

en: This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). In earlier reports such as the CH2014-IMPACTS report (CH-Impacts 2014), the topic of mountain permafrost hydrology was not addressed. Here, we provide a baseline of the available knowledge of mountain permafrost in the Swiss Alps for future reference. We compile an overview of the current understanding of mountain permafrost in the Swiss Alps, its distribution and characteristics, observed and projected changes, and expected impacts on slope stability, infrastructure and hydrological aspects. We also briefly describe the measurement techniques and modelling approaches applied. The chapter closes with a summary of the most important open research questions. The literature cited mainly includes studies on mountain permafrost published in scientific journals and assessments of long-term observation data. We focus on permafrost hydrology interactions wherever information is available. However, systematic studies on permafrost hydrology in mountain areas are still limited.

climate-change-impacts

display_name: climate-change-impacts

ice-rich-permafrost

display_name: ice-rich-permafrost

mass-movements

display_name: mass-movements

monitoring-techniques

display_name: monitoring-techniques

mountain-permafrost-hydrology

display_name: mountain-permafrost-hydrology

permafrost-degradation

display_name: permafrost-degradation

en: Mountain Permafrost Hydrology


en: beech dendrochronology fagus-sylvatica dendroecology network-analysis tree-ring

en:
R code to run the model behind the results of the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".
Requires beech_rawtrw.txt.

en: Model output

en: Output of the Mixed-effects model

en:
This dataset contains the raw ring-width and climate data (1952-2020) used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".
RW= ring width
Year= Calendar year
CN = core number
ID3= plot identifier
longitude = longitude (°)
latitude = latitude (°)
elevation = elevation (m)
age= estimated age in calendar year
radius= estimated radius in calendar year (mm)
MAT= mean annual temperature (°C)
MAP= mean annual precipitation (mm)
MCWB= mean climatic water balance (P-PET; mm)
amplitude= simple continentality measure (July - January mean maximum temperatures)
MATspline1 and 2, MCWBspline1 and 2, radiusspline4 and 5= natural spline transformed values of MAT, MCWB and radius ns(x,2)
CWD_pMay_Jun= Climatic Water Balance of previous year May to June !!!#please note CWD here is NOT Climatic Water Deficit, this was a labeling error in the creation of the dataset headers
CWD_May_Jun= Climatic Water Balance of current year May to June
Tmax_pMay_Jun= mean monthly maximum temperature of previous year May to June
Tmax_May_Jun= mean monthly maximum temperature of current year May to June
weight = site replication-based weighting input for model
SOM= membership of regional cluster 1-8 based on Supervised Organized Mapping

en: This dataset contains the raw ring-width and climate data (1952-2020), as well as the R code to run the model and the model output used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".

beech

display_name: beech

dendrochronology

display_name: dendrochronology

dendroecology

display_name: dendroecology

fagus-sylvatica

display_name: fagus-sylvatica

network-analysis

display_name: network-analysis

tree-ring

display_name: tree-ring

en: European Beech tree-ring dataset 1952-2020


radiosondes

maintainer: Jörg Wieder

title_for_slug: radiosondes

en: radiosonde wind pressure wind-direction raclets temperature relative-humidity holoballoon

en: Radiosond Template Files

en:
The "template" files contain standard parameters, i.e. ambient pressure (mbar and Pa), temperature (°C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), as well as additional information, e.g. time (UTC and local time), dew point (°C), mixing ration (g/kg), density (kg m³), altitude (m MSL), height above ground (ft and m AGL), and geographic coordinates (longitude, latitude). If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosond Sounding Files

en:
The "soundings" radiosond data contains information on ambient pressure (mbar), temperature (C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), and are ordered with respect to the height above ground (m AGL) with a step size of 20 m. The first line of each file idicates the geographic location and the start time (in UTC) of the ascent. If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

wind

display_name: wind

wind-direction

display_name: wind-direction

en: Radiosondes


maintainer: James W. Kirchner

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

en: MODIS NDSI script

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

mimetype: text/plain

en: Daily time series

en: Daily hydrometeorological and ecohydrological time series

en: Subcatchment polygons

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

en: Metadata

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

en: 30-minute time series

en: 30-minute hydrometeorological and ecohydrological time series.

en: MODIS EVI2 script

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

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

Data include 30-minute time series of

- weather variables,

- sap flow fluxes,

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

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

and daily time series of

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

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

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

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

diel-cycle

display_name: diel-cycle

diurnal-cycle

display_name: diurnal-cycle

ecohydrology

display_name: ecohydrology

evapotranspiration

display_name: evapotranspiration

groundwater

display_name: groundwater

sap-flow

display_name: sap-flow

snowmelt

display_name: snowmelt

streamflow

display_name: streamflow

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


maintainer: Daniel Scherrer

en: r-package gui species-migration species-interaction forest treemig spatial-modelling bioclimatic-variables forest-dynamics land-use forest-model

en: TreeMig Tutorial

en: Description input data

en: Details where the input data come from and how they have been processed

en: TreeMig Simulation Environment and Input Data

en:
The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data. For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders. For the first example simulation of the tutorial it contains the
  - the TreeMig executables for MacOS, Linux and Windows
  - simulation control files that define the setup and input files of the simulation (in folder C)
  - species parameters (in folder P)
  - bioclimatic data (in folder E)
  - folder R for results
  - folder Plots for plots of the simulation results.
  - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

en: Tutorial for using the TreeMig framework; updated

en:
Starter kit for the TreeMig framework that allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions.
The starter kit consists of a set of climate, land-use, topography and soil property input data for Switzerland, a script for installing the framework and running the GUI of the framework with the link to the input data, a tutorial covering a range of simulation examples for Switzerland and a transect through Europe, and an associated example simulation environment containing the model executables and other model inputs.

bioclimatic-variables

display_name: bioclimatic-variables

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-model

display_name: forest-model

gui

display_name: gui

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-interaction

display_name: species-interaction

species-migration

display_name: species-migration

treemig

display_name: treemig

en: Starter Kit for the TreeMig framework v1 for easy simulation of spatio-temporal forest dynamics


maintainer: Alec van Herwijnen

en: snow depth-hoar microstructure slab-avalanche strength snow-mechanics

en:
CT Reference Data: Contains sample names, positions, evaluated regions and microstructural parameters based on data from the RHOSSA and MOSAiC campaigns.
MOSAiC:
https://mosaic-portal.org/data/open-mcs/vessel/polarstern/sn_microct/sn_microct/exdata/
https://doi.org/10.1594/PANGAEA.952794
RHOSSA:
https://doi.org/10.16904/envidat.151

en: Parent sample variability: Contains the CT data used to estimate the parent sample variability.

en: Time series data: Contains the CT data of upper slab, weak layer and lower slab used to evaluate the temporal evolution within the artificial samples.

en: Experimental Data: Contains results of the compression experiments and microstructural parameters.

en:
This repository hosts the experimental data accompanying our publication, “On the compressive strength of weak snow layers of depth hoar,” featured in the Journal of Glaciology.

-Compressive Strength Data:
Measurements of the compressive strength for 92 artificially grown weak snow samples.
-Microstructural CT Data:
CT-derived microstructural information for each sample, including:
Density
Specific surface area
Connectivity density
Correlation lengths
Anisotropy
-Additional CT Data:
Parent Sample Variability: CT data used to assess the variability of the parent samples.
Temporal Evolution: CT data capturing the evolution within the artificial weak layers.
Reference Data: Information on the reference CT data sourced from the RHOSSA and MOSAiC campaigns.

depth-hoar

display_name: depth-hoar

microstructure

display_name: microstructure

slab-avalanche

display_name: slab-avalanche

snow

display_name: snow

snow-mechanics

display_name: snow-mechanics

strength

display_name: strength