greenland-shrubs-and-microclimate

maintainer: Nathalie Chardon

title_for_slug: greenland-shrubs-and-microclimate

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

en: GitLab Repository

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

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:
 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

field-data

display_name: field-data

sdm

display_name: sdm

validation

display_name: validation

en: Greenland shrubs and microclimate


maintainer: EnviDat Support

en: ecological-maps plants ecological-indicator-values soil-mapping landolt soil-ph soil-moisture switzerland

state: active

type: dataset

en: Predictors

media_type: application/zip

en: Predictors generated and used in Descombes et al. (2020)

format: ZIP

mimetype: application/zip

en: Description

en: Description of the predictors provided in this dataset

format: XLS

en:
Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.

This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection ("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"). The excel file (xlsx) provides a short description of the raster layers.

Paper Citation:

Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

switzerland

display_name: switzerland

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: rain-on-snow catchment-data meteorological-data snowpack snow-hydrology

en: The file contains all data needed to run simulations with the snow cover model SNOWPACK.

media_type: text/plain

format: TXT

mimetype: text/plain

en: Meteorological data used to run SNOWPACK for 58 catchments in the Swiss Alps. The data consists of a 2 km grid of "virtual meteorological stations" for each catchment. It was used to simulate snow cover processes during rain-on-snow events, therefore meteorological data of each catchment contains at least one rain-on-snow event. Further information can be found in the attached readme.txt and in Würzer & Jonas et al. (2017), currently under review in Hydrological Processes.

catchment-data

display_name: catchment-data

meteorological-data

display_name: meteorological-data

rain-on-snow

display_name: rain-on-snow

snow-hydrology

display_name: snow-hydrology

snowpack

display_name: snowpack

en: Meteorological data for investigation of rain-on-snow events in 58 catchments in Switzerland


en: land-use switzerland landscape-services statistical-model urbanization land-use-change modeling

en: Data for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

en:
The provided folder structure follows the set up of the scripts.
Data is only provided when it has been fully produced by us, otherwise dummies are given.
For data that will be produced by one of the scripts, no dummies are given.
The folders may contain sub-folders, which would be filled if the scripts were all run

 00_Analysis_Data
Data produced throughout the process of the calibration and projection that is later needed for analysis

 01_Raw_Data
Primary data from external sources that were used to compile the rasters and numbers used during the calibration and projection of landscape transitions as well as service calculation

 02_Prepared_Data
Data that will be prepared for further use by scripts are put in here
Additionally it contains the parameter sheets for the transition predictors that we compiled

 03_Model_Data
 Model outputs
Folder were the projection models statistics will be saved
 Models
Folder into which the actual transition models will be saved
 Residuals
Folder into which data on models residuals will be saved for future use

 04_Projected_Data
The output of the projections will be saved into this folder

 05_Service_Data
Any service calculations will be saved in here

 06_Results_Analysis
Any Analysis plots and tables as well as intermediate data will be saved here in the respective sub-folder

en: Scripts for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

en:
Scripts ordered by their main purpose within sequential folders in this ReadMe marked with

Within the folders the capital letters refer to the parallel or sequential use of the scripts; scripts here in ReadMe are marked with

 00_Function Scripts
 Functions.r
Script containing several functions for analyzing statistical models: cohen.kappa, meva.table, D2, max-Tss, plot-TSS

 Service_functions.R
Script containing functions for calculating all services and returning sums (and median for bd and rec) as well as rasters

 01_Data Preparation
 A_AS_raster_preparation.R
Preparation of the land use rasters for the years 1985, 1997, and 2009 based on the original Arealstatistik (AS/ASCH); Assigning our categories and assining new values to all "construction sites" for calibrating the models
For model projection starting point (2009) and for service calculations (all ASCH years) the "other" categories were assigned to the land use categories based on dominant neighbor, so that there were no empty cells
Creation of water & mountainous area raster that stayed stable throughout the projections
Creation of MFH raster (2009), to have as base for all new projection years, as this category never becames any other

 B_Factors_preparation.R
Preparation of the factor rasters that were used to calculate service values per cell (see Table 2): LWfactor, Transportation, Vol, Rivers, Lakes, slope, TWW, Hazard_risk

 B_Predictors_preparation.R
Preparation of the raster of the predictors used for calibrating (ASCH years) and projecting (stable) the transition models that do not depend on the land use categories: Temperature (ASCH years), solar radiation (stable), Public transportation access (stable), Elevation (stable), Workers density (ASCH years), Population change (ASCH transitions)

 B_Regions_preparation.R
Preparation of bioregions as raster to be used to mask asxxxx later to calculated services and land use area just for one region

 C_Pred_proj.R
Projecting the dynamic predictors into the future for projecting the land use transitions with the model: Linear extrapolations for workers densities and population; more detailed extrapolation of temperature (based on data for all stations that matched between the temp table we used for the calibration period and the projections from CS2M, and then making a spatial interpolation for all years based on the interpolation of temperature for all years per station)

 02_Model calibration
 A_Bioreg_pred_corr.R
Calculating correlation between the predictors for each bioregion and land use category, Output are those with too high correlation so that one predictor can be removed from transitions were both predictors occur

 A_trans_numbers_bioreg.R
Calculate transition numbers for each bioregion to decide which transitions to primarily consider

 B_Bioreg_ext_eval.R
Calculating TSS based on the external evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 85-97, evaluation data: 97-09)

 B_Bioreg_int_eval.R
Calculating TSS based on the internal evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 70%, evaluation data: 30%)

 C_Bioreg_TSS_analysis.R
Extracting TSS numbers from internal and external evaluation, calculating mean and median for each transition for each region for each evaluation method and saving as csv per region to be able to select only the transitions with high enough model performance

 D_Bioreg_Models_for_prediction.R
Calibration of the models that were then used to project land use transitions into the future; calibrated separately for each bioregion

 E_Residuals_trans_models.R
Calculation of Moran's I statistics to analyze the residuals of the land use transition models

 03_Projections
 A_Bioreg_Projections_run.R
Master script for: Extrapolaitng into the future with the regional models and then assembling for each time step to calculate new AS predictors; Several Outputs: txt file to test for NAs in Predictors, txt file with random seed number for sampling, txt file with times for calculation steps (check progress of script while running), csv sheet with projected transition frequencies, tif-rasters for every region and time step, pdf with raster plot for every region and time step

 04_Service calculation
 A_Bioreg_as_services.R
Calculate services per bioregion for years 1985, 1997, 2009

 A_Service_calculation_bioregions.R
Extracting service numbers(sums) per bioregions for all projection runs and storing them in a list of matrixes to be available for further plotting; can be run for swiss wide projections or regional ones

 05_Analysis
 A_Bioreg_ASCH_development.R
Extracting the occurrence of each category per region for all runs (first part), then for ASCH years, and then plotting them nicely; only works for regional projections

 A_Bioreg_Predictors_importanceD2.R
Calculating D2 for each transition and each considered predictor from bivariate models to get value of importance

 B_Bioreg_Pred_imp_analysis.R
Using calculated D2 to sort Predictors according to their importance for each transition

 A_Bioreg_Spatial_results_analysis.R
Plotting randomly sampled runs for ASCH and services to analyses their spatial development

 A_Bioreg_Transition_numbers_check_v1.R
Analyzing the frequency for all transitions for all regions and producing the point plots for each transition separately

 A_Bioreg_Transition_numbers_check_v2.R
Analyzing percentage and numbers of all transitions but plotting them per region and originating ASCH category

 B_Transition_Analysis.R
Further Analysis of transition numbers

 A_Service_analysis.R
Plotting of the service development for bioregions in several ways for analyzing

en:
Data and scripts of publication:

Madleina Gerecke, Oskar Hagen, Janine Bolliger, Anna M. Hersperger, Felix Kienast, Bronwyn Price, Loïc Pellissier (2019) Assessing potential landscape service trade-offs driven by urbanization in Switzerland. Palgrave communications.

Contains land use projections for Switzerland and scripts and data for these projections as well as the calculation of landscape services.

Data Folder:
Contains sub-folder with the data necessary for this study (provided were no copyright issues, otherwise placeholders with descriptions), and folders where produced data may be stored

Scripts Folder:
Contains scripts organized into subfolders depending on their purpose

Note: Some abbreviations within the scripts and data are derived from German words and not English.

land-use

display_name: land-use

land-use-change

display_name: land-use-change

landscape-services

display_name: landscape-services

modeling

display_name: modeling

statistical-model

display_name: statistical-model

urbanization

display_name: urbanization

en: Land use projections and services for Switzerland


restoring-grassland-multifunctionality

title_for_slug: restoring-grassland-multifunctionality

en: ecosystem-multifunctionality nematodes grassland biodiversity mineralization soil-biodiversity bacteria fungi restoration plants

en: Please note that EMF, MF.below, MF.abio and Soil.hetero have been corrected.

en: Please note that C.sto and Corg have been corrected.

en: Functional diversity above_belowground fauna

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

Resch, M. C., Schütz, M., Buchmann, N., Frey, B., Graf, U., van der Putten, W. H., Zimmermann, S., Risch, A. C. 2021. Evaluating long-term success in grassland restoration – an ecosystem multifunctionality approach. Ecological Applications 31, e02271.

 Study area

The study was conducted in the Canton of Zurich, Switzerland, in and around two nature reserves Eigental and Altläufe der Glatt (47°27’ to 47°29’ N, 8°37’ to 8°32’ E, 417 to 572 m a.s.l.). All studied grasslands were located with a radius of approximately 4 km. Average monthly temperatures range from 0.7 ± 2.0 °C (January) to 19.0 ± 1.5 °C (July), and monthly precipitation range from 60 ± 42 mm (January) to 118 ± 46 mm (July [maxima]; 1989-2017; MeteoSchweiz 2018). In our study, we focused on semi-dry and semi-wet oligo- to mesotrophic grasslands characterized by high plant species richness and groundwater fluctuations throughout the year (Delarze et al. 2015, see also Resch et al. 2019).

 Experimental design and sampling

A large-scale restoration experiment to expand and reconnect isolated remnants of species-rich grasslands was initiated in the nature reserve Eigental in 1990. Twenty hectares of adjacent intensive grasslands were chosen for restoration. In 1995, three restoration methods of increasing intervention intensities were implemented. The goal of all three methods was to lower the availability of soil nutrients and hence, facilitate ecosystem development towards the targeted nutrient-poor grasslands. These methods were: Harvest only (hay harvest twice a year), Topsoil (removal of the nutrient-rich topsoil), and Topsoil+Propagules (topsoil removal combined with the application of hay from target vegetation). Plant biomass harvest (once a year in late summer/early autumn) commenced in Topsoil and Topsoil+Propagules five years after the soils were removed and is still ongoing today. We measured restoration success by comparing the three restoration methods with intensively managed (Initial) and semi-natural grasslands (Target) 22 years after restoration. Initial grassland sites share the same agricultural history as the restored sites: mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes (Resch et al. 2019). Target sites were the sites from which hay for seeding the Topsoil+Propagules sites was collected. Soil conditions (i.e., soil types, soil texture) were comparable to those found in the restored grasslands (Resch et al. 2019). Additionally, Target sites were selected to represent a variety of semi-natural grasslands, including semi-dry to semi-wet conditions. In Target grasslands, biomass is harvested once a year in late summer or early autumn. Eleven 5 m x 5 m (25 m2) plots were randomly established in each of the five treatments (in total 55 plots; for a detailed map see Neff et al. 2020). An additional 2 m x 2 m (4 m2) subplot was randomly established at least 2 m away from each 25 m2 plot for destructive sampling. Data sampling took place between June and September 2017.

Vegetation properties

All plant species were identified within the 25 m2 plots (nomenclature: Lauber and Wagner 1996) in mid-June 2017 (in total 250 species). Vegetation structure and plant biomass were assessed diagonally on a transect of 2 m x 10 cm within the 25 m2 plot in early July 2017. We measured the maximum and mean height of the vegetation at the start, middle and end of the transect and calculated the standard deviation of these measures to describe vegetation structural heterogeneity (Schuldt et al. 2019). Thereafter, biomass was clipped on the entire transect to 1 cm height, sorted into five functional groups (graminoids, forbs, legumes, litter, and woody species), dried at 60 °C for 48 h, and weighed (Meyer et al. 2015).

 Aboveground arthropods

Aboveground arthropods were sampled at two locations in each 25 m2 plot in early July 2017 (see also Neff et al. 2020). Briefly, two cylindrical baskets (50 cm diameter, 67 cm height; woven fabric) were thrown simultaneously from outside the plot into two opposite corners. A closable mosquito mesh sleeve was mounted to the top of the baskets and an integrated metal ring at the bottom was fixed to the ground with metal stakes to assure that insects could not escape. A suction sampler (Vortis, Burkhard Manufacturing Co. Ltd., Hertfordshire, England) was then inserted into one of the baskets through the opening of the sleeve and the plot was “vacuumed" twice for 105 seconds with a 30 seconds break. The collected animals were immediately transferred into 70% ethanol. Arthropods were sorted and assigned to 23 taxonomic groups. Holometabolic larvae were lumped into one category while hemimetabolic larvae were grouped separately from adults in the respective taxonomic rank. We used mean values of individuals per plot for total abundance. Aboveground arthropod richness was defined by the number of different taxa to lowest taxonomic level (in total 23 taxa). All taxa were assigned to one of five trophic levels: 1) primary producers, 2) primary consumers, 3) secondary consumers, 4) tertiary consumers, and 5) quaternary consumers.

 Belowground fauna

Sampling of all belowground fauna took place in mid-July 2017. Earthworms were sampled in two 30 cm x 30 cm x 20 cm soil monoliths at two opposite corners of the 25 m2 plot (opposite to aboveground arthropod sampling). The excavated soil monolith was broken by hand, all earthworms collected and immediately transferred in a 4% formaldehyde solution. Thereafter, earthworm individuals were identified to species level (in total 10 taxa; Christian and Zicsi 1999) and species assigned to three functional groups (Bouché 1977).
To assess soil arthropod communities, we randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) in each 4 m2 subplot with a slide hammer corer lined with a plastic sleeve (AMS Samplers, American Falls, Idaho, USA). Soil arthropods were extracted using Berlese-Tullgren funnels (3 mm mesh), starting the day of sampling and lasting 14 days. Individuals were stored in 70% ethanol. Soil arthropods were assigned to 41 taxonomic groups and 4 feeding types. Holometabolic and hemimetabolic larvae were treated as previously described for aboveground arthropods. Belowground arthropod richness refers to the 41 taxonomic groups.

For soil nematode sampling, we randomly collected eight soil cores of 2.2 cm diameter (Giddings Machine Company, Windsor, CO, USA) within each 4 m2 subplot to a depth of 12 cm. The eight cores were combined, gently homogenized, placed in coolers, kept at 4 °C and transported to the laboratory at NIOO in Wageningen (NL) within one week after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink 1960) and prepared for morphological identification and quantification as described by Resch et al. (2019). Nematodes were identified to family level (39 taxa) according to Bongers (1988), assigned to 17 functional groups, 5 feeding types and 5 colonizer-persister (C-P) classes (Yeates et al. 1993, Bongers 1990, Resch et al. 2019).

We randomly collected two more soil cores (2.2 cm diameter x 12 cm depth) within each 4 m2 subplot to determine soil microbial communities. Again, the soil cores were combined, homogenized, placed in coolers and transported to the laboratory at WSL in Birmensdorf (Switzerland) where the metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNeasy PowerMax Soil Kit (Quiagen, Hilden, NRW, GER) according to the manufacturer`s instructions. PCR amplification of the V3-V4 region of the prokaryotic small-subunit (16S) and the ribosomal internal transcribed spacer region (ITS2) of eukaryotes was performed with 1 ng of template DNA utilizing PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates and pooled. The pooled amplicons were sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Quality filtering, clustering into operational taxonomic units (OTUs) and taxonomic assignment were performed as described by Frey et al. (2016) and Adamczyk et al. (2019). We used a customised pipeline largely based on UPARSE (Edgar 2013) implemented in USEARCH v. 9.2 (Edgar 2010). After discarding singletons of dereplicated sequences, clustering into OTUs with 97% sequence similarity was performed (Edgar 2013). Quality-filtered reads were mapped on the filtered set of centroid sequences. Taxonomic classification of prokaryotic and fungal sequences was conducted querying against most recent versions of SILVA (v.132, Quast et al. 2013) and UNITE (v.8, Nilsson et al. 2018). Only taxonomic assignments with confidence rankings equal or higher than 0.8 were accepted (assignments below 0.8 set to unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as eukaryotic OTUs assigned other than fungi were removed prior to data analysis. In addition, prokaryotic and fungal datasets were filtered to discard singletons and doubletons. Thereafter, OTU abundance matrices were rarefied to the lowest number of sequences per community, to normalize the total number of reads and achieve parity between samples (Prokaryota: 29,843 reads; Fungi: 26,690 reads). Finally, prokaryotic and fungal observed richness (number of OTUs) were estimated (Prokaryota: 14,010 OTUs; Fungi: 5,813 OTUs). For prokaryotes, we distinguished five and for fungi six functional types based on lowest taxonomic resolution (Nguyen et al. 2016, Tedersoo et al. 2014). Belowground taxon richness was defined by the total number of earthworm, arthropod, nematode, fungi, and prokaryote taxa assigned to lowest taxonomic level. Finally, all belowground taxa were assigned to the same five trophic levels as the aboveground arthropods.

 Soil chemical and physical properties, soil nitrogen mineralization

We randomly collected three 5 cm diameter x 12 cm depth soil samples in each 4 m2 subplot with a slide hammer corer (AMS Samplers, American Falls, Idaho, USA), pooled them and then made two subsamples. One was field-fresh and stored at 3 °C until analysis, the other was dried for 48 h at 60 °C and passed through a 4 mm mesh. From the dried sample, we measured soil pH potentiometrically in 0.01 M CaCl2 (soil:solution ratio=1:2; 30 minutes equilibration time). Total and organic carbon content were measured on fine-ground samples (≤ 0.5 mm) by dry combustion using a CN analyzer NC 2500 (CE Instruments, Wigan, United Kingdom). Inorganic carbon of samples with a pH > 6.5 was removed with acid vapor prior to analysis of organic carbon (Walthert et al. 2010). We calculated total soil carbon (C) storage after correcting its content for soil depth, stone content and density of fine earth (see below). Exchangeable cations were determined on another 5 g dry soil sample with 50 mL unbuffered 1 M NH4Cl solution (soil:solution ratio=1:10, end-over-end shaker for 1.5 hours) and measured by an ICP-OES (Optima 7300 DV, Perkin-Elmer, Waltham, Massachusetts, USA). Thereafter, cation exchange capacity (CEC) was calculated as the sum of exchangeable cations and protons (and expressed as mmolc per 1 kg soil) and used to describe nutrient retention capacity in our plots. Concentrations of exchangeable protons were calculated as the difference between total and Al-induced exchangeable acidity as determined by the KCl-method (Thomas 1982).

Ammonium (NH4+) and nitrate (NO3−) were extracted from a 20 g fresh subsample with 80 mL 1M KCl for 1.5 hours on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnemühle FineArt GmbH, Dassel, Germany). NH4+ concentrations were determined colorimetrically by automated flow injection analysis (FIAS 300, Perkin-Elmer, Waltham, Massachusetts, USA). NO3− concentrations were measured colorimetrically according to Norman and Stucki (1981). Potential soil net nitrogen (N) mineralization was assessed during an 8-week incubation period under controlled moisture (60% of field capacity), temperature (20 °C) and light conditions (dark) in the laboratory. We weighed duplicate samples of fresh soil equivalent to 8 g dry soil (24 h at 104 °C) into 50 mL Falcon tubes. Soil samples were extracted for NH4+ and NO3− at the beginning and after eight weeks as described above. Soil net N mineralization was calculated as the difference between the inorganic nitrogen (NH4+ and NO3−) before and after the incubation (Hart et al. 1994), corrected for the total incubation time and represented per day values expressed as mg N kg-1 soil d-1.

To assess soil physical properties, we randomly collected one undisturbed soil core per 4 m2 subplot (5 cm diameter, 12 cm depth) in a steel cylinder that fit into the slide hammer (AMS Samplers, American Falls, Idaho, USA). The cylinder was capped in the field to avoid disturbance. We then measured field capacity in the laboratory. For this purpose, the cylinder and soil therein were saturated in a water bath and drained on a sand/silt-bed with a suction corresponding to 60 cm hydrostatic head. The moist soil was dried at 105 °C to constant weight. Field capacity was calculated by dividing the mass of water by the total mass of wet soil contained at 60 cm hydrostatic head and used to describe water holding capacity. Thereafter, samples were passed through a 4 mm mesh. Fine-earth and skeleton fractions were weighed separately to assess bulk soil density (fine-earth plus skeleton), density of fine earth, and proportion of skeleton. Particle density was determined with the pycnometer method (Blake and Hartge 1986), and total porosity and proportion of fine pores were calculated (Danielson and Sutherland 1986). Clay, silt, and sand contents were quantified with the sediment method (Gee and Bauder 1986).

Surface and soil temperature (12 cm depth, water-resistant digital pocket thermometer; IP65, H-B Instrument, Trappe, Pennsylvania, USA) as well as volumetric soil moisture content (12 cm depth, time domain reflectometry; Field-Scout TDR 300, Spectrum Technologies, Aurora, Illinois, USA) were measured at five random locations within the 4 m2 subplots every month from June to September. We calculated the standard deviation of each temperature and moisture measure over four months to describe seasonal variations. Slope inclination was determined at plot-level via GPS measurements (GPS 1200, Leica Geosystem, Heerbrugg, Switzerland) and categorized into slope gradient classes according to FAO standards (1990). Thickness of the topsoil horizon (equivalent to Ah or Aa horizon) was determined at one soil monolith (30 x 30 x 30 cm3) per 4 m2 subplot in cm and rounded to next integer.

 References

Adamczyk, M., F. Hagedorn, S. Wipf, J. Donhauser, P. Vittoz, C. Rixen, A. Frossard, J. Theurillat, and B. Frey. 2019. The soil microbiome of GLORIA mountain summits in the Swiss Alps. Frontiers in Microbiology 10:1080-1101.

Blake, G.R., and K. H. Hartge. 1986. Particle Density. Pages 377-382 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Bongers, T. 1988. De nematoden van Nederland. Stichting Uitgeverij van de Koniklijke Nederlandse Natuurhistorische Verenigung (KNNV), Utrecht.

Bongers, T. 1990. The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia 83:14-19.

Bouché, M. B. 1977. Strategies lombriciennes. Ecological Bulletins 25:122-132.

Christian, E., and A. Zicsi. 1999. Ein synoptischer Bestimmungsschlüssel der Regenwürmer Österreichs (Oligochaeta: Lumbricidae). Die Bodenkultur 50:121-131.

Danielson, R. E., and P. L. Sutherland. 1986. Porosity. Pages 443-461 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie ‐ Gefährdung ‐ Kennarten, Ott Verlag, Bern.

Edgar, R. C. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461.

Edgar, R. C. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods 10:996–998.

FAO. 1990. Guidelines for soil description, third ed. Land and Water Development Division at the Food and Agriculture Organization of the United Nations (FAO), Rome.

Frey, B., T. Rime, M. Phillips, B. Stierli, I. Hajdas, F. Widmer, and M. Hartmann. 2016. Microbial diversity in European alpine permafrost and active layers. FEMS Microbiology Ecology 92:fiw018.

Gee, G.W., and J. W. Bauder. 1986. Particle-size analysis. Pages 383-411 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Hart, S. C, J. M. Stark, E. A. Davidson, and M. K. Firestone. 1994. Nitrogen mineralization, immobilization, and nitrification. Pages 985-1016 in R. W. Weaver, S. Angle, P. Bottomley, D. Bezdicek, S. Smith, A. Tabatabai, and A. Wollum, editors. Methods of soil analysis: Part 2—Microbiological and biochemical properties. Soil Science Society of America (SSSA) Inc., Madison.

Lauber, K., Wagner, G., 1996. Flora Helvetica. Flora der Schweiz. Haupt Verlag, Bern.

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

Meyer, S. T., C. Koch, and W. W. Weisser. 2015. Towards a standardized rapid ecosystem function assessment (REFA). Trends in Ecology and Evolution 30:390-397.

Neff, F., M. C. Resch, A. Marty, J. Rolley, M. Schütz, A. C. Risch, and M. M. Gossner. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands – a functional approach. Ecological Applications 0:e02133

Nguyen, N.H., Z. W. Song, S. T. Bates, S. Branco, L. Tedersoo, J. Menke, J. S. Schilling, and P. G. Kennedy. 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20:241–248.

Nilsson, R. H., K.-H. Larsson, A. F. S. Taylor, J. Bengtsson-Palme, T. S. Jeppesen, D. Schigel, P. G. Kennedy, K. Picard, F. O. Glöckner, L. Tedersoo, I. Saar, U. Kõljalg, and K. Abarenkov. 2018. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research 47:259–264.

Norman, R. J., and J. W. Stucki. 1981. The Determination of Nitrate and Nitrite in Soil Extracts by Ultraviolet Spectrophotometry. Soil Science Society of America Journal 45:347-353.

Oostenbrink, M. 1960. Estimating nematode populations by some selected methods. Pages 81-101 in J. J. Sasser, and W. R. Jenkins, editors. Nematology. Univ. of North Carolina Press, Chapel Hill.

Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O. Glöckner. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41:590-596.

Resch, M. C., M. Schütz, U. Graf, R. Wagenaar, W.H. van der Putten, and A. C. Risch. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56:1782-1793.

Schuldt, A., A. Ebeling, M. Kunz, M. Staab, C. Guimarães-Steinicke, D. Bachmann, N. Buchmann, W. Durka, A. Fichtner, F. Fornoff, W. Härdtle, L. R. Hertzog, A-N. Klein, C. Roscher, J. Schaller, von G. Oheimb, A. Weigelt, W. Weisser, C.Wirth, J. Zhang, H. Bruelheide, and N. Eisenhauer. 2019. Multiple plant diversity components drive consumer communities across ecosystems. Nature Communications 10:1460.

Tedersoo, L., M. Bahram, S. Põlme, U. Kõljalg, N. S. Yorou, R. Wijesundera, L. Villarreal Ruiz, A. M. Vasco-Palacios, P. Q. Thu, A. Suija, M. E. Smith, C. Sharp, E. Saluveer, A. Saitta, M. Rosas, T. Riit, D. Ratkowsky, K. Pritsch, K. Põldmaa, M. Piepenbring, C. Phosri, M. Peterson, K. Parts, K. Pärtel, E. Otsing, E. Nouhra, A. L. Njouonkou, R. H. Nilsson, L. N. Morgado, J. Mayor, T. W. May, L. Majuakim, D. J. Lodge, S. See Lee, K.-H. Larsson, P. Kohout, K. Hosaka, I. Hiiesalu, T. W. Henkel, H. Harend, L.-D. Guo, A. Greslebin, G. Grelet, J. Geml, G. Gates, W. Dunstan, C. Dunk, R. Frenkhan, L. Dearnaley, A. De Kesel, T. Dang, X. Chen, F. Buegger, F. Q. Brearley, G. Bonito, S. Anslan, S. Abell, and K. Abarenkov. 2014. Global diversity and geography of soil fungi. Science 346:1256688.

Thomas, G.W. 1982. Exchangeable cations. Pages 159-165 in A. L. Page, R. H. Miller, and D. R. Keeney, editors. Methods of Soil Analysis: Part 2—Chemical and microbiological properties. Soil Science Society of America (SSSA) Inc., Madison.

Walthert, L., U. Graf, A. Kammer, J. Luster, D. Pezzotta, S. Zimmermann, and F. Hagedorn. 2010. Determination of organic and inorganic carbon, δ13C, and nitrogen in soils containing carbonates after acid fumigation with HCl. Journal of Plant Nutrition and Soil Sciences 173:207-216.

Yeates, G. W., T. Bongers, R. G. M. de Goede, D. W. Freckman, and S. S. Georgieva. 1993. Feeding habits in soil nematode families and genera – an outline for soil ecologists. Journal of Nematology 25:315-331.

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

ecosystem-multifunctionality

display_name: ecosystem-multifunctionality

fungi

display_name: fungi

grassland

display_name: grassland

mineralization

display_name: mineralization

nematodes

display_name: nematodes

restoration

display_name: restoration

soil-biodiversity

display_name: soil-biodiversity

en: Restoring grassland multifunctionality


en: avalanche natural-hazard satellite snow hazard-mapping

en: SPOT6 Avalanche outlines 24 January 2018

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset.

format: SHP

en:
Outlines of 18'737 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 24 January 2018. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping.pdf)

The generation of the data is described in:

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

Abstract.
Accurate and timely information on avalanche occurrence are key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability considering accuracy, completeness and reliability of the reported avalanche events is limited. In this study we present the spatial continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12’500 km2).

We tested different satellite sensors available for rapid mapping during a first avalanche period. Based on these experiences, we tasked SPOT6/7 data for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18’737 individual avalanche events, applying image enhancement techniques to analyze regions in cast shadow as well as brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having a unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period.

This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data is of great value to validate avalanche bulletins, complete existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.

Koordinate System: CH1903+ LV95 LN02

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow

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.


maintainer: Jana von Freyberg

en: stable-water-isotopes plant-water-uptake root-zone endmember-mixing-models uncertainties review ecohydrology

en: Data set of MEE-20-04-264 (CSV)

media_type: text/csv

format: CSV

en: Data set of MEE-20-04-264

media_type: application/pdf

en: The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal ‘Methods in Ecology and Evolution’.

format: PDF

mimetype: application/pdf

en: The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal 'Methods in Ecology and Evolution'.

ecohydrology

display_name: ecohydrology

endmember-mixing-models

display_name: endmember-mixing-models

plant-water-uptake

display_name: plant-water-uptake

review

display_name: review

root-zone

display_name: root-zone

stable-water-isotopes

display_name: stable-water-isotopes

uncertainties

display_name: uncertainties

en: Data set of: Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources


en: photovoltaic mountain reflection snow energy irradiance albedo

en: Windows users use the 7-zip app to extract the zip-file.

en: PV Data

en:
 Overview
The SUNWELL Modelling Environment is a combination of data and code that models electricity production from satellite-derived irradiance data and other spatial data sets for all of Switzerland. This ensemble accompanies the publication "The bright side of PV production in snow-covered mountains", published in the Proceedings of the National Academy of Science and reproduces all results and figures of. Code and resources are in their original form (with documentation). A new version with a more generalized application to PV modelling and with more flexibility in terms of input and output formats will be released in the coming months.

 Format
All code is written and has to be executed in Matlab. The input and output data sets are also in the Matlab-specific .mat format. Whenever publicly available, the original data is provided as geotif, .xlsx or other common format.
This is the case for:

- Digital Elevation Model (InputsFromMatlab/MSG/OriginalData/ASTERDEM),
- Landsurface cover type (InputsFromMatlab/MSG/OriginalData/CORINE),
- Population Density (InputsFromMatlab/MSG/OriginalData/popdensRaster,
- Electricity production from three of our validation sites (/Validation/WSL),
- Measured irradiance for two validation sites (/Validation/ASRB)
The ‘Metadata’ documents in the respective folders provide further information about the data sources and processing. Figures are produced either in .pdf or .png format.

 Structure
The central level of the SUNWELL environment holds the 5 Mains, which run the different modelling aspects of the paper; each code is documented separately. Additional code is located in the ‘DataProcessing’ and the ‘functions’ folder. Functions are called in the different Mains.

‘InputsFromMatlab’ contains the radiation and albedo input data sets in separate subfolders (SIS/SISDIR/ALB). The original data is not publicly available, but can be requested for research purposes free of charge. We provide a processed subset of the data set that was used to run the SUNWELL simulations. The MSG subfolder contains additional spatial input data sets.

‘Outputs’ contains the output files from the different mains (matching names, Main_CHallpixels.m  Prod_CHallpixels)

‘Publication_figures’ contains all individual figures from the PNAS publication, as well as the generating code (/code_plot) and the power point figures (/ppts) that provide the combined final figures.

‘Validation’ contains the data sets used in the model validation:

- Electricity production from three of our validation sites (/WSL),
- Measured irradiance for two validation sites (/ASRB)

Electricity production from a validation site at Lac des Toules in Wallis (/LDT), this data set was provided under an NDA and cannot be made publicly available.

Paper Citation:
 > _Annelen Kahl; Jérôme Dujardin; Michael Lehning (2018). Dataset on PV Production in Snow Covered Mountains. PNAS - Proceedings of the National Academy of Sciences. (in press)_

albedo

display_name: albedo

energy

display_name: energy

irradiance

display_name: irradiance

mountain

display_name: mountain

photovoltaic

display_name: photovoltaic

reflection

display_name: reflection


en: complex-terrain wrf cosmo-wrf grayzone numerical-simulation

en:
This dataset contains simulation data for *"Influence of Air Flow Features on Alpine Wind Energy Potential" (Kristianti et al., 2024).*
•	The WRF_dia2702 and WRF_dia1103 folders contain the namelist.wps, namelist.input, and output files from the simulation in the Diablerets region on 27/02/2021 and 11/03/2021, respectively.
•	The WRF_luk2310 and WRF_luk0912 folders contain the namelist.wps, namelist.input, and output files from the simulation in the Lukmanier region on 23/10/2020 and 09/12/2020, respectively.
•	The postprocessing file contains Python codes used to create figures in Kristianti et al. (2024).
**Notes:**
•	Topography input is provided by Gerber et al. (2021).
•	Simulations are run using the COSMO-WRF model developed by Gerber et al. (2018).
•	Visualization by Python code uses wrf-python provided by Ladwig et al. (2017).
*This dataset was produced by the EDGE consortium sponsored by the Swiss Federal Office of Energy's SWEET programme, Swiss National Science Foundation (SNSF): Grant 179130, and the Swiss National Supercomputing Centre (CSCS) projects s938, s1115 and s1242.*

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

grayzone

display_name: grayzone

numerical-simulation

display_name: numerical-simulation

wrf

display_name: wrf

en: COSMO-WRF Dataset for Swiss Alps Simulations in Gray-Zone Resolution


maintainer: Anita C Risch

en: microbial-respiration anthropogenic-management nutrient-addition microbial-biomass-carbon climate herbivore-exclusion soil-properties

en: Risch et al. GEB_data.xlsx

en:
Study sites and experimental design
We collected data from 23 sites that are part of the Nutrient Network Global Research Cooperative (NutNet, https://nutnet.umn.edu/). The mean annual air temperature (MAT) across these sites ranged from -4 to 22°C, mean annual precipitation (MAP) from 252 to 1592 mm, and elevations from 6 to 4261 m above sea level (Fig 1a, Supplementary Table S1), hence cover a wide range of climatic conditions under which grasslands occur (Fig 1b). Soil organic C concentrations ranged between 0.8 to 7.8%, soil total N concentrations between 0.1 and 0.6%, and the soil C:N ratio between 9.1 and 21.5. Soil clay content spanned from 3.0 to 35%, and soil pH from 3.4 to 7.6 (Supplementary Table S2).
At each site, the effects of nutrient addition and herbivore exclusion were tested via a randomized-block design (Borer et al., 2014). Three blocks with 10 treatment plots each were established at each site, except for the site at bldr.us (only two blocks). Each of these 10 plots was randomly assigned to a nutrient or fencing treatment. An individual plot was 5 x 5 m, divided into four 2.5 x 2.5 m subplots. Each subplot was further divided into four 1 x 1 m square sampling plots, one of which was set aside for soil sampling (Borer et al., 2014). Plots were separated by at least 1 m wide walkways. We collected soil samples from four different treatments for this study: (i) untreated control plots (Control), (ii) herbivore exclusion plots (Fence), (iii) plots fertilized with N, P, K, plus nine essential macro and micronutrients (NPK), and (iv) plots with simultaneous fertilizer addition and herbivore exclusion (NPK+Fence). The experiments were established at different times in the past, with years of treatment different among sites (2 – 9 years since start of treatment; Supplementary Table S1). For the nutrient additions, all sites applied 10 g N m-2 each year as time-release urea; 10 g P m-2 yr-1 as triple-super phosphate; 10 g K m-2 yr-1 as potassium sulfate. A micro-nutrient mix (Fe, S, Mg, Mn, Cu, Zn, B, Mo, Ca) was applied at 100 g m-2 together with K in the first year of treatments but not thereafter.
We excluded large vertebrate herbivores (Fence) by fencing two plots, one with and one without NPK additions, within each block. The fences excluded all aboveground mammalian herbivores with a body mass of over 50 g (Borer et al., 2014). At most sites, the fences were 180 cm high, and the fence contained a wire mesh (1 cm holes) for the bottom 90 cm with a 30 cm outward-facing flange stapled to the ground to exclude burrowing animals. Climbing and subterranean animals may potentially still access these plots (Borer et al., 2014). For slight modifications in fence design at a few sites see Supplementary Table S3. Most sites only had wild herbivores, although four sites were also grazed by domestic animals (Supplementary Table S1).

Collection of soil samples, soil microbial respiration, microbial biomass, and other soil properties
Each of the 23 sites received a package containing identical material from the Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland to be used for sampling (Risch et al., 2015; Risch et al., 2019). We collected two soil cores of 5 cm diameter and 12 cm depth in each sampling plot and composited them to measure MR, MBC, and soil chemical properties (see below). An additional sample (5 x 12 cm) was collected to assess soil physical properties. This sample remained within a steel sampling core after collection and both ends were tightly closed with plastic caps to avoid disturbance. All soils were shipped cooled to the laboratory at (Location will be disclosed after manuscript acceptance) within a few days after collection. Soils were sampled roughly 6 weeks prior to peak biomass at each site during 2015 and 2016.
To assess MR (CO2 production) in a laboratory incubation experiment we weighed duplicate soil samples (8 g dry soil equivalent) into 50-ml Falcon tubes. No additional substrate (glucose, sugar) was added to these samples. We adjusted the soil moisture of each sample to 60% field capacity. We then placed a 15 ml plastic test tube (Semadeni 1701A) containing 7.25 ml 0.05 M NaOH over each soil sample. The test tube was fixed with a plastic rod so that it was not in contact with the soil sample. The Falcon tubes were then sealed with a screw cap and placed in an incubator under completely dark conditions at 20°C. The CO2 produced by microbial respiration was absorbed by the 0.05 M NaOH. For five weeks we measured the decrease in conductivity within the 0.05 M NaOH solution on a weekly basis with a Multimeter WTW Multi 3410 (WTW GmbH, Germany) and replaced the 0.05 M NaOH with fresh solution. We included Falcon tubes without soil samples in each incubation run as blanks to test if tubes were tight and no CO2 could enter or escape. We calibrated the relationship between conductivity reduction and NaOH absorbed as follows: 400 ml 0.05 M NaOH was placed in a beaker and its conductivity was measured with the multimeter. While stirring, air containing CO2 was blown into the solution for approximately one minute, which reacts with NaOH to form Na2CO3. After this process, conductivity was measured again. We then transferred 7.25 ml of the solution into a smaller beaker and added 1 ml of 0.1 M BaCl2 to precipitate Na2CO3 and then titrated the solution with 0.05 M HCl to determine the remaining NaOH. We then repeated these steps with the remaining solution a total of nine times and plotted the conductivities (y-axis) against the NaOH consumed (x-axis, Supplementary Fig S1). This regression line was used to infer the consumption of NaOH from the conductivity reduction in the incubation experiments and to calculate CO2 evolution during incubation. In addition, we determined the optimum concentration for the NaOH solution in series of preliminary experiments, so that the concentration was not too high to become insensitive, but also not too low so that not all NaOH reacts during incubation. We then calculated MR (mg CO2-C kg dry soil-1 h-1) as total amount of CO2 released over the 5 weeks divided by the duration of the entire incubation in hrs.
Soil microbial biomass carbon (MBC; mg C kg soil-1 ) was measured at the beginning of the experiment by measuring the maximal respiratory response to the addition of glucose solution (4 mg glucose per g soil dry weight dissolved in distilled water; substrate-induced respiration method) on approximately 5.5 g of soil (J. P. E. Anderson & Domsch, 1978; Nico Eisenhauer et al., 2018; Scheu, 1992). For this purpose we used an O2-micro-compensation apparatus (Scheu, 1992). More specifically, substrate-induced respiration was calculated from the respiratory response to D-glucose for 10 hr at 20°C. Glucose was added according to preliminary studies to saturate the catabolic enzymes of microorganisms (4 mg g soil-1 dissolved in 400 ml deionized water). The mean of the lowest three readings within the first 10 hrs (between the initial peak caused by disturbing the soil and the peak caused by microbial growth) was taken as maximum initial respiratory response (MIRR; ml O2 kg soil-1 h-1) and microbial biomass (mg C kg soil-1) was calculated as 38 x MIRR (Beck et al., 1997; Cesarz et al., 2022; Thakur et al., 2015).
The rest of the composited sample was dried at 65°C for 48 h, ground and sieved (2 mm mesh) to assess the soil pH, mineral soil total C and N and C:N ratio, and mineral soil organic C (Risch et al., 2019). The undisturbed sample was used to assess water holding capacity (WHC), bulk density (BD), and soil texture [sand, silt, clay; methods in (Risch et al., 2019)]. We used the percentage of sand and clay as an indicator of soil texture in this study. MAT (°C), MAP (mm) and temperature of the wettest quarter (°C) were obtained from www.worldclim.com (Fick & Hijmans, 2017; Hijmans, Cameron, Parra, Jones, & Jarvis, 2005). These variables were selected as they were found to be drivers of soil nutrient processes across these sites in earlier studies (Risch et al., 2020; Risch et al., 2019). Mean annual soil temperatures (MAST; °C) for the 0 to 5 cm soil layer were obtained for each site from the SoilTemp maps (J. Lembrechts et al., 2021; J. J. Lembrechts et al., 2022), global gridded modelled products of soil bioclimatic variables for the 1979-2013 period at a 1-km² resolution, based on CHELSA, ERA5 and in-situ soil temperature measurements.
Numerical calculations and statistical analyses
We calculated MMQ as MR/MBC. We corrected this measure using the average soil temperature of each site (MMQsoil). This temperature correction is necessary as incubation temperatures are usually much higher than site mean annual soil temperatures (see Xu et al. 2017). MMQsoil = MMQ x Q10(MAST – 20)/10, where Q10 was assumed to be 2 (Xu et al. 2017). See Supplementary Fig S2 for comparison of air and soil temperatures across the 23 sites as well as the incubation temperature.
Some of the explanatory variables (clay, soil organic C, C:N ratio) were skewed and were thus log-transformed prior to analyses. All continuous explanatory variables were centred and scaled to have a mean of zero and variance of one. To avoid collinearity between them we filtered them using correlation analysis (Supplementary Fig S3). From the variables that were strongly correlated (Pearson’s |r| > 0.70) (Dormann et al., 2013), we selected the ones that allowed us to minimize the number of variables (Supplementary Fig S3). Specifically, soil total N concentration, soil total C concentration, soil sand content and soil bulk density were dropped from the dataset. We then assessed how these edaphoclimatic variables are related to MMQ across our global grasslands.
For this, we used linear mixed effects models (LMMs) fitted by maximum likelihood with the lme function in the nlme package (version 3.1-153) (Pinheiro, Bates, DebRoy, & Sarkar, 2021) in R version 3.6.3. (R Core Team, 2019). We used treatment as a fixed effect and plot nested in site as random effects to assess treatment differences in MMQsoil, as well as MR, and MBC. The number of years since the treatment started was included as a fixed effect in all the initial models but was not significant and therefore not retained in the models. To assess how differences in MMQsoil were affected by environmental factors (soil, climatic properties) we again used LMMs. Soil and climatic properties were included as fixed effects and plot nested in site as random effects. We did not include interactions between environmental variables. We then used the MuMin package (Barton, 2018) (version 1.42.1) to select the best models that explained the most variation based on Akaike’s information criterion (AIC; model.avg function). We used the corrected AIC (AICc) to account for our small sample size and selected the top models that fell within 4 AICc units (delta AICc < 4) (Burnham & Anderson, 2002; Johnson & Omland, 2004). We present all our top models rather than model averages. Conditional averages are provided in the Supplementary material.
Based on findings from analyses described above and the literature, we developed a conceptual model of direct and indirect relationships between both edaphoclimatic variables and experimental treatments (Supplement Figure S4) to obtain a more holistic approach in understanding how these properties affect MMQsoil. We had data from 23 sites with 272 observations. We tested this model using structural equation modelling based on a d-sep approach (Lefcheck, 2016; Shipley, 2009). We considered those environmental drivers that were included in our top LMMs, namely temperature of the wettest quarter (T.q.wet), soil pH, water holding capacity (WHC) and soil organic C (organic C; Supplementary Figure S4). These factors were allowed to directly affect MMQsoil, and via their interactions with treatments. In addition, treatments were allowed to directly affect MMQsoil. Treatments were included as dummy variables in the model. We tested our conceptual model (Supplementary Fig S4) using the piecewiseSEM package (version 2.0.2; Lefcheck, 2016) in R 3.4.0, in which a structured set of linear models are fitted individually. This approach allowed us to account for the nested experimental design, and overcome some of the limitations of standard structural equation models, such as small sample sizes (Lefcheck, 2016; Shipley, 2009). We used the lme function of the nlme package to model response variables, including site as a random factor. Good fit of the SEM was assumed when Fisher’s C values were non-significant (p > 0.05). For all significant interactions between soil or climate variables and treatments detected in the SEMs, we calculated treatment effect sizes, i.e., the differences in MMQsoil between Control and treatments as log response ratios (LRR) and plotted these values against the climate or soil factors. The LRR were defined as log(Control/Treatment), where treatment was either Fence, NPK or NPK+Fence. To assess which of the LRR-climate or soil property relationships were significant we again used LMMs, in which soil and climatic properties were included as fixed effects and plot nested in site as random effects.

format: SERVICE

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

Paper Citation:

Risch Anita C., Zimmermann, Stefan, Schütz, Martin, Borer, Elizabeth T., Broadbent, Arthur A.D., Caldeira, Maria C., Davies, Kendi F., Eisenhauer, Nico, Eskelinen, Anu, Fay, Philip A., Hagedorn, Frank, Knops, Johannes M.H., Lembrechts, Jonas, J., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Seabloom, Eric W., Silveira, Maria L., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Drivers of the microbial metabolic quotient across global grasslands. Global Ecology and Biogeography

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

The microbial metabolic quotient (MMQ; mg CO2-C mg MBC-1 h-1), defined as the amount of microbial CO2 respired (MR; mg CO2-C kg soil-1 h-1) per unit of microbial biomass C (MBC; mg C kg soil-1), is a key parameter for understanding the microbial regulation of the carbon (C) cycle, including soil C sequestration. Here, we experimentally tested hypotheses about the individual and interactive effects of multiple nutrient addition (NPK+micronutrients) and herbivore exclusion on MR, MBC, and MMQ across 23 sites (5 continents). Our sites encompassed a wide range of edaphoclimatic conditions, thus we assessed which edaphoclimatic variables affected MMQ the most and how they interacted with our treatments. Soils were collected in plots with established experimental treatments. MR was assessed in a five-week laboratory incubation without glucose addition, MBC via substrate-induced respiration. MMQ was calculated as MR/MBC and corrected for soil temperatures (MMQsoil). Using LMMs and SEMs, we analysed how edaphoclimatic characteristics and treatments interactively affected MMQsoil. MMQsoil was higher in locations with higher mean annual temperature, lower water holding capacity, and soil organic C concentration, but did not respond to our treatments across sites as neither MR nor MBC changed. We attributed this relative homeostasis to our treatments to the modulating influence of edaphoclimatic variables. For example, herbivore exclusion, regardless of fertilization, led to greater MMQsoil only at sites with lower soil organic C (<1.7%). Our results pinpoint the main variables related to MMQsoil across grasslands and emphasize the importance of the local edaphoclimatic conditions in controlling the response of the C cycle to anthropogenic stressors. By testing hypotheses about MMQsoil across global edaphoclimatic gradients, this work also helps to align the conflicting results of prior studies.

anthropogenic-management

display_name: anthropogenic-management

climate

display_name: climate

herbivore-exclusion

display_name: herbivore-exclusion

microbial-biomass-carbon

display_name: microbial-biomass-carbon

microbial-respiration

display_name: microbial-respiration

nutrient-addition

display_name: nutrient-addition

soil-properties

display_name: soil-properties


en: roads density buffer jupyter geospatial gis

en: Road data from OSM for the surroundings of Menzberg, Switzerland

en: Plots

en: Random plot locations

en: Road Density Calculation (Python Notebook) online

en:
This dataset contains the road and plot data used for the geospatial analysis example showcased in "Fostering Open Science at WSL with the EnviDat Environmental Data Portal", a contribution to the 5th Open Source Geospatial Research and Education Symposium (OGRS), 2018.
The example uses Jupyter Notebook to calculate road densities in the neighbourhood of sample plot locations with Python. Road data were extracted from OpenStreetMap, while the point data (sample plots) were generated manually.

buffer

display_name: buffer

density

display_name: density

geospatial

display_name: geospatial

gis

display_name: gis

jupyter

display_name: jupyter

roads

display_name: roads

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: Dataset for OGRS 2018 publication


en: urban-ecology birds diversity arthropods insects spiders snails urbanization species-richness-models species-abundance-distributions sdms

en: contains the raw data to perform species distribution models, only for common species

en: abundance and occurrence data of the 1446 species studied, used to investigate species-abundance distributions and species-occurrence distributions

en: contains the raw data to perform the species richness models of all species

en: contains the raw data to perform the species richness models of the rare species

en: contains the raw data to perform the species richness models of the hypercommon species

en:
Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.
Data are provided on request to contact person against bilateral agreement.

arthropods

display_name: arthropods

birds

display_name: birds

diversity

display_name: diversity

insects

display_name: insects

sdms

display_name: sdms

snails

display_name: snails

species-abundance-distributions

display_name: species-abundance-distributions

species-richness-models

display_name: species-richness-models

spiders

display_name: spiders

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

en: Ecological properties of urban ecosystems. Biodiversity dataset of Zurich


en: ground-vegetation fagus-sylvatica forest-management climate-change forest air-pollution

en: Taxa Data

en: This table contains information about the individual taxa found in the historic and current vegetation relevés in Scherrer et al. 2024.

en: Site Information

en: This table contains information about the 254 forest sites used in the publication of Scherrer et al. 2024.

en: This dataset comprised 254 historical (1937 – 1948) and resurveyed (2019 – 2022) vegetation relevés of beech-dominated forests sites in the Swiss Jura mountains. The data contains species lists as well as site information.

air-pollution

display_name: air-pollution

climate-change

display_name: climate-change

fagus-sylvatica

display_name: fagus-sylvatica

forest

display_name: forest

forest-management

display_name: forest-management

ground-vegetation

display_name: ground-vegetation

en: Resurveyed vegetation releves of beech forests in the Swiss Jura Mountains


en: mass-flux antarctica drifting-snow snowmicropen meteorological-data terrestrial-laser-scanning

en: README

en: Describes the dataset in detail

en: Dataset

en: Links to the dataset

en:
This dataset contains the data acquired during the expedition to Princess Elisabeth Antarctica Station in December 2016 and January 2017. The dataset consits of meterorological data, drifting snow mass flux data, SnowMicroPen data and Terrestrial Laser Scanning data.

Please refer to the README for more information about the data.

This dataset is the basis of the following publication:

Sommer, C. G., Wever, N., Fierz, C., and Lehning, M.: Wind-packing of snow in Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-36, in review, 2018.

antarctica

display_name: antarctica

drifting-snow

display_name: drifting-snow

mass-flux

display_name: mass-flux

meteorological-data

display_name: meteorological-data

snowmicropen

display_name: snowmicropen

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

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: Expedition to Princess Elisabeth Antarctica Station, 2016/2017


snow-drift-station-flowcapt

maintainer: Benjamin Walter

title_for_slug: snow-drift-station-flowcapt

en: flowcapt acoustic-mass-flux snow-blowing raclets snowdrift wind-speed

en: maximum mass flux

en: The data file contains the maximum mass flux of 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: average wind speed

en: The data file contains the average wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: avergaged mass flux

en: Average mass flux of 10 min measurement interval. The Cycle duration is 1 min which includes 6 s observation and 54 s stand-by. The time is given in UTC+1.

en: maximum wind speed

en: The data file contains the maximum wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6 s observations and 54 s stand-by. The time is given in UTC+1.

en: minimum mass flux

en: The data file contains the minimum mass flux of 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: minimum wind spees

en: The data file contains the minimum wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: The FlowCapt is an ultra-robust instrument measuring solid particle acoustic mass - flux intensities (g m‾ ² s‾ ¹) and wind speeds (m s‾ ¹). It was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849). The vertical tube with a length of 1 m monitors snowdrift and snow-blowing; and is mounted at a height between 0.1 an 1.1 m above snow surface. The time within each data set is given in UTC+1.

acoustic-mass-flux

display_name: acoustic-mass-flux

flowcapt

display_name: flowcapt

raclets

display_name: raclets

snow-blowing

display_name: snow-blowing

snowdrift

display_name: snowdrift

wind-speed

display_name: wind-speed

en: Snow Drift Station - Flowcapt


maintainer: Christoph Marty

en: hydrology climate-change-impacts water-cycle snowfall snow

en: DORA Persistent URL

en: Synthesis report chapter

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). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.
Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0° Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels.
As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.

climate-change-impacts

display_name: climate-change-impacts

hydrology

display_name: hydrology

snow

display_name: snow

snowfall

display_name: snowfall

water-cycle

display_name: water-cycle

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: Snow and the water cycle in a changing climate


greenland-climate-network-gc-net-data

title_for_slug: greenland-climate-network-gc-net-data

en: climate air-temperature automatic-weather-station net-radiation aws greenland

en: Access the latest L1 data

en:
The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial version of L1 data has been archived.

en: GC Net Chart Parameters

media_type: application/json

en: Stations parameters from static bucket

format: JSON

en: 22-Petermann ELA

en: GC Net Config for Stations

en: Stations Config from the static bucket

en: Archived GC-Net Data (deprecated)

en:
The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial versions of L0 and L1 data have been archived.

en:
In Memory of Dr. Konrad (Koni) Steffen

Update October 2022: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).
Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS. Future data versions will be available at: [https://doi.org/10.22008/FK2/VVXGUT](https://doi.org/10.22008/FK2/VVXGUT)
 Background
Starting with a single station in 1991, the Greenland Climate Network (commonly known as GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen, and spanning the Greenland Ice Sheet (GrIS). This first station was "Swiss Camp" or the "ETH-CU" camp (GC-Net station 01) which was used as a field science and education site by Koni for years. The GC-Net was expanded with multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data (see "C-file" below) were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado.

 Overview
Provided in this dataset are the 16 longest running stations in the network, which are spread over a significant area of the GrIS and the majority of the unique climatic zones. From the South Dome high point in the South, to the Western Jakobshavn ablation region in the west, to the Petermann glacier in the North across east of the Northeast Greenland Ice Stream to the east, GC-Net is the longest running climatological record of Greenland.

 The standard GC-Net station consists of:
* Air temperature measurements at 2 heights above the surface
* Temperature and humidity measurements at 2 heights above the surface
* Wind speed and direction measured at 2 heights above the surface
* Sonic distance sounder measurements for 2 snow height and distance of instruments to surface
* Incoming shortwave radiation measurement
* Reflected shortwave radiation measurement
* Net broadband radiation (long- and short-wave) measurement
* Air pressure measurement
Data have often been repatriated in near-real time using one of either the GOES geostationary satellite or the ARGOS polar orbiting satellite transmission system. The stations were visited typically every 1-2 years for maintenance and service, and to download full uncorrupted data directly from the dataloggers. GC-Net stations were visited by Twin Otter equipped with snow skids to land directly on the open-ice at the AWS locations, or by helicopter near the west coast. The AWSs operate on solar and battery power and occasionally lost power during the dark and cold winter months, particularly when the batteries were aging.

 Dataset
This dataset consists of 2 main data levels; Level 0 and Level 1.
Level 0 is the raw data from the dataloggers, historical processing codes, satellite transmissions, and Koni’s personal data archive. Level 0 data (.zip) directories contain subdirectories:
* “C file” - contains the historical processed datafile for each station.
* “Campbell logger files” - contains the raw csv datafiles from the stations’ Campbell Scientific dataloggers since the CR1000 era (~2007-2008 for most stations).
* “Photos” - contains photographs of the station when available marked by year.
Level 1 is the appended, calibrated, cleaned, and quality flagged data. The full processing scheme is open-source and publicly available on the following GitHub repository (please also check GitHub for the latest L1 data):
[GC-Net L1 data on GitHub](https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing "GC-Net-level-1-data-processing")
Level 1 data is provided in the newly described csv-compatible [NEAD format](https://www.envidat.ch/#/metadata/nead "NEAD format").

 Additional Details
Dataset description publication will be forthcoming.
The Geological Survey of Denmark and Greenland (GEUS) has been imperative in the reprocessing and continuity mission of GC-Net. Multiple GC-Net stations have been replaced with updated and upgraded AWS hardware at the same coordinates by GEUS. This effort will ensure continuity of the GC-Net dataset into the future.

air-temperature

display_name: air-temperature

automatic-weather-station

display_name: automatic-weather-station

aws

display_name: aws

climate

display_name: climate

greenland

display_name: greenland

net-radiation

display_name: net-radiation

en: Greenland Climate Network (GC-Net) Data


photogrammetric-drone-data-dorfberg

maintainer: Yves Bühler

title_for_slug: photogrammetric-drone-data-dorfberg

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

en: Data Access

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

All products are also available as a cloud optimized geotiff.

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

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

snow-distribution

display_name: snow-distribution

uav

display_name: uav

en: Photogrammetric Drone Data Dorfberg


data-interactive-avalanche-segmentation

title_for_slug: data-interactive-avalanche-segmentation

en: deep-learning interactive avalanche segmentation snow

en:
Images and corresponding avalanche annotations used for interactive avalanche segmentation:
SLF train, vali and test
WebNew
GroundPic
UserPic
UIBK dataset: List of images used
Pictures are from SLF (mostly from the Dischma Cams) if not otherwise indicated. If used, the data needs to be properly cited!

avalanche

display_name: avalanche

deep-learning

display_name: deep-learning

interactive

display_name: interactive

segmentation

display_name: segmentation

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: Data interactive avalanche segmentation


maintainer: Henning Löwe

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

en: Data Structure (Python Notebook - view online)

en: Data Structure (Python Notebook)

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

discrete-element-modelling

display_name: discrete-element-modelling

microstructure

display_name: microstructure

snow-mechanics

display_name: snow-mechanics

spherical-model-snow

display_name: spherical-model-snow

unconfined-compression-experiments

display_name: unconfined-compression-experiments

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


maintainer: Armin Sigmund

en: backward-trajectories snow-sublimation antarctica evaporation stable-water-isotopes lagrangian-analysis isotopic-distillation

en: Model code

en: Model Sublimation and Model Air Parcel (python scripts)

en: Data

en: Modeled data including the data shown in the figures of the related research article

en: Readme

en: Meta data

en: Python scripts for data visualization

en: Scripts for generating the figures of the related research article and generating the data files (csv) from the model output

en:
 Summary

This data set contains Python programming code and modeled data discussed in a related research article. We developed a simple isotope model to study the drivers of the particularly depleted vapour isotopic composition measured on the ship of the Antarctic Circumnavigation Expedition close to the outlet of the Mertz glacier, East Antarctica, in the 6-day period from 27 January 2017 to 1 February 2017. The model considers the stable water isotopologues H2(16O), H2(18O), and HD(16O). It uses data from the ERA5 reanalysis product with a spatial resolution of 0.25° x 0.25° (Hersbach et al., 2018) and 10-day backward trajectories for the location of the ship, published by Thurnherr et al. (2020a). Our data set includes the model code, Python scripts for visualizing the results, and data produced by the model including the results shown in the figures of the related research article. Here, we summarize the most important model characteristics while further details can be found in the readme.txt file and the related research article including its supporting information.

 Main model characteristics

The modeling approach consists of two steps called *Model Sublimation* and *Model Air Parcel*. The former estimates the isotopic compositions of the snow and sublimation flux across the Antarctic Ice Sheet using an Eulerian frame of reference while the latter models the vapour isotopic composition and specific humidity along air parcel trajectories using a Lagrangian frame of reference. The isotope effects of most phase changes are represented by equilibrium fractionation. Only for ocean evaporation, kinetic fractionation is additionally taken into account (original Craig-Gordon formula). For snow sublimation, two assumptions are tested: *Run E* assumes that sublimation is associated with equilibrium fractionation while *Run N* assumes that sublimation occurs without isotopic fractionation.

 Model Sublimation
Model Sublimation uses a simple one-dimensional mass-balance approach in each grid cell, considering snow accumulation due to snowfall and vapour deposition and snow ablation due to sublimation. The snowpack is represented by 100 layers of equal thickness (e.g., 1 cm) and density (350 kg m-3). The isotopic composition of snowfall is parameterized by generalizing a site-specific, empirical relationship between the daily mean air temperature and snowfall isotopic composition. In the case of vapour deposition, Model Sublimation assumes equilibrium fractionation and estimates the isotopic composition of the atmospheric vapour as the average value for two idealized situations: (i) locally sourced vapour which has the same isotopic composition as the sublimation flux; (ii) non-locally sourced vapour in isotopic equilibrium with snowfall. Model Sublimation is run with a time step of 1 h, independently of Model Air Parcel.

 Model Air Parcel
Every hour, an ensemble of trajectories arrives at different heights in the ABL above the ship. For each of these trajectories, we consider an air parcel with a constant volume of 1 x 1 x 1 m3. The air parcels are initialized at the first suitable time when the trajectories are located in the ABL, either over the ice-free ocean in conditions of evaporation or over snow (Antarctic Ice Sheet or sea ice). Subsequently, the masses of the water isotopologues in the air parcels are simulated with a time step of 3 h, considering vapour uptake or removal due to the moisture flux at the snow or liquid ocean surface (only if the parcel is in the ABL) and cloud/precipitation formation (if the saturation specific humidity is reached). Sea ice is taken into account in a very simplified way. We represent the sea ice by grid cells with a sea-ice cover of more than 90% and assume the isotopic composition of the sublimation flux to be identical to that in the nearest grid cell of the Antarctic Ice Sheet. The isotopic composition of the sublimation flux is taken from Model Sublimation whereas the isotopic composition of the vapour deposition flux (over snow) and condensation flux (over ice-free ocean) is simulated assuming an isotopic equilibrium with the air parcel. Isotope effects of cloud/precipitation formation are represented using the classic Rayleigh distillation model with equilibrium fractionation, where the cloud water is assumed to precipitate immediately after formation.

 References

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J.,... others (2018). *ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. doi: 10.24381/cds.bd0915c6

Thurnherr, I., Wernli, H., & Aemisegger, F. (2020a). *10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017*. Zenodo. doi: 10.5281/zenodo.4031705

antarctica

display_name: antarctica

backward-trajectories

display_name: backward-trajectories

evaporation

display_name: evaporation

isotopic-distillation

display_name: isotopic-distillation

lagrangian-analysis

display_name: lagrangian-analysis

snow-sublimation

display_name: snow-sublimation

stable-water-isotopes

display_name: stable-water-isotopes

en: Modeled Isotopic Composition of Water Vapour Along Air Parcel Trajectories in the Antarctic