maintainer: Manuel R. Popp

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

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

en:
This data set contains roughly 5600 photographs of plants and plant parts from 153 sites recorded in all major biogeographic regions of Switzerland using smartphone cameras. Image names contain a letter referring to depicted plant parts: b = bark, i = inflorescence, f = infructescense, s = several parts, v = vegetative parts.
Photos are grouped in folders by observation (individual or population), i.e., photos in the same folder are of the same species, observed at the same time and sampling plot. At a higher organisational level, these folders are grouped by sampling plot (usually 3×3 m in open areas and 10×10 m in forests). The main folder contains metadata including taxon name, sampling time and location, as well as the TypoCH habitat type encoded as integers.
This data set accompanies the following publication:
Popp, M. R., Zimmermann, N. E. & Brun, P. Evaluating the use of automated plant identification tools in biodiversity monitoring—a case study in Switzerland. _Ecological Informatics_ **103316** (2025) doi:[10.1016/j.ecoinf.2025.103316](https://doi.org/10.1016/j.ecoinf.2025.103316).

photo

display_name: photo

plant-species

display_name: plant-species

species-observations

display_name: species-observations

testing

display_name: testing

vascular-plants

display_name: vascular-plants

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


maintainer: Daniela Brito Melo

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

state: active

type: dataset

en: Data in Figures

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

format: SERVICE

en: LES-LSM code

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

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

drifting-snow

display_name: drifting-snow

large-eddy-simulations

display_name: large-eddy-simulations

mixed-grain-transport

display_name: mixed-grain-transport

saltation

display_name: saltation

snow-cohesion

display_name: snow-cohesion

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

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.

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester


maintainer: Yael Frischholz

en: alps bifaciality photovoltaic snow winter-electricity

media_type: application/zip

en:
Title: Monitoring data set at A2 (utility-scale power-plant Muttsee), months 10.2022-09.2023
Provider: AXPO - IWB Industrielle Werke Basel
Last edition: 17.01.2024
Editor: Y.Frischholz, Laboratory of Cryospheric Sciences, EPFL
File name structure:
The power-plant is made of 23 string boxes (GAK) and inverters (WR) named after their given number. One file per device is provided in the data/ subfolder, following the same naming convention (e.g. for the string box number 1, the file name is: “A2_GAK01.csv” and for the corresponding inverter, the file name is “A2_WR01.csv”). Refer to the publication for further details on the setup of the power plant.
File format:
All files are provided in the CSV format.
Column headings:
	String boxes and inverters (GAK & WR):
	⁃	device_name (string): name of the device (same as file name)
	⁃	timestamp (datetime64): time stamp in format “YYYY-MM-DD HH:mm:ss”, time-zone: UTC
	String boxes (GAK):
	⁃	I_ (double): input current from the substring number (W)
	⁃	I_SUM (double): output current of string box (A)
	⁃	U_DC (double): output voltage of string box (V)
	⁃	P_DC_ (double): input DC power from the substring number (W)
	⁃	P_DC (double): output DC power of string box (W)
	⁃	$S$_T#_$T$ (double): temperature from the sensor connected to the string box (empty if not existing), placed on the “front” or “back” side (S) of table T. Two types of temperature measurements (#) are available: 1=standard cell temperature (°C) , 2=module temperature on the rear side (°C) .
	⁃	$S$_SRAD_T (double): poa irradiation (W/m**2) from the sensor connected to the string box (empty if not existing), placed on the “front” or “back” side (S) of table T.
		Inverters (WR):
	⁃	I_DC (double): input DC current(A)
	⁃	U_DC(double): input DC voltage(V)
	⁃	P_DC (double): input DC power (W)
	⁃	P_AC (double): output AC power (W)
Preprocessing:
	1. Outliers:
	⁃	Quantiles Q1 and Q99 are computed for each variable
	⁃	Values below Q1 and above Q99 are considered outliers
	⁃	Outliers are replaced by NaNs.

	2. Missing data:
	⁃	Data holes of less or equal to 3 hours are interpolated linearly for all variables
	⁃	Data holes of more than 3 hours are replaced using the monthly nan-mean of the considered hour if more than 50% of the qualifying data points are available. For instance, if the data point 2022-12-12 12:12:00, is missing for a given variable and is part of a data hole larger than 3 hours, then it is replaced by the mean of all data points with hour = 12 of the month of December 2022, if 50% of all the qualifying data points are not NaN. In case more than 50% of the qualifying points are missing, the data point remains as missing (NaN).
	3. Wiring losses:
	⁃	To be consistent with A1, wiring losses are corrected at A2. The wiring loss between the string boxes and the inverters is removed by multiplying the value of input P_DC at the inverter level (P_DC,inv) by the mean ratio P_DC,inv / P_DC,arr.
	⁃	The computed mean wiring loss is 2.3\%. It is computed from 2023-02-01 to 2023-09-30, after the long data hole in the data sets of the string boxes.

Mean uptime:
	Raw:
	⁃	WR: 98.2%
	⁃	GAK*: 38.5%
	Processed:
	⁃	WR: 99.6%
	⁃	GAK*: 56.4%

	*The uptime at the string box level (GAK) is low because PMUs at this level shut down during steady and low signal leading to unavailable data at night. In addition, the 23 days downtime of 	January 2023 could not be interpolated nor replaced.

format: ZIP

en:
Title: Monitoring data set at A1 (test-site of Totalp), years 2018-2022
Provider: ZHAW - EKZ
Edition: Y.Frischholz, Laboratory of Cryospheric Sciences, EPFL
Last edition: 17.01.2024
File name structure:
The test site contains 6 segments named from A to F. The six files provided in the data/ subfolder follow the same naming convention (e.g. for segment A, the file name is: “A1_A.csv”). Refer to the publication for further details on the setups of the segments.
File format:
All files are provided in the CSV format.
Column headings:
	•	timestamp (datetime64): time stamp in format “YYYY-MM-DD HH:mm:ss”, time-zone: UTC
	•	poa_front (double): front plane-of-array irradiation (W/m**2)
	•	poa_back (double): rear plane-of-array irradiation (W/m**2)
	•	mod_temp (double): module’s temperature (°C)
	•	P_AC (double): output AC power (W)
Preprocessing:
	Outliers:
	⁃	Quantiles Q1 and Q99 are computed for each variable
	⁃	Values below Q1 and above Q99 are considered outliers
	⁃	Outliers are replaced by NaNs.
	Missing data:
	⁃	Data holes of less than 3 hours are interpolated linearly for all variables
	⁃	Data holes of more than 3 hours are replaced using the monthly nan-mean of the considered hour if more than 50% of the qualifying data points are available. For instance, if the data point 2022-12-12 12:12:00, is missing for a given variable and is part of a data hole larger than 3 hours, then it is replaced by the mean of all data points with hour = 12 of the month of December 2022, if 50% of all the qualifying data points are not NaN. In case more than 50% of the qualifying points are missing, the data point remains as missing (NaN).
	Wiring losses :
	⁃	As described in [1] , the test-site setup caused abnormally large wiring losses, mainly due to the distance to optimisers and to the low installed power per DC-wire.
	⁃	The wiring losses comprised between 10% and 15%, are averaged hourly and added to the measured DC power.

	Misc.:
	⁃	P_AC is computed for each segment based on the measured DC power and the average inverter’s efficiency (96.1%).
	⁃	As described in [1], the peak power of the installed module, used to compute the final yield is slightly different from the values announced by the manufacturer.
Mean uptime:
	Raw:
	⁃	95.2%
	Processed:
	⁃	100%
References:
[1] 	D. Anderegg, S. Strebel, and J. Rohrer, “Alpine Photovoltaik Versuchsanlage Davos Totalp : Erkenntnisse aus 5 Jahren Betrieb,” de, Sep. 	2023, Accepted: 2023-09-29T09:13:16Z Publisher: ZHAW Zürcher Hochschule für Angewandte Wissenschaften.DOI: 10.21256/	zhaw-2524. [Online]. Available: https://digitalcollection.zhaw.ch/handle/11475/28797 (visited on 12/15/2023).

en:
On this repository you can find the monitoring data of the 3 PV power-plants analysed in the article _"Confirmation of the power gain for solar photovoltaic systems in alpine areas"_ (doi: 10.3389/fenrg.2024.1372680) . Each folder contains the following two elements:
1. **README.txt** : describes the content of the folder. Especially it describes the plant's monitoring system's architecture and relates it to the various files included in the data sub-folder. It also provides a detailed description of all processing steps leading to the data sets as available in the data sub-folder and used in the pre-cited article.
3. **data/** : sub-folder containing the file(s) used in the referenced article.

alps

display_name: alps

bifaciality

display_name: bifaciality

photovoltaic

display_name: photovoltaic

snow

display_name: snow

winter-electricity

display_name: winter-electricity

en: Monitoring data sets of alpine photovoltaic power-plants


maintainer: EnviDat Support

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

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: Roksolana Petrovska

en: lar fagus shade-tolerance regeneration mortality-rate acer dominance

en: Mortality of regeneration

media_type: text/csv

en: Acer platanoides, Acer pseudoplatanus and Fagus sylvatica living and dead juveniles (0-500 cm in height) grown in the deep shade of a prime beech-dominated forest.

format: CSV

en: The R-script of survival analysis for accelerated failure time model

en:
 One individual per species, vitality class (low and high) and height class (eight classes: 0–10, 11–20, 21–35, 36–60, 61–90, 91–130, 131–200 and 201–500 cm) was randomly selected and harvested in each of the six plots. This resulted in a sample of 82, 80 and 89 living individuals of A. platanoides, A. pseudoplatanus and F. sylvatica, respectively.

 Additionally stems of dead Acer spp. and F. sylvatica trees that had died within the last three years (2015–2018) were randomly harvested, matching the height classes of the harvested living trees wherever possible. In total, 179 dead young trees (60 A. platanoides, 72 A. pseudoplatanus and 47 F. sylvatica) were collected.

 Variables:
* species_code: a_pla - Acer platanoides, a_pse - Acer pseudoplatanus, f_syl - Fagus sylvatica
* species: as above
* dummy: 0 - living individual, 1 - dead individual
* LAR_cm2_g: leaf area ratio or ratio of leaf area to total plant biomass, [cm2/g]
* tree_age: in years
* avg_ring_micron: average width of the last 5 rings in tree life excluding the last ring
* dry_mass_g: aboveground and belowground biomass
* DLI: direct light index (measured only under living individuals)
* BLI: diffuse light index (measured only under living individuals)
* GLI: global light index

acer

display_name: acer

dominance

display_name: dominance

fagus

display_name: fagus

lar

display_name: lar

mortality-rate

display_name: mortality-rate

regeneration

display_name: regeneration

shade-tolerance

display_name: shade-tolerance

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: Mortality of regeneration: Acer spp. and Fagus sylvatica


maintainer: Frank Techel

en: european-alps avalanche-forecast warning-regions snow danger-level

en: Forecast avalanche danger level

en: contains the highest forecast danger level (dangerLevelMax) for each day and warning region

mimetype: text/csv

en: shape file warning regions Alps

mimetype: application/zip

en:
This dataset contains the data used in the publication by Techel et al., 2018 _Spatial consistency and bias in avalanche forecasts - a case study in the European Alps_ (Nat Haz Earth Syst Sci).
For details on the data please refer to this publication.
The dataset contains the following:
- shape files for the warning regions in the Alps
- highest forecast danger level for each warning region and day

avalanche-forecast

display_name: avalanche-forecast

danger-level

display_name: danger-level

european-alps

display_name: european-alps

warning-regions

display_name: warning-regions

educ

en: Education, culture and sport

en: Forecast avalanche danger level European Alps 2011 - 2015


maintainer: Paraskevi Georgakaki

en: wrf jungfraujoch ice-water-content secondary-ice-production liquid-water-content ice-crystal-number-concentration blowing-snow swiss-alps

en: Observational data

en:
The .zip file contains 6 .csv files including all microphysical measurements taken at the Jungfraujoch station during the two periods of interest:

* 25-28 January 2014 : northwesterly-winds (NW)
* 29 January - 1 February 2014 : southeasterly-winds (SE)

The total Ice Crystal Number Concentration (ICNC; reported in L⁻¹) is measured by the two-dimensional stereo hydrometeor spectrometer (2D-S; Lawson et al., 2006) over the size range 10-1280 μm. The total ice water content (IWC; reported in gm⁻³) is also derived by the 2D-S data using the Brown and Francis (1995) mass-diameter relationship. Finally, the quantification of the liquid water content (LWC; reported in gm⁻³) is based on the liquid droplet size distribution data derived from a DMT cloud droplet probe (CDP; Lance et al., 2010) over the size range 2-50 μm. The frequency of the measurements in the .csv files is 30 min, to match the output frequency of the model. Datetime is also reported in the files (DD/MM/YYYY h:mm) in UTC.

Acknowledgment: Collection and pre-processing of the microphysical measurements has been performed by Gary Lloyd (gary.lloyd@manchester.ac.uk). More details about the instrumentation during the CLACE2014 campaign can be found in Lloyd et al. (2015).

en: WRF outputs

en:
The .zip file contains all the NetCDF outputs produced by the mesoscale Weather Research and Forecasting (WRF) model (version: 4.0.1). Two case studies were simulated in this study, between 25-28 January 2014 (00:00 UTC) and 29 January - 1 February 2014 (00:00 UTC), respectively. The former is denoted as "NW" and the latter as "SE", owing to the prevailing wind conditions at the Jungfraujoch (JFJ) station.

The outputs come from the innermost domain of the simulations with a spatial resolution of 1 km and are extracted from the nearest to JFJ model grid point (46.55°N 7.98°E). The dimensions of each model variable is [145,100], which corresponds to time and height, respectively. The frequency of the outputs is 30 min. Note that the first 24 hours of each simulation (i.e., the first 48 outputs) were considered spin up time.

The following sets of sensitivity simulations were presented in this study:

* CNTRL_NW.nc, CNTRL_SE.nc : Sensitivity simulations performed with the default version of WRF
* DS_PHILL_NW.nc, DS_PHILL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Phillips et al. (2018)
* DS_SULL_NW.nc, DS_SULL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Sullivan et al. (2018)
* PHILL_NW.nc, PHILL_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Phillips et al. (2017)
* TAKAH_NW.nc, TAKAH_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Sullivan et al. (2018) based on Takahashi et al. (1995)
* TAKAHrim_NW.nc, TAKAHrim_SE.nc : Same set-up as before but applied only to collisions between rimed ice particles
* BIPS_Xk0_DY_NW.nc, BIPS_Xk0_DY_SE.nc : Sensitivity simulations including a source of Xk0 (either 10.0 or 100.0 L⁻¹) ice crystals with sizes of DY (either 10 or 100 μm) in the first atmospheric level of the model
* BIPS100_PHILL_NW.nc, BIPS100_PHILL_SE.nc : Sensitivity simulations where the combined effect of blowing snow and ice-ice collisional break-up is examined

en:
This repository contains all WRF model outputs and observational data sets used for the paper:
Georgakaki, P., Sotiropoulou, G., Vignon, É., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021.

blowing-snow

display_name: blowing-snow

ice-crystal-number-concentration

display_name: ice-crystal-number-concentration

ice-water-content

display_name: ice-water-content

jungfraujoch

display_name: jungfraujoch

liquid-water-content

display_name: liquid-water-content

secondary-ice-production

display_name: secondary-ice-production

swiss-alps

display_name: swiss-alps

en: Data for the publication "Secondary ice production processes in wintertime alpine mixed-phase clouds"


en: food-preferences latent-semantic-analysis national-dishes food-taste-similarity agro-food-trade

en: information on ingredients of national dishes

en: shows information of ingredients of all national dishes of 171 countries and their names

en: National dishes, migration data and data for bilateral food trade flows

en:
The data include information on national dishes, migration data and data for bilateral food
trade flows for 5 years as well as importer and exporter GDP, population, distance between
most populated cities, bilateral information on Preferential Trade Agreements, common
official primary langauge, common colonizer post-1945, colonial relationship for overall
171 countries. The final data set contains 145,350 observations (29,070 country pairs for
5 years)

en: dyadic food similarity measures, pairwise similarity and dissimilarity (chord diagram)

en:
The figures show the distribution of two dyadic food tastes similarity measures on the base of
the national dishes: (i) the Manhattan distance and the (ii) latent semantic analysis. The chord diagram
shows the results for country pairs with most and less similarity on basis of the LSA-approach.

en: The data described in this article were collected daily over the period 4 June 2018 to 23 August 2018 and contains information of several data sources. The database includes information on national recipes and their ingredients for 171 countries, measures for food taste similarities between all 171 countries as well as bilateral migration and agro-food trade data for 5 years. The database can be used for analyzing e.g., the relation between food preferences and international trade or food preferences and health outcomes (e.g., obesity) across countries.

agro-food-trade

display_name: agro-food-trade

food-preferences

display_name: food-preferences

food-taste-similarity

display_name: food-taste-similarity

latent-semantic-analysis

display_name: latent-semantic-analysis

national-dishes

display_name: national-dishes

envidat

en: EnviDat: the Environmental Data Portal

en: Data of national dishes, their similarity and trade flows


maintainer: Alec van Herwijnen

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

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

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

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

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

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

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

depth-hoar

display_name: depth-hoar

microstructure

display_name: microstructure

slab-avalanche

display_name: slab-avalanche

snow

display_name: snow

snow-mechanics

display_name: snow-mechanics

strength

display_name: strength

educ

en: Education, culture and sport

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: Irene Cordero

en: land-use pesticide soil-nutrients drought heatwave soil soil-biodiversity

en: All soil data

en: Soil physicochemical characteristics

format: XLS

en:
Soil chemical and biological properties of soils affected by 10 different perturbations related with global change, applied individually or in combination. Greenhouse experiment. Perturbations applied to intact soil cores (15 cm diameter, 15 cm deep) collected in an extensively managed grassland on the WSL grounds in June 2023.
This dataset contains all data on which the publication below was based:
"Fioratti Junod M, Gombeer S, Holmes J, Zimmerman S, Rillig M, Risch AC and Cordero I. Soil functionality declines under multiple superimposed global change perturbations. XXXX "
Please, cite this publication together with the citation of the datafile.
Database includes:
Pot_ID: pot or soil core identifier.
Batch: numeric, 1 or 2.
Enzyme_plate: numeric, from 1 to 6.
Number_of_perturbations: numeric, from 0 to 10.
N_addition: binary. Yes = 1, No = 0.
P_addition: binary. Yes = 1, No = 0.
Defoliation: binary. Yes = 1, No = 0.
Trampling: binary. Yes = 1, No = 0.
Insecticide: binary. Yes = 1, No = 0.
Fungicide: binary. Yes = 1, No = 0.
Herbicide: binary. Yes = 1, No = 0.
Antibiotic: binary. Yes = 1, No = 0.
Drought: binary. Yes = 1, No = 0.
Heat_wave: binary. Yes = 1, No = 0.
Treatment: character. Description of the treatment, 15 levels.
SWC: soil water content. Numeric, unitless.
Green_biomass: green plant biomass. Numeric, g.
Brown_biomass: brown or dead plant biomass. Numeric, g.
Biomass_cut: plant biomass cut during defoliation treatments. Numeric, g.
CO2_flux_light: net CO2 flux under ambient light. Numeric, mg CO2 m-2 h-1.
CO2_flux_dark: ecosystem respiration. Numeric, mg CO2 m-2 h-1.
S: organic matter stabilisation factor (tea bag index). Numeric, unitless.
k: organic matter decomposition rate (tea bag index). Numeric, unitless.
DOC: dissolved organic carbon, mg Kg-1 dry soil.
IC: inorganic carbon, mg Kg-1 dry soil.
Ammonium_KCl: plant available ammonium, mg Kg-1 dry soil.
Nitrate_KCl: plant available nitrate, mg Kg-1 dry soil.
Phosphate: phosphate, mg Kg-1 dry soil.
MBC: microbial biomass carbon, mg Kg-1 dry soil.
MBN: microbial biomass nitrogen, mg Kg-1 dry soil.
PHO: phosphatase activity, nmol h-1 g-1 dry soil.
BG: β-glucosidase, nmol h-1 g-1 dry soil.
XYL: xylosidase, nmol h-1 g-1 dry soil.
CBH: cellobiohydrolase, nmol h-1 g-1 dry soil.
NAG: N-acetylglucosaminidase, nmol h-1 g-1 dry soil.
LAP: leucine aminopeptidase, nmol h-1 g-1 dry soil.
POX: phenoloxidase, nmol h-1 g-1 dry soil.
PER: peroxidase, nmol h-1 g-1 dry soil.
pH: soil pH, unitless
Water_stable_aggregates: water stable aggregates, %
Surface_infiltration_time: water surface infiltration time, s.

drought

display_name: drought

heatwave

display_name: heatwave

land-use

display_name: land-use

pesticide

display_name: pesticide

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-nutrients

display_name: soil-nutrients

en: Escalating effects of multiple perturbations on soil functionality


maintainer: Athanasios Nenes

en: supersaturation cloud-droplets sigma-w wolfgangpass raclets cloud-modeling

en: Predicted cloud droplet numbers (kappa=0.1) Davos Wolfgang

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.1 is assumed, which is the lower limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The wind-lidar derived standard deviation of updraft velocities (sigma-w) in m/s, used to apply the parameterization employing a single characteristic velocity (Morales and Nenes, 2010)
* The maximum predicted cloud parcel supersaturation (Smax%)
* The total in-situ aerosol number concentration (Naer) in cmˉ³, measured by the SMPS
* The predicted cloud droplet numbers (Nd) in cmˉ³
* The activation fraction (%)

en: Predicted cloud droplet numbers (kappa=0.25) Weissfluhjoch

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.25 is assumed, which is the upper limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The wind-lidar derived standard deviation of updraft velocities (sigma-w) in m/s, used to apply the parameterization employing a single characteristic velocity (Morales and Nenes, 2010)
* The maximum predicted cloud parcel supersaturation (Smax%)
* The total in-situ aerosol number concentration (Naer) in cmˉ³, measured by the SMPS
* The predicted cloud droplet numbers (Nd) in cmˉ³
* The activation fraction (%)

en: Cloud droplet properties were predicted between February 24 and March 8 2019 for the measurement site Davos Wolfgang (1630 m a.s.l., LON: 9.853594, LAT: 46.835577). Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from a Scanning Mobility Particle Size Spectrometer (SMPS) instrument deployed at Davos Wolfgang (https://www.envidat.ch/dataset/aerosol-data-davos-wolfgang). The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at the same station and are extracted from the first bin of the instrument, being 200 m above ground level. The hygroscopic properties of the particles measured at Davos Wolfgang could not be estimated, owing to a lack of concurrent CCN measurements. As a sensitivity test, droplet calculations are performed assuming two different values of the aerosol hygroscopicity parameter, 0.1 and 0.25, based on the analysis carried out for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

wolfgangpass

display_name: wolfgangpass

en: Predicted cloud droplet numbers Davos Wolfgang


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

en: Updated SDM outputs

en: Refined scenarios of plants distribution in the European Alps

format: GeoTIFF

en: Data Access

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

beta-diversity

display_name: beta-diversity

conservation

display_name: conservation

european-alps

display_name: european-alps

functional-diversity

display_name: functional-diversity

global-change

display_name: global-change

land-use-change

display_name: land-use-change

migclim

display_name: migclim

phylogenetic-diversity

display_name: phylogenetic-diversity

plants

display_name: plants

species-distribution-models

display_name: species-distribution-models

taxonomic-diversity

display_name: taxonomic-diversity

zonation

display_name: zonation

envidat

en: EnviDat: the Environmental Data Portal

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


en: climate-change scenario policy-action visualization landscape-change

en: Entremon subtitles FR

en: Scenarios of landscape change under climate change in the Swiss mountains. Trailer in French with subtitles.

en: Seeland subtitles FR

en:
Scenarios of landscape change under climate change in the Swiss lowlands.
Trailer in French with subtitles.

en: Seeland subtitles DE

en:
Scenarios of landscape change under climate change in the Swiss lowlands.
Trailer in German with subtitles.

en: Entremont subtitles DE

en:
Scenarios of landscape change under climate change in the Swiss mountains.
Trailer in German with written subtitles.

en:
Short videos showing scenarios of potential landscape development in a 4°C warmer climate for a Swiss mountain region and a region on the Swiss Plateau. The videos are in German or French with subtitles.
The videos' key message is: Climate change will have an impact on the Swiss landscapes. However, according to the strategy chosen to adapt to climate change, society and politics will also have a strong influence on the future landscapes and the services they will provide. If no action is taken to adapt to climate change today, then society will have to react in a hurry at a later point in time. This can often result in resource-intensive technical solutions. Conversely, if society takes action in an anticipatory way already today, nature-based solutions can be implemented more successfully and humans can benefit more from the landscape services..
These short videos are teasers that should make the viewers curious to visit the panorama pictures (https://viergrad.envidat.ch/).

climate-change

display_name: climate-change

landscape-change

display_name: landscape-change

policy-action

display_name: policy-action

scenario

display_name: scenario

visualization

display_name: visualization

educ

en: Education, culture and sport

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: Teaser videos Swiss landscapes under climate change


en: spatial-modelling avalanche-forecast forecast-models avalanche-release-probability machine-learning

en: Randomly sampled subset of grid points from 1 km DEM

en: Contains the randomly sampled subset of grid points from 1 km DEM. Three variables: coordinates (x, y) and elevation (elev).

en: Snowline estimates 2022/2023

en: Daily snowline estimates provided by observers for North (aspect = 0) and South (aspect = 180) slopes for the 2022/2023 season. For aspects in between North and South, values are interpolated. Contains observation date (date), coordinates (Swiss coordinates x, y), snowline elevation (snowline), and aspect.

en: Natural avalanches 2023/2024

en: Natural avalanche observation (dry avalanches). 2023/2024 forecasting season.

en: Snowline estimates 2023/2024

en: Daily snowline estimates provided by observers for North (aspect = 0) and South (aspect = 180) slopes for the 2023/2024 season. For aspects in between North and South, values are interpolated. Contains observation date (date), coordinates (Swiss coordinates x, y), snowline elevation (snowline), and aspect.

en: Avalanche forecast

en: Data contains forecasts relating to dry-snow avalanche conditions only. Forecast issued at 17.00 local time. Contains variables for the warning region (sector_id), publication date (date), danger level - sub-level combination expressed using numeric values (integer value of danger level -0.33 for sub-level minus, -0 for sub-level neutral, +0.33 for sub-level plus, altitude as indicated in the bulletin, and whether aspect (North = 0, Northeast = 45, ...) was indicated in the bulletin (no: 0, yes: 1).

en: Human-triggered avalanches 2023/2024

en: The file contains the reported human-triggered avalanches. The variables are as follows: id, release date (date), name, Swiss coordinates (x,y), elevation (ele), remark, reported avalanche size expressed using avalanche activity index (sizeAAI), aspect, event type (type), event (yes = 1, no = 0)

en: Model predictions (raw data)

en: Model predictions (raw data) for three models, in nowcast and forecast mode. Not all models are available for both seasons. - These data are the basis for the interpolated predictions, which can be found in the other zip-folder

en: GPS data and human-triggered avalanches

en: Contains (1) the subset of points extracted from GPS tracks recorded during backcountry touring and uploaded to the website skitourenguru.ch and (2) the corresponding subset of human-triggered avalanches. Data (1) was provided by Skitourenguru GmbH and data (2) was collected by SLF. - Variables contained are date, Swiss coordinates (x, y), aspect (N = 0, NE = 45, ...), elevation (ele), and event (event = 1, non-event = 0).

en: Human-triggered avalanches 2022/2023

en: Natural avalanches 2022/2023

en: Natural avalanche observation (dry avalanches). 2022-2023 forecasting season.

en: Model predictions (interpolated)

en: The zip file contains csv-files the interpolated model predictions for three models, in forecast and nowcast-mode, for the three different data sets, as described in the publication.

en:
This data set was used in the analysis by Techel et al. **Can model-based avalanche forecasts match the discriminatory skill of human danger level forecasts? A comparison from Switzerland**. Please note that the title of the preprint was *Forecasting avalanche danger: human-made forecasts vs. fully automated model-driven predictions*, submitted to *Natural Hazards Earth System Sciences* on 20 Aug 2024.
The final manuscript used data from three forecasting seasons (2022/2023, 2023/2024, 2024/2025) , the preprint two forecasting seasons (2022/2023, 2023/2024) .
Currently, the repository contains data from two avalanche forecasting seasons in Switzerland. The third season will be added (to be done, 30 June 2025).
**Interpolated predictions**
- The .zip file contains the interpolated predictions for the three models in nowcast- and forecast- mode. This data is needed to reproduce the figures and tables in the submitted preprint.
The other data are the **raw data** underlying the interpolations:
- Avalanche forecast by WSL Institute for Snow and Avalanche Research SLF, published at 17.00 local time, valid for the following 24 hours and relating to dry snow avalanche conditions.
- Model predictions in *nowcast*- and *forecast*-mode for three models (*danger level*, *instability*, *natural avalanche*), valid for 12.00 local time
- Subset of points extracted from GPS tracks (courtesy of Skitourenguru GmbH)
- Avalanche observations - natural avalanches and human-triggered avalanches
- Estimates of the snowline
- Randomly chosen subset of grid points used for generating reference distributions
For details regarding the data sets refer to the publication.

avalanche-forecast

display_name: avalanche-forecast

avalanche-release-probability

display_name: avalanche-release-probability

forecast-models

display_name: forecast-models

machine-learning

display_name: machine-learning

spatial-modelling

display_name: spatial-modelling

en: Evaluating the predictive performance of human avalanche forecasts and model predictions in Switzerland


en: picea-abies subalpine afforestation belowground-biomass isotopes

en: This data set provides soil organic carbon, total nitrogen, and stable carbon isotope (13C) values of mineral soil (0-45cm), organic horizons (Oi, Oe, Oa), and root biomass of an afforestation sequence (0-130years) on a subalpine pasture in Jaun, Switzerland.

afforestation

display_name: afforestation

belowground-biomass

display_name: belowground-biomass

isotopes

display_name: isotopes

picea-abies

display_name: picea-abies

subalpine

display_name: subalpine

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: Soil organic carbon stock of an afforestation sequence on a subalpine pasture


en: environmental---history environmental--policy switzerland ecology wetlands historical-carthography

en: Wetlands 1850 - 2010 (shapefiles)

en:
 History of wetlands - GIS-Layers
 Location of wetlands based on signatures in topographic maps
* Basis 1850: original measuring table sheets Dufour created between the years 1834 and 1863.
* Basis 1900/ Basis 1950: The Siegfried maps were updated and published at regular intervals between 1870 and 1949. This long period of time made it possible to reconstruct wetlands for two periods based on the Siegfried Maps: the first edition (hereinafter referred to as "1900") and the last edition ("1950").
* Basis 2010: Map sheets from the National Map from 2006 to 2011 were used for the most recent time period of the reconstruction.
 Location of wetlands due to reconstructions
The reconstruction **must not** be interpreted in the sense of a surface-accurate separation, but shows without claim to location accuracy the approximate extent of the areas that would have corresponded to the mapping criteria for wetlands used in today's topographic maps around 1850, 1900 and 1950. The basics can be found in Stuber & Bürgi 2019, chapter 5.4.

en:
Naturally, large parts of the Swiss Plateau are characterised by wetlands
and meandering rivers. That this is no longer the case today is the result
of centuries of efforts to obtain dry land. But how did this process take
place? What were the relevant actors and what were their motivations? And
what can be said about the ecological consequences of this development?

In a research project on the history of wetlands in Switzerland since 1700,
we conducted (a) a historical analysis of the development of land use in
wetlands and the actors involved, (b) a historical-cartographic
reconstruction of wetland extent since 1850 and (c) an evaluation of
ecological effects of changes in wetlands on various organisms groups.

The series of GIS layers on wetland history stem from the second part of
the project. The area reconstruction is based on digitized and homogenized
signatures from national map series, as they have been available since
about 1850. Details about the digitalization process and the homogenization
procedures applied ("Rekonstruktionen") are included in Stuber & Bürgi
2019.

Book Citation:
 > Stuber M, Bürgi M (2019) Vom «eroberten Land» zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. "Bristol Schriftenreihe", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.

ecology

display_name: ecology

environmental---history

display_name: environmental---history

environmental--policy

display_name: environmental--policy

historical-carthography

display_name: historical-carthography

switzerland

display_name: switzerland

wetlands

display_name: wetlands

en: History of wetlands in Switzerland since 1850


maintainer: Michael Lehning

en: snow-farming terrestrial-laser-scanning snowpack winter-tourism snow-conservation snow

en: Martell .smet

en: Meteodata used as input for SNOWAPCK simulations, orginating from Station Hintermartell.

en: HS Martell autumn 2015

media_type: text/plain

en:
Snow depth/ height of snow heap in autumn 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: European Datum 1950 UTM
Nodata: -9999

format: TXT

mimetype: text/plain

en: HS Martell spring 2015

en:
Snow depth/ height of snow heap in spring 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: European Datum 1950 UTM
Nodata: -9999

en: Martell .sno

en: Input snow profile (.sno file) used for SNOWPACK simulations of the Martell heap

en: Fluela .smet

en: Meteodata used as input for SNOWAPCK simulations, orginating from Station Windtunnel and DAV.

en: HS Fluela autumn 2015

en:
Snow depth/ height of snow heap in autumn 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: CH1903
Nodata: -9999

en: Fluela .sno

en: Input snow profile (.sno file) used for SNOWPACK simulations of the Flüela heap

en: HS Fluela spring 2015

en:
Snow depth/ height of snow heap in spring 2015 obtained by terrrestrial laserscanning
cell size: 10 cm
spatial reference: CH1903

en:
Two data sets obtained for snow farming projects (Fluela, Davos, CH and Martell, IT) in 2015.

The data set contains for each site:

* 10 cm GIS raster of snow depth calculated from terrestrial laserscanning surveys (TLS) in the end of winter season (April/May)

* 10 cm GIS raster of snow depth calculated from TLS in the end of summer season (October)

Input files for SNOWPACK model:

* .sno: snow profile at the end of winter

* .smet: meteorological data measured by weather stations in the area

For more details see Grünewald, T., Lehning, M., and Wolfsperger, F.: Snow farming: Conserving snow over the summer season, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-93, in review, 2017.

snow

display_name: snow

snow-conservation

display_name: snow-conservation

snow-farming

display_name: snow-farming

snowpack

display_name: snowpack

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

winter-tourism

display_name: winter-tourism

en: Snowfarming data set Davos and Martell 2015


en: ground-temperature frost-change-velocity soil sdms aridity precipitation

media_type: image/tiff

en: Frost change frequency modified from CHELSA

format: TIFF

en: Precipitation of driest quarter modified from CHELSA

en: Precipitation seasonality from CHELSA

en: Soil organic carbon content modified from SoilGrids

en: Mean diurnal range modified from CHELSA

en: Mean annual temperature modified from CHELSA

en: Soil pH modified from SoilGrids

en: Soil clay content modified from SoilGrids

en: The dataset contains seven environmental layers (average annual temperature, aridity [annual precipitation divided by annual potential evapotranspiration], frost change frequency, precipitation in the driest quarter, mean diurnal temperature range, and precipitation seasonality) modified from CHELSA (https://chelsa-climate.org/) and three soil layers (soil organic matter content, pH water, and clay content) modified from SoilGrids (https://soilgrids.org/).

aridity

display_name: aridity

frost-change-velocity

display_name: frost-change-velocity

ground-temperature

display_name: ground-temperature

precipitation

display_name: precipitation

sdms

display_name: sdms

soil

display_name: soil

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: Environmental layers for SDM simulations (GDPlants)


en: strategic-spatial-planning urban-transformations land-use-transformations large-scale-urban-development-projects strategic-projects europe land-use-change spatial-planning

en: QCA R script large-scale urban development projects Europe

en:
For the set up of this R script, we used the “QCA” (Duşa, 2019) and “SetMethods” (Oana and Schneider, 2018) R packages.

The following further resources were also used:
Dusa, A. (2019). QCA with R: A comprehensive resource. Springer: Berlin.
Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences. A guide to qualitative comparative analysis. Cambridge: Cambridge University Press.
Thomann, E., Oana, I.-E., & Wittwer, S. (2018). Performing fuzzy- and crisp set QCA with R. A user-oriented beginner's guide. Retrieved from http://www.evathomann.com/links/qca-r-manual

en: QCA data set large-scale urban development projects

en: Please refer to the sections: 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalization in the Metadata for further details on this resource.

en:
Table of Content: 1. General context of the data set "lsUDPs" ; 2. Background and aims of the study using the data set lsUDPs; 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalisation

1. General context of the data set "lsUDPs"
The data set "lsUDPs" has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2020. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, strategic spatial plans) into quantitative land-change modelling approaches at the urban regional level. The first stage (2016-2017) of the CONCUR project focussed on 21 urban regions in Western Europe. The urban regions were selected through a multi-stage strategy for empirical research (see Hersperger, A. M., Grădinaru, S., Oliveira, E., Pagliarin, S., & Palka, G. (2019). Understanding strategic spatial planning to effectively guide development of urban regions. Cities, 94, 96–105. https://doi.org/10.1016/j.cities.2019.05.032 ).

2. Background and aims of the study using the data set lsUDPs
As part of the CONCUR project, a specific task was to examine the relationship between strategic spatial plans and the formulation and implementation (i.e. urban land change) of large-scale urban development projects in Western Europe.
Strategic urban projects are typically large-scale, prominent urban transformations implemented locally with the aim to stimulate urban growth, for instance in the form of urban renewals of deprived neighborhoods, waterfront renewals and transport infrastructures. While strategic urban projects are referred to in the literature with multiple terms, in the CONCOR project we call them large-scale urban development projects (lsUDPs).
Previous studies acknowledged both local and supra-local (or structural) factors impacting the context-specific implementation of lsUDPs. Local governance factors, such as institutional capacity, coordination among public and private actors and political leadership, intertwine with supra-local conditions, such as state re-scaling processes and devolution of state competencies in spatial planning, de-industrialisation and increasing social inequality. Hence, in implementing lsUDPs, multi-scalar factors act in combination.
Because the formulation and implementation of lsUDPs require multi-scalar coordination among coalitions of public and private actors over an extended period of time, they are generally linked to strategic spatial plans (SSPs). Strategic spatial plans convey collective visions and horizons of action negotiated among public and private actors at the local and/or regional level to steer future urban development, and can contain legally binding dispositions, but also indicative guidelines.
The key question remains as to what extent large-scale urban development projects and strategic spatial plans can be regarded as aligned. By alignment, or “concordance”, we mean that strategic projects are formulated and implemented as part of the strategic planning process (“high concordance”), or that the strategic role of projects is reconfirmed in (subsequent) strategic plans (“moderate concordance”). Lack of concordance is found when lsUDPs have been limitedly (or not at all) acknowledged in strategic spatial plans.
We assume that certain local and supra-local factors, characterising the development of the projects, foster (but not strictly “cause”) the degree of alignment between lsUPDs and SSPs. In this study, we empirically examine how, and to what extent, the concordance between 38 European large-scale urban development projects and strategic plans (outcome: CONCOR) has been enabled by five multi-scalar factors (or conditions): (i) the role of the national state (STATE), (ii) the role of (inter)national private actors (PRIVATE), (iii) the occurrence of supra-regional external events (EVENTS), (iv) the degree of transport connectivity (TRANSP), and (v) local resistance from civil society (RESIST). We adopted a (multi-data) case-based qualitative strategy for empirical research and applied the formalised procedure of within- and cross-case comparison offered by fuzzy-set Qualitative Comparative Analysis appropriate for the goal of this study. Based on set theory, QCA formally integrates contextual sensitivity to case specificities (within-case knowledge) with systematic comparative analysis (across-case knowledge).
The research question the data set has been created to reply to is the following: which conditions, and combinations of conditions, enable the concordance between large-scale urban development projects and strategic spatial plans?
The conditions (“independent variables”) considered are. STATE: the set of large-scale urban projects characterized by a high degree of state intervention and support in their formulation and implementation, PRIVATE: the set of large-scale urban projects characterized by a high degree of involvement of (inter)national private actors in their formulation and implementation, EVENTS: the set of large-scale strategic projects whose formulation and implementation have been strongly affected by unforeseen international events and/or global trends, TRANSP: the set of large-scale strategic projects with a high degree of road and/or transit connectivity, and RESIST: set of large-scale strategic projects whose realization has been characterized by resistances that have substantially delayed or modified the project implementation.
The outcome (“dependent variable”) under analysis is CONCOR: the set of large-scale urban projects having a high degree of concordance/alignment/integration with strategic spatial plans

3. The data set lsUDPs

   3.1 Selection of cases and data collection
To generate the current data set on large-scale urban development projects in European urban regions (data set "lsUDPs"), we identified 35 large-scale urban development projects in a sample of the 21 Western urban regions considered in the CONCUR project (see supra, Hersperger et al. 2019): Amsterdam, Barcelona, Copenhagen, Hamburg, Lyon, Manchester, Milan, Stockholm, Stuttgart. The criteria we followed to identify the 35 large-scale urban development projects are: geographical location, size (large-scale), site (located either in the city core or in the larger urban region) and urban function (e.g. housing, transportation infrastructures, service and knowledge economic functions). Employing these criteria facilitated the selection of diverse large-scale urban development projects while still ensuring sufficient comparability.
In 2016, we performed 47 in-depth interviews with experts in urban and regional planning and large-scale strategic projects and infrastructure (i.e. academics and practitioners) about the formulation, implementation and development (1990s–2010s) of each project in each of the 9 selected urban regions. On average, each interviewee answered questions on 3.1 large-scale urban development projects. Three cases were subdivided into two cases because a clear differentiation between specific implementation stages was identified by the interviewees (expansion of the Barcelona airport, cases “bcn_airport80-90” and “bcn_airport00-16”; realisation of Lyon Part-Dieu, cases “lyo_partdieu70-90” and “lyo_partdieu00-16”; MediaCityUK, cases “man_salfordquays80-00” and “man_mediacityuk00-16”). Therefore, from the initial 35 cases, the final number of analysed cases in the lsUDPs dataset is 38.

    3.2 The data set lsUDPs: Data management and operationalisation
Interviews were fully transcribed and analysed through MAXQDA (version 12.3, VERBI GmbH, Berlin, Germany), and intercoder agreement was evaluated on a sample of nine interviews. We also compiled “synthetic case descriptions” (SCD) for each case (totalling more than 160 SCDs) to spot potential inconsistencies among interviewees’ accounts and to facilitate completion of the “calibration table” for each case (see below). An online expert survey distributed to the interviewees (response rate 78%) helped systematise the information collected during the interviews. We also consulted both academic and gray literature on the case studies to check for possible ambiguity and inconsistencies in the interview data, and to solve discrepancies between our assigned set membership scores and questionnaire values. Site visits were also carried out to retrieve additional information on the selected cases.
For each case (i.e. each of the 38 selected large-scale urban development projects), we operationalised each condition (i.e. STATE, PRIVATE, EVENTS, TRANSP, RESIST) and the outcome (CONCOR) in terms of sets, for subsequent application of Qualitative Comparative Analysis. This process is called “calibration”; we used a number of indicators for each condition to qualitatively assess each large-scale project across the conditions. The case-based qualitative assessment was then transformed into fuzzy-set membership values. Fuzzy-set membership values range from 0 to 1, and should be conceived as “fundamentally interpretative tools” that “operationalize theoretical concepts in a way that enhances the dialogue between ideas and evidence” (Ragin 2000:162, in “Fuzzy-set Social Science”. Chicago: University Press). We employed a four-value fuzzy-set scale (0, 0.33, 0.67, 1) to “quantify” into set membership scores the individual histories of cases retrieved from interview data. Only the condition TRANSP was calibrated as a crisp-set (0, 1).
The translation of qualitative case-based information into numerical fuzzy-set membership values was iteratively performed by populating a calibration table following standard practices recently emerged in QCA when dealing with qualitative (interview) data.

europe

display_name: europe

land-use-change

display_name: land-use-change

land-use-transformations

display_name: land-use-transformations

large-scale-urban-development-projects

display_name: large-scale-urban-development-projects

spatial-planning

display_name: spatial-planning

strategic-projects

display_name: strategic-projects

strategic-spatial-planning

display_name: strategic-spatial-planning

urban-transformations

display_name: urban-transformations

en: lsUDPS Large-scale urban development projects in European urban regions


maintainer: Matthias Haeni

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

en: Site aerial photo (Isone)

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

mimetype: image/jpeg

en: Data plot of the forest station (ISB)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Data plot of the field station (ISF)

en: Site photo of the forest station (ISB)

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

en: Forest station data (ISB)

en: Data file.

en: Site photo of the field station (ISF)

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

en: Field station data (ISF)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Isone in Switzerland where one station is located within a natural broad-leaved forest stand (ISB) with European beech (_Fagus sylvatica_; 70-100 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, ISF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Isone is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


en: spatial-planning land-use-simulations land-use-change land-use-transformations digital-zoning-plans

en: Legend simulated maps

en:
Table of Content: 1. General context of the data set "LUSzoning”; 2. Background and aims of the study using the data set LUSzoning; 3. The data set LUSzoning.
1. General context of the data set "LUSzoning".
The data set "LUSzoning" stands for Land-use simulations integrating zoning regulations in Spanish functional urban areas. The data set has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2021. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, digital zoning plans) into quantitative land-change modelling approaches at the urban regional level.
2.	Background and aims of the study using the data set “LUSzoning”.
As part of the CONCUR project, a specific task was to integrate planning spatial policies in land-change modelling. Planning can be implemented in modelling using either hard or gradual restrictions. Different studies have addressed the inclusion of spatial planning policies in land-use change modelling. However, the integration of zoning constraints is generally established as hard or Boolean-based restrictions (e.g., whether urban development is allowed or not), while not accounting for the spatial heterogeneity or gradual characteristics within planning zones (e.g., whether planning regulations allow low, medium or high urban density), though these could improve real patterns simulations in urban areas. We assume Spanish General Zoning plans were suitable to explore the integration of planning into land-change modelling as soft constrains because they define land-use intensities in the buildable zoning areas. In light of the above considerations, the overall aim of the study was to model urban land-use changes using a multi-scenario approach that integrates digitized zoning plans for the Functional Urban Areas (FUAs) of Madrid, Barcelona, Valencia, and Zaragoza. The following specific objectives were addressed: i) to analyse the role of planning by defining three future scenarios that integrate digitized zoning plans and one scenario that assumes almost no planning intervention; ii) to introduce zoning constraints that reflect different degrees of urban densities; iii) to generate a transferable spatially-explicit modelling framework to integrate planning into land-use change simulations.
Four future land-use demands scenarios were defined for the FUAs. Storylines were created considering probable development scenarios related to zoning plans, current Spanish legislation and sustainability goals defined along two axes: a high market-oriented vs. high planning-intervention axis, and an axis of short-term economic growth vs. long-term sustainable growth. The sustainable development scenario (S1) is characterized by low gross floor area (GFA) growth that is limited to areas that are currently under development according to zoning plans. The business-as-usual scenario (S2) is characterized by medium GFA growth in the range of on-going trends. The strong development scenario (S3) is characterized by high GFA growth rates. Growth is restricted to buildable areas without urbanization project designated in zoning plans. The unrestricted development scenario (S4) prioritizes a high degree of market liberalization characterized by high GFA growth that surpasses population demands. S4 follows a rapid economic growth pattern with almost no planning intervention.
3.	 The data set “LUSzoning”.
The dataset includes 16 .asc raster layers providing the simulated land-uses under four defined scenarios for Barcelona, Madrid, Valencia and Zaragoza Functional Urban Areas (FUAs) for 2030. The simulated raster layers were created using CLUMondo simulation framework and have a spatial resolution of 30m. The .asc layers name include the name of the FUA and scenario number. For example, the output from simulating the urban growth for the city of Zaragoza under Scenario 2 is named “Zaragoza_S2.tif”. Furthermore, a .txt file named “Legend.txt” includes the numeric value of the land-use and the category of land-use that represents to interpret the .asc raster layers. The name of the land-use classes is a reclassification of the Urban Atlas 2012 land-use classes within the four Spanish FUAs analyzed.

digital-zoning-plans

display_name: digital-zoning-plans

land-use-simulations

display_name: land-use-simulations

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: LUSzoning: Land-use simulations integrating zoning regulations in Spanish functional urban areas


wind-lidar-davos-wolfgang

title_for_slug: wind-lidar-davos-wolfgang

en: profiles lidar wind wolfgangpass raclets

en: Wind LIDAR data Wolfgang

en: A Leosphere WidCube 100S was deployed at Davos Wolfang, measuring wind profiles as well as performing PPI and RHI scans. Time format is in UTC with highest time resolution of 5 seconds.

en: Scanning wind Lidar from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 200 m above ground to 8100 m. The time resolution is up to 5 seconds. The Lidar was measuring wind profiles but also performed plan position indicator (PPI) and range height indicator (RHI) scans.

lidar

display_name: lidar

profiles

display_name: profiles

raclets

display_name: raclets

wind

display_name: wind

wolfgangpass

display_name: wolfgangpass

en: Wind LIDAR Davos Wolfgang


en: microct intercomparison icecube snow specific-surface-area

en: Grain survey of snow particles

en: The file contains all measurements of the artificial particles for snow types A, B, C, D and E, including the artificial particle mass in the Petri dish.

en: SMP measurement files for snow types A, B, C and D.

en: TARTES simulations

en: Jupyter notebook with TARTES simulations.

en: IceCube and MicroCT data

en: The file contains all micro-CT and IC measurements for snow types A, B, C, D and E used in our study. All samples were processed (and stored) in a cold laboratory at -15°C.

en: artificial_snow_data set

en: The file contains all micro-CT and IC measurements of the artificial snow data set. The data set was originally produced to investigate the influence of black carbon on SSA measurements.

en: The file contains all data for the plot in figure 1 of the co-located density and SSA measurement at Summit, Greenland in May 2015, conducted by Schneebeli, Proksch, Schmid.

en: Statistics of samples

en: Influence of black carbon on the measurement of the specific surface of snow with the near infrared method - Philipp Baumann

en: These experiments investigate different possible errors by measuring the specific surface area (SSA) of snow with the near infrared method. The influence of black carbon on this measurement was disproved, the theory was therefore validated. It shows that sunlight has no influence on the measurement with the IceCube. Wind drift snow as thin layer on the sample can increase the measured value of the specific surface by a few percent. This could be part of the error, observed in former measurements at SLF. As reference for the measured SSA values with the near infrared method, computer tomography was used to verify the SSA values.

en: The specific surface area (SSA) of different snow types were measured with the IceCube instrument and the Scanco Medical microCT 40. In addition, the snow particles created during the preparation of IceCube samples were counted. The difference in SSA between these instruments is explained by the formation of the surface particles. A numerical simulation using TARTES simulation support the observations.

icecube

display_name: icecube

intercomparison

display_name: intercomparison

microct

display_name: microct

snow

display_name: snow

specific-surface-area

display_name: specific-surface-area


maintainer: Frank Hagedorn

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

en: Gao_Frank_C13 pulse labeling.xlsx

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

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

biogeochemical-cycling

display_name: biogeochemical-cycling

drought

display_name: drought

forest

display_name: forest

microbial-biomass

display_name: microbial-biomass

plfa

display_name: plfa

educ

en: Education, culture and sport

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: Gao_Drought resistance and resilience of rhizosphere communities_13C-PLFA


maintainer: Anna Braun

en: fem metamorphism snow microstructure numerical-simulations

author: EnviDat

en: Simulation parameters and outputs for "Towards a physically and microstructure-based equation for the evolution of the specific surface area in snow"

format: ODS

en:
In the associated study [1], evolution of randomly generated bi-continuous microstructures that resemble real snow microstructures has been investigated. The control of the ambient conditions (temperature and temperature gradient) and of the physical processes at play on the rate of change of the specific surface area have been evaluated. Based on the evaluation, a new physically-informed governing law for the coarsening of microstructures has been proposed. This law accurately predicts the simulation outputs under various conditions. Furthermore, a potential parametrization of newly introduced material properties has been proposed and evaluated on snow coarsening data.
This dataset contains the parameters used for the mesh generation and the finite-element simulations. The output parameters of simulations, such as the ice fraction, surface of the ice-air interface per total volume, mean curvature and growth rate are provided.
[1]: A.Braun, K. Fourteau, S. Frei, M. Lehning, H. Löwe: Towards a physically and microstructure-based equation for the evolution of the specific surface area in snow, Journal of Glaciology, accepted for publication in 2024

fem

display_name: fem

metamorphism

display_name: metamorphism

microstructure

display_name: microstructure

numerical-simulations

display_name: numerical-simulations

en: Simulation parameters and outputs for towards a physically and microstructure-based equation for the evolution of the specific surface area in snow


tree-ring-laser-ablation-data

maintainer: Paula Ballikaya

title_for_slug: tree-ring-laser-ablation-data

en: dendrochemistry tree-rings laser-ablation dendrochronology air-pollution

en: Laser ablation data

en:
This dataset contains the values of several chemical elements (Mg, Al, Si, S, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Tl, Pb, Bi) measured in the latewood of tree rings of Mongolian oak from Harbin, China, at a 5-year resolution. Due to the lack of a suitable reference material for wood, absolute concentration was not calculated, and the ratio between the chemical element and 13C was taken as proxy for the element signal.

In Harbin, one of the largest cities and most important industrial centers in northeastern China, air quality monitoring systems were built only by the end of 2015 to meet the national requirements. Thus, dendrochemical analyses could be used as a tool to complement for the lack of air quality data over longer periods of time, allowing for the reconstruction of the temporal trend of trace metals. Our main scopes were to: (a) assess the chemical composition of Quercus mongolica Fisch. ex Ledeb. tree rings from Harbin using a recently developed system of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), (b) identify the main chemical elements which
derived from air pollution and may be used as indicators over the period 1965–2020 in Harbin, while excluding those that were controlled by physiological processes in the tree, and (c) reconstruct the history of pollution in Harbin by comparing the tree-ring chemical composition of recent decades with that of previous decades, in trees growing in the highly polluted city of Harbin and in trees growing in a control site 90 km away from major pollution sources.

Briefly, the temporal trend of some elements was influenced by physiological factors, by environmental factors such as pollution, or influenced by both. Mg, K, Zn, Cu, Ni, Pb, As, Sr and Tl showed changes in pollution levels over time.

air-pollution

display_name: air-pollution

dendrochemistry

display_name: dendrochemistry

dendrochronology

display_name: dendrochronology

laser-ablation

display_name: laser-ablation

tree-rings

display_name: tree-rings

en: Tree-ring laser ablation data


maintainer: Nander Wever

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

en: List of SnowMicroPen measurements

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

en: SnowMicroPen pts files and processed txt files

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

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

en: Manual snow pits and other snow density measurements

en: Documentation SnowMicroPen measurements

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

en: Jupyter notebook for processing the SnowMicroPen files.

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

antarctica

display_name: antarctica

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen

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


maintainer: Alexandre Buttler

en: biogeochemisty climate-change silvopastoral-systems transplantation-experiment soil

en: Dataset

en: Zip archive of all datasets detailed in study.

en:
Silvopastoral systems are highly productive and combine long-term wood production with annual provision of forage for livestock. In the Swiss Jura Mountains these systems are a key component of the landscape. As in other cold biomes, climate change can potentially accelerate landscape change within these historically sustainable systems.

In order to anticipate the evolution of subalpine wooded pasture ecosystems under future climate and land-use changes, this project focused on the interplay between soil, vegetation and climate. It was aimed at providing experimental evidence for chief ecosystem processes, with emphasis on the quality of the ecosystem services provided. The main interest was placed on vegetation turf resistance to climate change along an unwooded – sparsely wooded - densely wooded pasture gradient (land-use intensity), where plant productivity, diversity and succession along with rates of carbon cycling and microbial activity provided measures of ecosystem functioning at both plot and landscape level.

Experimental transplantation of monolith soil turfs to lower altitudes allowed to simulate soil warming and reduced annual precipitation. In order to simulate a year-round warmer and drier climate the natural climate variation along an altitudinal gradient was used as a proxy. The aim was to simulate realistic climate change scenarios for the second half of the 21st century predicted by the IPCC report and downscaled for Switzerland providing regionalized interpolated projections integrating therein trends for temperature increase and precipitation decrease. By using permanent meteorological stations within the network of the Federal Office of Meteorology and Climatology (MeteoSwiss), we obtained high resolution regional data on the variation of mean annual temperature (MAT) and mean annual precipitation (MAP) in relation to altitude in the Swiss Jura Mountains. We observed a general increase of +0.5 K in MAT and a decrease of -20 % MAP for each 100 m decrease in altitude along the SE slope of the Swiss Jura Mountains. These relationships served for the selection of the transplantation sites such that in comparison to a control site at 1350 m a.s.l. (Combe des Amburnex, N 46°54’, E 6°23’) a +2 K MAT and -20 % MAP was achieved at 1010 m a.s.l. (Saint-George, N 46°52’, E 6°26’), a +4 K MAT and -40 % MAP at 570 m a.s.l., (Arboretum d’Aubonne, N 46°51’, E 6°37’), and a +5 K MAT and -50 % MAP at 395 m a.s.l. (Les Bois Chamblard, N 46°47’, E 6°41’). The two stations at 1010 m a.s.l. and 570 m a.s.l. corresponded to the IPCC scenario A1B for a moderate increase in greenhouse gas emissions and to scenario A2 for a high increase in greenhouse gas emissions, respectively. The station at 395 m a.s.l. was chosen to represent an extreme scenario with climate variables lying at the positive tail distribution of model predictions under the A2 scenario.

Soil cores were assembled into rectangular PVC boxes of 60  80 cm2 size and of 35 cm height. All mesocosms were dug down to surface level into previously prepared trenches in the ground thus preventing lateral heat exchange with the atmosphere. Since at each site the mesocosms were placed in a common garden with no light interception, mesocosms with turfs from the two wooded pastures were shaded from direct sun light to simulate the natural light conditions in the corresponding habitats. Each mesocosm was equipped with a drainage system and was connected to a water tank thus representing a zero potential lysimeter collecting soil solution and precipitation/snowmelt runoff. ECH2O EC-TM sensor probes coupled to Em50 data-loggers (Decagon Devices, Inc., USA) recorded soil temperature and volumetric water content in each mesocosm at the top-soil (0 to -3 cm) every minute and data were averaged over one hour intervals. Climate parameters at each transplantation site were monitored continuously throughout the experiment by means of automated weather stations (Sensor Scope Sàrl, Switzerland), measuring rain precipitation (non-heated tipping bucket gauges) and air temperature and humidity 2 m above the ground surface at one minute intervals.

A list of above- and belowground variables were measured to assess the resilience of biogeochemical processes, plant productivity, tree regeneration, and carbon sequestration for each respective land-use practice. Furthermore, the experimental data were used to improve on (parameterization) the existing spatially explicit, dynamic model WoodPaM and refine the modelʼs climatic and land-use variables so that different scenarios of climate change and land use change could be simulated. Natural and management induced disturbance patterns were incorporated into the model.
The data have been made available within the project CCES Mounted. The climate stations Sensorscope are still in use within the project CLIMARBRE (Wald und Klimawandel, WSL/BAFU).

#References

1. Puissant, J., Cécillon, L., Mills, R.T.E., Robroek, B.J.M. Gavazov, K., De Danieli, S., Spiegelberger, T., Buttler, A., Brun, J.J. 2015. Seasonal influence of climate manipulation on microbial community structure and function in mountain soils. Soil Biology and Biochemistry 80: 296–305.
2. Mills, R., K. Gavazov, T. Spiegelberger, D. Johnson and A. Buttler 2014. Diminished soil functions occur under simulated climate change in a sup-alpine pasture, but heterotrophic temperature sensitivity indicates microbial resilience. Science of the Total Environment, vol. 473–474(0): 465-472.
3. Gavazov, K., Spiegelberger, T. and Buttler, A. 2014. Transplantation of subalpine wood-pasture turfs along a natural climatic gradient reveals lower resistance of unwooded pastures to climate change compared to wooded ones. Oecologia (174) : 1425-1435.
4. Peringer A., Siehoff S., Chételat J., Spiegelberger T., Buttler A. & Gillet F. 2013. Past and future landscape dynamics in pasture-woodlands of the Swiss Jura Mountains under climate change. Ecology and Society, 18, 3: 11. DOI: 10.5751/ES-05600-180311. [online] URL: http://www.ecologyandsociety.org/vol18/iss3/art11/
5. Gavazov, K. S., A. Peringer, A. Buttler, F. Gillet and T. Spiegelberger. 2013. Dynamics of Forage Production in Pasture-woodlands of the Swiss Jura Mountains under Projected Climate Change Scenarios. Ecology and Society 18 (1): 38. [online] URL: http://www.ecologyandsociety.org/vol18/iss1/art38/

biogeochemisty

display_name: biogeochemisty

climate-change

display_name: climate-change

silvopastoral-systems

display_name: silvopastoral-systems

soil

display_name: soil

transplantation-experiment

display_name: transplantation-experiment

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: Biogeochemical data from a transplantation experiment of monolith soil turfs along an altitudinal gradient to simulate climate change scenarios


latent-heat-flux-over-snow

maintainer: Sergi González-Herrero

title_for_slug: latent-heat-flux-over-snow

en: meteo snow swiss-alps central-asia meteo-station meta

en:
This file includes the Jupyter notebooks to reproduce the method and the figures of the paper. This consists in:
- 3 files for Alpine Valley
- 3 files for Antarctic Plateau
- 1 file for application at Pamir mountains

en:
This file includes the datasets used to compare the methods. It includes:
- EC Postprocessed heat flux data from sonic anemometer and gas analyzer. Files named: [SITE]_(NO)WatVap_eddypro_OUT_[type].csv
- MOST Postprocessed data from SNOWPACK model. Files named [SITE]_[year]_[METHOD].smet
- Configuration files for SNOWPACK (.ini files) and Eddypro (.eddypro files)

[SITE] correspond to DISHMA/DISH or Alpine Valley, PLATEAU/PLAT for Antarctic Plateau, TAJ for Pamir mountains (no EC)
[type] correspond to fluxnet, full_output or qc_details
[METHOD] indicates the stability model used and/or the roughness length

en:
This dataset contains three case studies where are compared the latent heat flux calculated using the Eddy Covariance (EC) method, the Monin-Obukhov Similarity Theory (MOST) and the modified Bowen-ratio method presented in the research paper "Using the sensible heat flux eddy covariance based exchange coefficient to calculate latent heat from moisture mean gradients overn snow". We include the datasets, the code and the ini files for reproduce the research.
The dataset contain two files in zip format:
 1. Sowftware-Heat_fluxes_on_snow: Jupyter notebooks to reproduce the method and the figures of the paper.
 2. data.zip: Datasets used to compare the methods

central-asia

display_name: central-asia

meta

display_name: meta

meteo

display_name: meteo

meteo-station

display_name: meteo-station

snow

display_name: snow

swiss-alps

display_name: swiss-alps

en: Latent Heat Flux over Snow


en: senegal fish teleo metabarcoding edna

en: Data access

en: Please use cyberduck to download for the moment

en:
Monitoring fish diversity through eDNA metabarcoding along the Casamance river
In 2018, water samples were collected in 13 locations along the Casamance in Senegal. Samples were taken either from a boat or by filtering from the side of the river. At each station, two filtration replicates were performed using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 30 min, during which approximately 30 liters of water were filtered through each filtration capsule. After filtration, the water inside the capsules was removed, and the capsules were filled with 50 ml of conservation buffer for preservation at room temperature. We followed strict contamination control protocols throughout both the fieldwork and laboratory processes, adhering to the guidelines of Valentini et al. (2016). To prevent contamination, each sample was processed using disposable gloves and single-use filtration equipment. We use primers for eukaryote and fish. The MiSeq Reagent Kit v3 (2x75 bp) (Illumina, San Diego, CA, USA) was used for paired-end sequencing at a theoretical sequencing depth of 200,000 reads per sample.
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

edna

display_name: edna

fish

display_name: fish

metabarcoding

display_name: metabarcoding

senegal

display_name: senegal

teleo

display_name: teleo

en: Environmental DNA Freshwater Senegal Casamance 2018