maintainer: Daniel Scherrer

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

en: TreeMig Simulation Environment and Input Data

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

en: Description input data

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

en: TreeMig Tutorial

en: Tutorial for using the TreeMig framework; updated

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

bioclimatic-variables

display_name: bioclimatic-variables

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-model

display_name: forest-model

gui

display_name: gui

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-interaction

display_name: species-interaction

species-migration

display_name: species-migration

treemig

display_name: treemig

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


maintainer: EnviDat Support

en: groundwater studibach switzerland time-series alptal

state: active

type: dataset

media_type: text/plain

en:
Groundwater time series of the monitoring network in the Studibach (C7) catchment, Alptal, Switzerland published in
Rinderer M., van Meerveld I, McGlynn B. (2019): From points to patterns – Assessing runoff source area dynamics and hydrological connectivity using time series clustering. Water Resources Research, doi: 2018WR023886R

format: TXT

mimetype: text/plain

en: Groundwater time series between 2010 and 2014 of the distributed monitoring system in the Studibach (C7), Alptal, Switzerland. Data published in Rinderer M., van Meerveld I, McGlynn B. (2019): From points to patterns – Assessing runoff source area dynamics and hydrological connectivity using time series clustering. Water Resources Research, doi: 2018WR023886R

alptal

display_name: alptal

groundwater

display_name: groundwater

studibach

display_name: studibach

switzerland

display_name: switzerland

time-series

display_name: time-series

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Groundwater time series Studibach (Rinderer et al., 2019, WRR)


en: number-of-caught-persons avalanche-accidents avalanche-fatalities number-of-dead-persons avalanche-accident-statistics

en: Fatal avalanche accidents in Switzerland since 1995-1996

media_type: text/csv

format: CSV

mimetype: text/csv

en:
Attention: this data is not updated after 2022 anymore.
This data collection contains information concerning all accidents by snow avalanches causing at least one fatality in Switzerland. The data set commences on 01/10/1995. After the completion of a hydrological year, the new data is added.
The following information is provided:
 * avalanche identifier
 * date of the accident
 * accuracy of the date in range of days before and after
 * canton
 * name of the locality
 * start zone of the avalanche
  * coordinates (Swiss coordinate system, approximately in middle of start zone)
  * accuracy of the coordinates in meters
 * elevation (in meteres above sea level, app. in middle of start zone)
 * slope aspect (main orientation of start zone)
 * slope inclination (in degree, steepest point within start zone)
 * number of dead persons
 * number of caught persons
 * number of fully buried persons
 * forecasted avalanche danger level
 * activity/location of the accident party at the time of the incident

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-accidents

display_name: avalanche-accidents

avalanche-fatalities

display_name: avalanche-fatalities

number-of-caught-persons

display_name: number-of-caught-persons

number-of-dead-persons

display_name: number-of-dead-persons


en: compression-experiments snow microstructure snow-mechanics x-ray-tomography

en: README

en: README file explaining the organization of the data

en: Snowmicroquakes Data

en: Compression experiments, X-ray images and simulation data with supplemented README file

format: SERVICE

en:
When snow is compressed with a certain speed, micro-snowquakes are triggered in the porous structure of bonded crystals. The present dataset covers uniaxial compression experiments of snow at different strain rates and concurrent X-ray tomography imaging documenting this feature.

The experiments were conducted in a micro-compression stage operated in the X-ray tomography scanner in the SLF cold laboratory. The dataset comprises the compression force data of 17 compression experiments, the 3D image data from 4 X-ray tomography scans and the results of numerical simulations.

compression-experiments

display_name: compression-experiments

microstructure

display_name: microstructure

snow

display_name: snow

snow-mechanics

display_name: snow-mechanics

x-ray-tomography

display_name: x-ray-tomography

en: Compressive stick slip and snow-micro-quakes


en: chondrichthyes teleo metabarcoding bay-biscay edna actinopterygii fish-composition atlantic-region

en: Data access

en: Please use cyberduck to download for the moment

en:
Environmental DNA complements scientific trawling in surveys of marine fish biodiversity (Dataset 2021)
At the end of October and the beginning of November 2021, we selected 18 sites from the 2021 EVHOE survey for eDNA sampling. The French international EVHOE bottom trawl survey is conducted annually in autumn in the Bay of Biscay (BoB) to monitor demersal fish resources. All selected sites were located on the continental shelf at depths ranging from 30 to 230 m. At each site, we sampled seawater using an underwater pump equipped with two peristaltic heads (subspace), mounted on Ifremer’s Pagure sledge. We collected approximately 30 liters of water directly from the seabed at a flow rate of 1.0 L min⁻¹. For filtration, we used VigiDNA 0.20 μm filtration capsules (SPYGEN, Le Bourget-du-Lac, France) along with disposable sterile tubing. Each filtration device processed water along a transect, representing two replicates per sampling site. To prevent contamination during fieldwork, we adhered to strict protocols, including the use of disposable gloves and single-use filtration equipment. At the end of each filtration, we emptied the remaining water from the capsule and replaced it with 80 mL of CL1 conservation buffer before storing the samples at room temperature. We processed the eDNA capsules at SPYGEN, following the protocol outlined by Polanco-Fernández et al. (2020). Library preparation and sequencing were performed at Fasteris (Geneva, Switzerland). Specifically, we prepared four libraries using the MetaFast protocol (a ligation-based method) and sequenced them separately. Paired-end sequencing was conducted using a MiSeq sequencer (2 × 125 bp, Illumina, San Diego, CA, USA) on two MiSeq Flow Cell Kits (v3; Illumina), following the manufacturer’s instructions. We analyzed the sequence reads using the OBITools package (http://metabarcoding.org/obitools; Boyer et al., 2016), following the protocol described by Valentini et al. (2016).
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)

actinopterygii

display_name: actinopterygii

atlantic-region

display_name: atlantic-region

bay-biscay

display_name: bay-biscay

chondrichthyes

display_name: chondrichthyes

edna

display_name: edna

fish-composition

display_name: fish-composition

metabarcoding

display_name: metabarcoding

teleo

display_name: teleo

en: Environmental DNA Marine France Evhoe 2021


maintainer: Lyu

en: species-distribution-model species-richness fagales species-range-map pinales species-occurrence

en: Species range maps of species in Fagales and Pinales (with enough occurrences)

media_type: application/zip

en: species_map.zip includes species 1690 range maps generated based on the integrated method described in Lyu et al., (2022). File names with (polygon) indicate that this map is only generated by polygon mapping, as there are not enough occurrences for SDMs.

format: ZIP

en: Occurrence maps for species of Fagales and Pinales with few records

en: Point_buffer.zip includes 267 occurrence maps of species that do not have enough occurrence data for polygon mapping.

en:
This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022).
To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details).

fagales

display_name: fagales

pinales

display_name: pinales

species-distribution-model

display_name: species-distribution-model

species-occurrence

display_name: species-occurrence

species-range-map

display_name: species-range-map

species-richness

display_name: species-richness

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: Species distribution maps of Fagales and Pinales (GDPlants)


mountain-permafrost-hydrology

maintainer: Jeannette Noetzli

title_for_slug: mountain-permafrost-hydrology

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

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

media_type: application/pdf

format: PDF

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

climate-change-impacts

display_name: climate-change-impacts

ice-rich-permafrost

display_name: ice-rich-permafrost

mass-movements

display_name: mass-movements

monitoring-techniques

display_name: monitoring-techniques

mountain-permafrost-hydrology

display_name: mountain-permafrost-hydrology

permafrost-degradation

display_name: permafrost-degradation

en: Mountain Permafrost Hydrology


en: snow-modelling snowpack avalanche source-code snowdrift

en:
This dataset includes the FORTRAN code of the FSM2trans model version used in the paper: Quéno et al., Snow redistribution in an intermediate-complexity snow hydrology modelling framework, The Cryosphere, 2024.
The FSM2trans code is derived from the FSM2 model (Essery et al., 2015) and its variant FSM2oshd (Mott et al., 2023). In this model, we implemented and adapted the snowdrift model SnowTran-3D (Liston et al., 2007) and the avalanching model SnowSlide (Bernhardt and Schulz, 2010).
This dataset does not include the processing scripts to prepare meteorological input data for the model, provided as bin files.

avalanche

display_name: avalanche

snow-modelling

display_name: snow-modelling

snowdrift

display_name: snowdrift

snowpack

display_name: snowpack

source-code

display_name: source-code

en: FSM2trans code


bryophytes-of-europe-traits-bet-dataset

title_for_slug: bryophytes-of-europe-traits-bet-dataset

en: iucn mosses indicator-values liverworts red-list hornworts life-history-traits bioclimatic-variables

en: PDF including the (trait) explanations.

en: Excel file including the BET dataset and (trait) explanations.

format: XLS

en: Text file including the BET dataset.

en: The Bryophytes of Europe Traits (BET) dataset includes values for 65 biological and ecological traits and 25 bioclimatic variables for all 1816 bryophytes included in the European Red List (Hodgetts et al. 2019). The traits are compiled from several regional trait datasets and manually complemented using Floras, species-specific literature and expert knowledge. The bioclimatic variables are calculated using the European range of each species. Details regarding the trait compilation and extraction of bioclimatic variables can be found in the corresponding data paper (Van Zuijlen et al. 2023).

bioclimatic-variables

display_name: bioclimatic-variables

hornworts

display_name: hornworts

indicator-values

display_name: indicator-values

iucn

display_name: iucn

life-history-traits

display_name: life-history-traits

liverworts

display_name: liverworts

mosses

display_name: mosses

red-list

display_name: red-list


maintainer: Anita C Risch

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

en: Risch et al. GEB_data.xlsx

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

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

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

Paper Citation:

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

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

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

anthropogenic-management

display_name: anthropogenic-management

climate

display_name: climate

herbivore-exclusion

display_name: herbivore-exclusion

microbial-biomass-carbon

display_name: microbial-biomass-carbon

microbial-respiration

display_name: microbial-respiration

nutrient-addition

display_name: nutrient-addition

soil-properties

display_name: soil-properties


maintainer: Jana von Freyberg

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

en: Data Set Documentation: alptal_Isotopes_daily_2015-2019_v2

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

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

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

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

alptal

display_name: alptal

catchment

display_name: catchment

hydrology

display_name: hydrology

precipitation

display_name: precipitation

stable-water-isotope

display_name: stable-water-isotope

stream-water

display_name: stream-water

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


maintainer: Rebecca Mott

en: eddy-covariance-measurements turbulent-sensible-heat-fluxes dischmex snow-patch patchy-snow-covers

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 3.2 m on a daily basis.

mimetype: application/zip

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.6 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.5 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.2 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.3 m.

en: Gletschboden setup

media_type: image/png

en: Background map based on data © 2014/2016 swisstopo (JD100042)

format: PNG

mimetype: image/png

en: Output explanation

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 1.7 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (DA-600, Kaijo-Denki) in 0.3 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 0.7 m.

en:
This dataset contains eddy-covariance measurements in the ablation period of 2014-2016. Measurements were taken from two turbulence towers over a long-lasting snow patch, which are 5 m apart from each other (2014 and 2015). The turbulence towers were equipped with two YOUNG ultrasonic anemometers mounted 0.7 m (in 2014) and 3.3 m (in 2015) above snow-free ground, two ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) mounted at 2.6 m (in 2014) and 2.2 m (in 2015) above snow-free ground and one anemometer (DA-600, Kaijo Denki) mounted at 0.3 m above snow surface.

The measurement setup changed in 2016 and includes a measurement above the snow-free ground in upwind direction (Swiss coordinates: 790191/176689). The measurement tower is equipped with one ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 3.3 m above the snow-free ground. Additionally, one measurement tower is installed above the long-Lasting snow patch and equipped with the same setup as 2015.

Turbulence data were sampled at a frequency of 20 Hz. The processing of the data to quality controlled fluxes has been done with the Biomicrometeorology flux software (Thomas et al., 2009). The program applies plausibility tests and a despiking test after Vickers and Mahrt (1997) on the measured data. The routine further applies a time-lag correction and considers the deployment (e.g. the sonic azimuth). A frequency response correction (Moore, 1986) is done and a three-dimensional rotation is performed. Finally, quality assurance/quality control (QA/QC) flags after Foken et al., (2004) are issued and fast Fourier transform power and co-spectra are calculated. The change in snow height is considered in the post-processing for every measurement day. The turbulence data were averaged to 30 minute intervals.

dischmex

display_name: dischmex

eddy-covariance-measurements

display_name: eddy-covariance-measurements

patchy-snow-covers

display_name: patchy-snow-covers

snow-patch

display_name: snow-patch

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

en: DISCHMEX - Impact of extreme land-surface heterogeneity on micrometeorology over spring snow-cover


maintainer: Paraskevi Georgakaki

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

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: 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:
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

wrf

display_name: wrf

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


radiosondes

maintainer: Jörg Wieder

title_for_slug: radiosondes

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

en: Radiosond Template Files

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

en: Radiosond Sounding Files

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

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

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

wind

display_name: wind

wind-direction

display_name: wind-direction

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: Radiosondes


en: solutes groundwater catchment oxygen-isotopes erlenbach hydrogen-isotopes stream-water catchment-data precipitation

en: This dataset includes mean concentrations of solutes (Calcium, Iron, Magnesium, Sodium, Potassium, Silicon, Nitrate, Sulfate, and Chloride) and stable isotope values (hydrogen and oxygen) in water molecules. These were measured in precipitation, groundwater, soil water, and surface water samples collected from the Erlenbach catchment and its close vicinity in Switzerland during Spring and Fall 2021. Detailed information is available in the accompanying documentation (documentation_for_the_hydrochemical_data_collected_during_spring_and_fall_2021_in_the_erlenbach_catchment.pdf).

en: This document provides a detailed description of the hydrochemical dataset, including, among other things, an overview of the dataset content, an explanation of the column headers, a description of the locations where samples were collected, and an explanation of the abbreviations used in the dataset.

en: The dataset contains hydrochemical data collected from spring to fall 2021 in the Erlenbach catchment, Switzerland. This hydrochemical data includes solute concentrations and stable isotopes of oxygen and hydrogen in water molecules, measured in streamwater, soil water, groundwater, and rainfall samples. Samples were collected during seven rainfall events with higher temporal resolution (hourly) as well as at lower resolution between events. A detailed description of the dataset is provided in the documentation.

catchment

display_name: catchment

catchment-data

display_name: catchment-data

erlenbach

display_name: erlenbach

groundwater

display_name: groundwater

hydrogen-isotopes

display_name: hydrogen-isotopes

oxygen-isotopes

display_name: oxygen-isotopes

precipitation

display_name: precipitation

solutes

display_name: solutes

stream-water

display_name: stream-water

en: Hydrochemical Data Collected during Spring-Fall 2021 in the Erlenbach Catchment


latent-heat-flux-over-snow

maintainer: Sergi González-Herrero

title_for_slug: latent-heat-flux-over-snow

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

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


maintainer: Esther Frei

en: stillberg treeline-ecosystems mountain-ecology literature-review long-term-experiment

en: The Stillberg bibliography (Stillberg_bibliography_v1.zip) comprises a comprehensive list of 276 scientific publications, 91 of them published in peer-reviewed ISI journals (Stillberg_bibliography_data_v1.csv) along with the metadata file (Stillberg_bibliography_metadata_v1.csv). Currently the bibliography comprises literature about the main afforestation experiment, the FACE × warming experiment, the nutrient addition experiment, and the G-TREE experiment, as well as further publications related to the Stillberg research site that have been published until August 2023. The bibliography can be filtered for different categories, e.g., experiment, peer-review, source repository or database, and source title.

en:
 Background information

The Stillberg ecological treeline research site in the Swiss Alps was established in 1975, with the aim to develop ecologically, technically, and economically sustainable reforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Over almost fifty years, research at the Stillberg site combined long-term monitoring of the large-scale high-elevation afforestation with experimental manipulations simulating global change impacts. Besides providing a scientific basis and practical guidelines for high-elevation afforestation, this research has contributed to a comprehensive understanding of ecological processes in the treeline ecotone across different compartments and scales, from individual trees, non-tree vegetation and soils to whole ecosystems, in the context of global change resulting in more than 150 publications.

 Dataset generation

We compiled a comprehensive list of scientific publications covering research at the Stillberg research site by conducting searches in the literature databases Web of Science and Google Scholar, as well as in the Digital Object Repository of the Swiss Federal Institute for Forest, Snow and Landscape Research WSL (DORA). We compiled all publications about the afforestation experiment, the FACE × warming experiment, the nutrient addition experiment, the G-TREE experiment, as well as other studies related to the Stillberg research site.

 Data description

The Stillberg bibliography (Stillberg_bibliography_data_v1.csv) comprises a comprehensive list of 276 scientific publications, 91 of them published in peer-reviewed ISI journals. Currently the bibliography comprises literature about the main afforestation experiment, the FACE × warming experiment, the nutrient addition experiment, and the G-TREE experiment, as well as further publications related to the Stillberg research site that have been published until August 2023. The bibliography can be filtered for different categories, e.g., experiment, peer-review, source repository or database, and source title. The bibliography is described in a metadata file (Stillberg_bibliography_metadata_v1.csv). The bibliography along with the metadata file are provided in a ZIP-folder (Stillberg_bibliography_v1.zip).

literature-review

display_name: literature-review

long-term-experiment

display_name: long-term-experiment

mountain-ecology

display_name: mountain-ecology

stillberg

display_name: stillberg

treeline-ecosystems

display_name: treeline-ecosystems

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Bibliography of the long-term treeline research site Stillberg, Switzerland


en: switzerland sentinel-2 force raster-processing remote-sensing

media_type: image/tiff

en: An example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The date of the image is 24.07.2018.

format: TIFF

en: Parameter file that defined all the parameters for processing the Sentinel-2 Level 2 imagery

format: OCTET

en:
We processed Sentinel-2 images from 2017 to 2023 for Switzerland with the Software FORCE (Frantz 2019). The respective parameter files can be found here: [Github](https://github.com/TLKoch/Sentinel-2_CH). All the images consist of several TB and therefore, access will be granted upon request.
The available bands (in spatial reference system EPSG 3035) are the following:
*Red, Green, Blue, NIR, Red-Edge-1, Red-Edge-2, Red-Edge-3, SWIR-1, SWIR-2*
The available indices (in spatial reference system EPSG 3035) are the following:
*CCI, CIRE, NDWI/NDMI, NDVI, EVI*
Further indices might be provided upon request.

 Processing
After downloading Level 1 data for the 14 Sentinel-2 tiles covering Switzerland (T31TGN, T32TLT, T32TMT, T32UMU, T32TNT, T32TPT, T31TGM, T32TLS, T32TMS, T32TNS, T32TPS, T32TLR, T32TMR, T32TNR) with an estimated cloud cover of less than 80 \%, we processed the data further with FORCE v. 3.7.8-12.
FORCE was used for radiometric correction, including atmospheric and topographic corrections using the digital elevation model from Copernicus Land Monitoring Service (EU-DEM v. 1.1), with a 25 m spatial resolution, a bidirectional reflectance distribution function (BRDF) correction, and an adjacency effect correction. For cloud masking, the improved Fmask algorithm with buffers of 300 m for clouds, 90 m for shadows, and 30 m for snow were used (Frantz et al. 2018; Zhu et al. 2015). All bands were resampled using cubic convolution to 10 m spatial resolution. Shadows, low and poor illumination as well as saturation were masked out. The co-registration of all images was made with monthly composites of near-infrared Landsat Collection 2 images from 2014 to 2021 (Rengarajan et al. 2020;Rufin et al. 2021). With this the geometric consistency improved. The processed images are available in tiles of 30 by 30 km.

 Example images
Uploaded is an example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The date of the image is 24.07.2018.

force

display_name: force

raster-processing

display_name: raster-processing

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

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: Sentinel-2 imagery of Switzerland


en: methoxy-groups common-garden carbohydrates isotopes tree-species

en: The resource contains a metadata sheet explaning the variables, as well as a sheet with the actual species and isotope data.

en: This dataset contains hydrogen and carbon isotope data from 73 tree and shrub species grown in a common garden (Kannenfeldpark) in Basel, Switzerland. Leaf and twig samples were collected in August 2019. The isotope measurements cover various organic compounds, including sugars, cellulose, and (lignin) methoxy groups, as well as leaf and twig xylem water. Details on sample preparation and isotope analysis are provided in Schuler et al. (2023) for carbohydrates and water, and in Wieland et al. (in review) for methoxy groups.

carbohydrates

display_name: carbohydrates

common-garden

display_name: common-garden

isotopes

display_name: isotopes

methoxy-groups

display_name: methoxy-groups

tree-species

display_name: tree-species

en: Stable isotope data from 73 tree and shrub species in a common garden in Basel Switzerland


en: land-use spatial-modelling bioclimatic-variables forest-dynamics forest-management forest treemig example-applications r-package species-migration climate-change

en: A condensed instruction for running the presented simulations.

en:
1) Simulation environment TMEnvironmentExampleApplications for the simulation of the case study applications. It contains a) simulation settings (folder C), b) species parameters (folder P), c) folders S, R and P for the state files and results and plots to be produced, and the binaries of the TreeMig model.
2) Input data for Switzerland in TMInputData (Climate, soil, land cover, DEM)
3) Several R scripts (folder Scripts) to run and plot the simulations directly from R without having to use the graphical user interface.
For learning how to use TreeMig we recommend the starter kit https://www.doi.org/10.16904/envidat.533.

en: R-script to install the TreeMig framework and start the graphical user interface

en: Where the environmental and land use input data come from and how they were processed

en:
The TreeMig framework allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions.
Here, case study examples in Switzerland are given that simulate the dynamics and spatial spread of competing tree species in a region in Switzerland, the invasion of a hypothetical invasive tree species, and the control of this species via a management model coupled to TreeMig. The datasets consist of the installation- and GUI starting-script, example environmental input data for Switzerland, the simulation environment, and further R-scripts for running the simulations and plotting the simulation results directly from R and for coupling TreeMig to the forest management model.

bioclimatic-variables

display_name: bioclimatic-variables

climate-change

display_name: climate-change

example-applications

display_name: example-applications

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-management

display_name: forest-management

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-migration

display_name: species-migration

treemig

display_name: treemig

en: Case study applications demonstrating the use and potential of the TreeMig framework v1


en: diagnostic-species character-species vegetation-classification typoch phytosociology vegetation-type

en: How the TypoCH list was modified.pdf

en: Describes details on the modification procedure to improve the TypoCH reference list.

en: TypoCH_modified list.csv

en:
The file contains the vegetation types and the modified list of diagnostic species (based on Delarze et al. (2015). The numbers indicate the weight of a species in the particular vegetation type for the calculation of the TypoCH score:
1) accompanying species -> 1 point
2) dominant accompanying species (if >10% cover in the plot) -> 2 points
3) character species -> 4 points
4) dominant character species (if >10% cover in the plot) -> 8 points

en: The TypoCH scoring system (Eggenberg & Bornand 2023), which is widely used in Switzerland, provides good results as classification method for vegetation types based on Delarze et al. (2015). However, the reference list contained species with no diagnostic value and important character species were missing. We therefore made a first attempt to modify the list based on numerical methods and expert knowledge to improve the classification success, at least for vegetation types of the open land (i.e. grasslands, fens, bogs).

character-species

display_name: character-species

diagnostic-species

display_name: diagnostic-species

phytosociology

display_name: phytosociology

typoch

display_name: typoch

vegetation-classification

display_name: vegetation-classification

vegetation-type

display_name: vegetation-type

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Modified TypoCH reference list of diagnostic species for the classification of vegetation types in Switzerland


aerosol-data-weissfluhjoch

title_for_slug: aerosol-data-weissfluhjoch

en: raclets aerosol linda ice-nucleation particles inp

en: LINDA Dataset Weissfluhjoch

en: The LINDA file contains information on temperature (in °C) dependent frozen fractions, number of Ice Nuleating Particles (INP, per standard liter) and differential INP concentrations (per standard liter and Kelvin).

en: OPC-GT526S Dataset Weissfluhjoch

en: Particle counts were recorded in six size channels (0.3, 0.5, 0.8, 1.5, 2.5 and 5 μm) in a resolution of 20 seconds (time in UTC) by a particle counter (GT-526S, Met One Instruments, Inc.).

en: SMPS Dataset Weissfluhjoch

en: The SMPS file contains aerosol data (particle concentration, total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) in a size range between 12 and 460 nm (electrical mobility diameter) as well as instrument characteristics (sample temperature, pressure, relative humidity, mean free path and gas viscosity) in a temporal resolution of 2 minutes, with time in UTC, measured at Weissfluhjoch.

mimetype: application/x-netcdf

en: OPC-N3 Dataset Weissfluhjoch

en: Particle concentrations in a size range of 0.35 μm to 40 μm were measured with an OPC-N3 (Alphasense, Inc.) in a resolution of 20 seconds (time in UTC). PM₁, PM₂,₅ and PM₁₀ were directly derived.

en: APS Dataset Weissfluhjoch

en: The APS file contains aerosol data (total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) for aerodynamic diameters between 0.5 μm and ~20 μm as well as instrument characteristics, in a temporal resolution of 20 seconds, with time in UTC, measured at Weissfluhjoch.

en:
Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.

Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0).

Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³).

Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³).

Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

aerosol

display_name: aerosol

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

linda

display_name: linda

particles

display_name: particles

raclets

display_name: raclets

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: Aerosol Data Weissfluhjoch


en: ecology biodiversity traits dispersal reef-fish

en: Tropical reef fish trait data

en:
Species trait (i.e. adult body size, home range mobility behaviour, circadian activity and schooling) data gathered from the General Approach to Species Abundance Relationships (GASPAR) database (Kulbicki et al. 2013, Parravicini et al. 2013).

Kulbicki, M. et al. 2013. Global biogeography of reef shes: a hier- archical quantitative delineation of regions. – PLoS One 8: e81847.

Parravicini, V. et al. 2013. Global patterns and predictors of tropi- cal reef sh species richness. – Ecography 36: 1254–1262.

To complement the trait data with Pelagic Larval Duration information refer to the following publication:

Luiz, Osmar J., et al. "Adult and larval traits as determinants of geographic range size among tropical reef fishes." Proceedings of the National Academy of Sciences 110.41 (2013): 16498-16502.

In Donati et al. "A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography in August 2019, we used the PLD gathered from Luiz et al. 2013 as described in the methods - section "Species trait data".

en: Dispersal parameter value d per simulation starting with a minimum dispersal value, where each species is able to colonise each adjacent cell, and ending with the value of d for which all the extant habitats, including the most remote Pacific islands, have been colonised (d ∈ [1.75°: 5.45°]).

en: Global coast files from https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html used in the Figures

en: Donati et al. main script

en:
Main script used in : Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages"
accepted in Ecography, August 2019

en: Main text figures

en: R Script used for figures in main text in: Donati et al. 2019: "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019.

en: Functions used in R scripts in: Donati et al. 2019 "A process-based-model supports an association between  # dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019

en: Functions for Figures illustrated in Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019.

en: Supplementary material Appendix 1 Figures illustrated in Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages". accepted in Ecography, August 2019

en: Input for the Global distribution maps for proportions of species richness for (a) simulated dispersal parameter value (b) observed dispersal-related traits

en: Geographic distribution data (species richness and proportions of high/low dispersal-related traits) on predominantly low dispersing and high dispersing species expected based on the simulations, and those where low- and high- dispersal related traits values are observed for the five investigated traits.

en: Global species distribution of tropical reef fish species

en: Each species corresponds to a "Gaspar_code" gathered from the General Approach to Species Abundance Relationships (GASPAR) database (Kulbicki et al. 2013, Parravicini et al. 2013) and is linked to a specific Fishned ID corresponding to a specific Latitude and Longitude.

en: Simulated rates of speciation and extinction under a range of dispersal parameters. Net diversification rates are calculated from the difference between speciation and extinction rates. This data allows the creation of Figure A4 in Supporting information, Appendix 1.

en: Coral reef grid cells (shape, dbf, etc. files)

en:
Grid cells of 5 × 5°, corresponding to circa 555 × 555 km at the Equator (see Parravicini et al. 2013, Leprieur et al. 2016)

Parravicini, V. et al. 2013. Global patterns and predictors of tropi- cal reef sh species richness. – Ecography 36: 1254–1262.

Leprieur, F. et al. 2016. Plate tectonics drive tropical reef biodiver- sity dynamics. – Nat. Commun. 7: 11461.

en: Global simulated distribution of species under a range of dispersal parameter values

en:
Global distribution of simulated species richness under a range of dispersal parameter values d
d ∈ [1.75°: 5.45°].

en: Input for spatial congruence test overlap between proportions of low and high dispersal and observed low- and high- dispersal-related traits in assemblages (30-50% threshold). see Table 2.

en:
This dataset contains all data and R codes (R Development Core Team, https://www.R-project.org) used in the following publication: Donati GFA, Parravicini V, Leprieur F, Hagen O, Gaboriau T, Heine C, Kulbicki M, Rolland J, Salamin N, Albouy C, Pellissier L. "A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted by Ecography in August 2019. When using this data and R scripts the above publication should be cited.

The interaction of habitat dynamics with species dispersal abilities could generate gradients
in species diversity and prevalence of life-history and ecological traits, when the latter are associated with dispersal potential. In this dataset, we use a spatial mechanistic model of speciation, extinction and dispersal, constrained by a dispersal parameter. This model allows to simulate the interplay between reef habitat dynamics over the past 140 million years and dispersal, shaping lineage diversification history and global assemblage composition of over 6000 tropical reef fish species. Global trait distribution data of tropical reef fish are used to evaluate the congruence between simulations and observations.

biodiversity

display_name: biodiversity

dispersal

display_name: dispersal

ecology

display_name: ecology

reef-fish

display_name: reef-fish

traits

display_name: traits

educ

en: Education, culture and sport

en: Simulated and observed prevalence of dispersal-related traits in tropical reef fish assemblages worldwide


maintainer: Dirk Nikolaus Karger

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

en: CHELSA V1.2 (deprecated)

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

format: GeoTIFF

en: CHELSA Technical Documentation

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

en: CHELSA V2.1 (current)

en: CHELSA Version 2.1 based on ERA5 forcings and CMIP6

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

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

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

bioclim

display_name: bioclim

climate

display_name: climate

climate-change

display_name: climate-change

climate-layers

display_name: climate-layers

climatology

display_name: climatology

free-climate-data

display_name: free-climate-data

precipitation

display_name: precipitation

temperature

display_name: temperature

envidat

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

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

en: Raw data for ground-dwelling arthropod analysis

en: R code of flight-active insects analysis.

en: R code of ground-dwelling arthropod analysis.

en: Raw data for ground-dwelling arthropod NMDS analysis

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

en: Raw data for flying insects analysis

en: R code for NMDS of flight-active insects

en: Raw data Moran's I test

en: R code for NMDS of ground-dwelling arthropods

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

en: Raw data for flying insects NMDS analysis

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

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

alan

display_name: alan

artificial-lighting

display_name: artificial-lighting

ecosystem-services

display_name: ecosystem-services

outdoor-lighting

display_name: outdoor-lighting

sustainability

display_name: sustainability


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

en: Shapefile of 307 catchments in Switzerland

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

en: Reservoir volumes

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

en: Water demand (deprecated)

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

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

en: Water demand version 2 (corrected version)

en: Water supply

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

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

Water demand was estimated for six categories:

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

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

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

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

This dataset contains the following information:

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

reservoirs

display_name: reservoirs

water-demand

display_name: water-demand

water-scarcity

display_name: water-scarcity

water-supply

display_name: water-supply

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Hydro-CH2018 reservoirs


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

en: WRFrun

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

en: WPSSetup

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

en: MeteorologicalData

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

en: Postprocessing

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

en: WPSrun

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

en: StaticData

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

en: COSMO-WRF documentation v1.0

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

Documentation about the procedure to run COSMO-WRF

mimetype: application/pdf

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

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

high-resolution

display_name: high-resolution

meteoswiss

display_name: meteoswiss

numerical-simulation

display_name: numerical-simulation

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: Sofia Pagliarin

en: milan urban-atlas imperviousness copernicus soil-sealing barcelona

en: Shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h), see Dataset description

en: Information on data sources

en:
Dataset description

This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following:

 * a)	Barcelona city boundaries
 * b)	Barcelona metropolitan area, Àrea Metropolitana de Barcelona (AMB)
 * c)	Barcelona greater city (Urban Atlas)
 * d)	Barcelona functional urban area (Urban Atlas)
 * e)	Milan city boundaries
 * f)	Milan metropolitan area, Piano Intercomunale Milanese (PIM)
 * g)	Milan greater city (Urban Atlas)
 * h)	Milan functional urban area (Urban Atlas)

In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2).

 Dataset composition

The dataset is provided in .csv format and is composed of:

_IMD15_BCN_MI_Sources.csv_: Information on data sources

 _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona:
 * a)	Barcelona city boundaries (label: bcn_city)
 * b)	Barcelona metropolitan area, Àrea metropolitana de Barcelona (AMB) (label: bcn_amb)
 * c)	Barcelona greater city (Urban Atlas) (label: bcn_grc)
 * d)	Barcelona functional urban area (Urban Atlas) (label: bcn_fua)

 _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan:
 * e)	Milan city boundaries (label: mi_city)
 * f)	Milan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim)
 * g)	Milan greater city (Urban Atlas) (label: mi_grc)
 * h)	Milan functional urban area (Urban Atlas) (label: mi_fua)

_IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h).

Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM.

In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included:

 * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan);
 * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area);
 * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness;
 * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.

 Further information on the Dataset

This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.

 1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster.

 2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA.

 3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.

Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h).

Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been ‘crossed’ (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following:

 * Clipping: Arctoolbox > Data management tools > Raster > Raster Processing > Clip. The ‘input’ file is the HRL IMD raster file as described in point 1) and the ‘output’ file is each of the spatial/territorial files. The option "Use Input Features for Clipping Geometry (optional)” was selected for each of the clipping.
 * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox > Data management tools > Raster > Raster properties > Delete Raster Attribute Table and then through Arctoolbox > Data management tools > Raster > Raster properties > Build Raster Attribute Table; the "overwrite" option has been selected.

 Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal > Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.

The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.

barcelona

display_name: barcelona

copernicus

display_name: copernicus

imperviousness

display_name: imperviousness

milan

display_name: milan

soil-sealing

display_name: soil-sealing

urban-atlas

display_name: urban-atlas

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Soil sealing Barcelona and Milan different territorial levels


maintainer: Matthias Haeni

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

en: Site aerial photo (Nationalpark)

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

mimetype: image/jpeg

en: Field station data (NAF)

en: Data file.

en: Forest station data (NAB)

en: Site photo of the forest station (NAB)

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

en: Site photo of the field station (NAF)

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

en: Data plot of the forest station (NAB)

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

en: Data plot of the field station (NAF)

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

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


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

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

en: Tree species profiles for 2020, Blue band

en: Tree species profiles for 2020, NDVI index

en: Tree species profiles for 2019, CCI index

en: Tree species profiles for 2020, CIre index

en: Tree species profiles for 2019, Green band

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

en: Tree species profiles for 2020, CCI index

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

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

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

en: Tree species profiles for 2019, NIR band

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

en: Tree species profiles for 2020, NIR band

en: Tree species profiles for 2019, EVI index

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

en: Tree species profiles for 2020, NDMI index

en: Tree species profiles for 2019, Blue band

en: Tree species profiles for 2019, CIre Index

en: Tree species profiles for 2020, Red band

en: Tree species profiles for 2019, NDVI index

en: Tree species profiles for 2020, Green band

en: Tree species profiles for 2020, EVI index

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

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

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

en: Tree species profiles for 2019, Red band

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

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

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

time-series

display_name: time-series

tree-species

display_name: tree-species

envidat

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


global-cryosphere-watch-data-survey

maintainer: Mathias Bavay

title_for_slug: global-cryosphere-watch-data-survey

en: data-usage data-portal automatic-publication gcw data-exchange

en: Third party data usage

en: Data interoperability survey

en: Two surveys on the topic of data usage where conducted for the Global Cryosphere Watch data portal. The first one focused on the data provider point of view while the second one focused on the data user point of view. 37 data providers (ie institutions) worldwide provided their answers for the first survey (from fall 2017 until summer 2018) while 54 users (contacted through various mailing list such as the Cryolist) answered the questions on their third party data usage (fall 2019 until January 2020).

automatic-publication

display_name: automatic-publication

data-exchange

display_name: data-exchange

data-portal

display_name: data-portal

data-usage

display_name: data-usage

gcw

display_name: gcw

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Global Cryosphere Watch data survey


ceilometer-klosters

title_for_slug: ceilometer-klosters

en: klosters raclets vertical-visibility ceilometer cloud-base-height

en: Ceilometer data Klosters

en: Ceilometer installed at Klosters measuring cloud base height, cloud cover and backscatter profile. The time resolution is 30 seconds, with time given in number of days from January 0, 0000, UTC. More information on the variables in the manufacturer manual.

en: Cloud base height (m) and vertical visibility (m) were measured with the VAISALA Ceilometer CL31 in Klosters (LON: 9.880413, LAT: 46.869019). The CL31 is an instrument with constant reliability for all weather conditions and simultaneous detection of three cloud layers in heights up to 7.6 km.

ceilometer

display_name: ceilometer

cloud-base-height

display_name: cloud-base-height

klosters

display_name: klosters

raclets

display_name: raclets

vertical-visibility

display_name: vertical-visibility

en: Ceilometer Klosters