wind-crust-formation-snowmicropen-data

title_for_slug: wind-crust-formation-snowmicropen-data

en: wind-packing smp wind-crust snow snowmicropen

en: README

en: Describes the dataset in detail

en: Processing scripts

en: Contains the R-scripts used to process the SMPs

en: SMP data

en: Links to the dataset

en:
This dataset contains the SnowMicroPen (SMP) data from 38 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winters 2015/16 and 2016/17 and include more than 1000 SMP measurements.

The SMPs are organized per experiment. Each experiment subfolder contains the processed SMP profiles and some additional files.

Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.

This dataset is the basis of the following publication:

Sommer C.G., Lehning M., & Fierz C. (2017). Wind tunnel experiments: Saltation is necessary for wind-packing. Journal of Glaciology, 63(242), 950-958. doi:10.1017/jog.2017.53

smp

display_name: smp

snow

display_name: snow

snowmicropen

display_name: snowmicropen

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

en: Wind crust formation: SnowMicroPen data


maintainer: Milojevic

en: extreme-values alpine-precipitation reservoir-inflow low-flow return-periods

state: active

type: dataset

en: Daily Luzzone Reservoir Inflow 01-01-2000 to 31-12-2019

en: Daily total water inflow to Luzzone reservoir. Natural inflow (LUZ_inflow_raw) is back-calculated based on daily water level in reservoir (removing hydropower inputs and spills). Linear interpolation between daily values is used to bring all inflow values into positive space (LUZ_inflow_intrp).

format: SERVICE

en: R codes for Extreme Values Analysis

en: Zip file containing R codes for running extreme value analyses for high precipitation and inflow events as well as code for determining return levels of low inflow and low precipitation events

en:
 Summary

* Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland

* R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis

 Data
The dataset included here is the "natural" reservoir inflow for the Luzzone reservoir.
Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html).

 Codes
R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels:

* GEV (Generalized Extreme Value)

* GPD and GPDd (Generalized Pareto Distribution including declustered version)

* eGPD (extended Generalized Pareto Distribution)

* MEV (Metastatistical Extreme Value)

alpine-precipitation

display_name: alpine-precipitation

extreme-values

display_name: extreme-values

low-flow

display_name: low-flow

reservoir-inflow

display_name: reservoir-inflow

return-periods

display_name: return-periods

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: Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs


modeling-snow-failure-with-dem

maintainer: EnviDat Support

title_for_slug: modeling-snow-failure-with-dem

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

en: Dataset

media_type: application/zip

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

format: ZIP

mimetype: application/zip

en: Python file

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

mimetype: text/x-python

en: Read-me

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

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

discrete-element-modelling

display_name: discrete-element-modelling

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-micro-mechanics

display_name: snow-micro-mechanics

en: Modeling snow failure with DEM


young-forest-with-browsing-damage

title_for_slug: young-forest-with-browsing-damage

en:
Number of regeneration trees where browsing of the shoots from the previous year was recorded in NFI’s regeneration survey.

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

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

avalanches-silvaprotect

display_name: avalanches-silvaprotect

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

economic-region

display_name: economic-region

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

main-tree-species-in-young-forest

display_name: main-tree-species-in-young-forest

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

rockfall-silvaprotect

display_name: rockfall-silvaprotect

stem-count-of-young-forest

display_name: stem-count-of-young-forest

total-number

display_name: total-number

young-forest-with-browsing-damage

display_name: young-forest-with-browsing-damage

young-plants

display_name: young-plants

en: Young forest with browsing damage


maintainer: Anita C. Risch

en: global-change fertilization ammonification soil nutrient-network grassland mineralization nitrogen nitrification herbivore climate

en: Risch et al. 2020 - Raw data for publication "Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties"

en:
we assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).

Study sites and experimental design
The 22 sites contributing to this project are part of the Nutrient Network Global Research Cooperative (NutNet, https://nutnet.umn.edu/). Mean annual temperature across our 22 sites ranged from -4 to 22°C, mean annual precipitation from 252 to 1,592 mm, and elevations from 6 to 4,261 m above sea level (Fig. 1, Supplementary Table S1). Soil organic C varied from 0.8 to 7.8%, soil total N from 0.1 to 0.6%, and the soil C:N ratio from 9.1 to 21.5. Soil clay content spanned from 3.0 to 35%, and soil pH from 3.4 to 7.6 (Supplementary Table S2). Thus, the sites covered a wide range of environments in which grasslands occur (Fig. 1, Supplementary Table S1 & S2).
At each site, the effects of nutrient addition and herbivore removal were tested via a randomized-block design (Borer et al., 2014; Supplementary Fig. S1a). Three replicate blocks with 10 treatment plots each were established at each site, with the exception of the site at bldr.us, where only two blocks were established (Supplementary Fig. S1a). The 10 plots were randomly assigned to a nutrient or fencing treatment, but only a subset of four plots was used in the current study, each with a different treatment (see below; Supplementary Fig. S1a). All plots were 5 x 5 m and divided into four 2.5 x 2.5 m subplots (Supplementary Fig. S1b). 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; Supplementary Fig. S1b). Plots were separated by at least 1 m wide walkways.
In this study, we collected data from the following four treatments: (i) untreated control plots (Control), (ii) herbivore removal 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 removal (NPK+Fence; Supplementary Fig. S1a). The number of years of treatment differed among sites (2 – 9 years since start of treatment; Supplementary Table S1). For the nutrient additions, all sites applied 10 g N m-2 yr-1 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.
The vertebrate herbivore removal treatment (Fence) was established by fencing two plots, one control and one NPK plot, within each of the blocks (Supplementary Fig. S1a). We designed the fences so that they would effectively exclude aboveground mammalian herbivores with a body mass of over 50 g (Borer et al., 2014). At the majority of sites, the height of the fences was 180 cm, and the fence design included wire mesh (1 cm holes) on the first 90 cm along 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. While most sites only had native herbivores, a few sites (4) were also grazed by domestic animals (Supplementary Table S1).
Potential and realized soil net N mineralization, ammonification, nitrification and other soil properties
Each site participating in the study received a package containing identical material from the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) to be used for sampling and on-site N incubations. For the field incubation, we followed the protocol by Risch et al. (2015, 2019). Briefly, we drove a 5 x 15 cm (diameter x depth) steel cylinder 13.5 cm deep into the soil after clipping the vegetation at randomized locations in each plot. The top 1.5 cm of the cylinder remained empty to capture incoming N from run-off or deposition with a polyester mesh bag (mesh-size 250 µm) filled with 13.2 ± 0.9 g of acidic and alkaline exchanger resin (1:1 mixture; ion-exchanger I KA/ion-exchanger III AA, Merck AG, Darmstadt). The bag was fixed in place with a metal Seeger ring (Bruetsch-Rüegger Holding, Urdorf, Switzerland). We then removed 1.5 cm soil at the bottom of the cylinder and placed another resin bag to capture N leached from the soil column. The exchange resin was saturated with H+ and Cl- prior to filling the bags by stirring the mixture in 1.2M HCl for 1 h and then rinsing it with demineralized water until the electrical conductivity of the water reached 5 μS/cm. The cylinders were then re-inserted into the cored hole, level with the soil surface, and incubated for an average of 42 days (range 40 to 57days). The individual site coordinators chose the timing of incubation to start approximately six weeks prior to peak plant biomass production. All incubations were completed between February 2015 and January 2016 accounting for differences in growing season between northern and southern hemispheres. At the end of the incubation, the cylinders were re-collected and immediately shipped to WSL in an insulated box together with cold packs to halt further mineralization. Gloves were worn at all times to avoid contamination of the samples. Upon arrival at WSL, we extracted the resin bags and a 20 g subsample of sieved soil (4 mm) separately in a 100 ml PE-bottle with 80 ml 1 M KCl for 1.5 h on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience). We measured NO3- (colorimetrically; Norman & Stucki, 1981) and NH4+concentrations (flow injection analysis; FIAS 300, Perkin Elmer) on these filtrates.
At the start of the field incubation, we additionally collected two soil cores of 5 x 12 cm (diameter x depth) in each sampling plot and composited them to measure potential soil net Nmin, soil chemical and biological properties (see below). We also collected an additional sample (5 x 12 cm) to assess soil physical properties, which remained within the steel cylinder. Both ends were tightly closed with plastic caps. Cores were carefully packed to avoid further disturbance, and together with the composited soil samples, were shipped to the laboratory at WSL within a few days after collection.
From the composited samples, we extracted an equivalent of 20 g dry soil with KCl, as described above, and measured NO3- and NH4+concentrations. Realized soil net Nmin was then calculated as the difference between the inorganic N content of samples collected at the end of the incubation (plus N extracted from the bottom resin bag) and the N content at the beginning of the incubation; values were scaled to represent daily mineralization rates (mg N kg-1soil d-1; Risch et al., 2015). Realized soil net Nmin values represent an average period of 42 days prior to peak biomass, typically the highest period of biological activity, and not the entire year (Risch et al., 2019).
A second subsample of the composited sample was used to determine potential soil net Nmin in the laboratory (Risch et al., 2019). Briefly, we weighed duplicate samples (8 g dry soil) into 50-ml Falcon tubes. Soil moisture was brought to 60% of the field capacity of each plot, the Falcon tubes tightly closed and then incubated at 20°C for 42 days in a dark room. Every week the Falcon tubes were opened and ventilated. At the end of the incubation, the soil samples were extracted the same way as described above and NO3- and NH4+ was determined. Potential soil net Nmin was calculated as the difference between the N content before and after the incubation and scaled to represent daily values (mg N kg-1soil d-1). Using our NO3- and NH4+ measures we also calculated potential and realized soil net nitrification and soil net ammonification to be able to better understand the drivers of fertilization and herbivore removal effects on potential and realized soil net Nmin.
A third subsample of the composite soil sample was sieved (2 mm mesh) and microbial biomass (μg Cmic g-1 soil dry weight) was estimated 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 (Anderson & Domsch, 1978). The rest of the composited sample was dried at 65°C for 48 h, ground and sieved (2 mm mesh) to assess a series of soil chemical properties (Risch et al., 2019). We measured the percentage of clay as an indicator of soil texture (Gee & Bauder, 1986; Risch et al., 2019).
Statistical analyses
Potential and realized soil net Nmin were square root transformed to account for a highly skewed data distribution (yt = sign(y)*sqrt|y|; negative values in the data set impeded log transformation). To assess treatment effects on potential and realized soil net Nmin, we used linear mixed effects models (LMMs) fitted by maximum likelihood using the lme function from the nlme package (version 3.131.1; Pinheiro et al., 2016), R version 3.6.1; R Foundation for Statistical Computing. Treatment (Control, Fence, NPK, NPK+Fence) was a fixed factor, with site and block as random factors, where block was nested within site. We also tested for effects of time since start of treatments in preliminary analyses by adding total treatment years as an additional fixed factor. We did not find a significant effect of years of treatment, and thus dropped this variable from the models. The LMMs were corrected using varIdent if the homogeneity of variance criterion was not met (Pinheiro et al., 2016). To visualize our results, we calculated treatment effects using Cohens’ d statistic (Cohen, 1977; Koricheva et al., 2013). Note that calculating response ratios (or log response ratios) was not possible with our data, as we have both negative and positive values. We also fitted LMMs for potential and realized soil net ammonification and nitrification to gain more insight into how global change affects the processes underpinning potential and realized soil net Nmin. We also sqrt-transformed (see above) these dependent variables. Treatment was included as a fixed factor with random factors as described above. In addition, we assessed how potential and realized soil net Nmin were related to potential and realized soil net ammonification and nitrification, respectively. For this, we calculated site averages for each treatment separately. We then ran LMMs, with potential and realized soil net Nmin as the dependent variable, potential and realized soil net ammonification/nitrification as the independent ones. Site was included as a random factor.
Based on our previous work (Risch et al., 2019) and the existing literature (Schimel & Bennett, 2004; Liu et al., 2017), we developed a priori causal conceptual models of relationships among treatments, environmental drivers, and potential and realized soil net Nmin (Supplementary Fig. S2) to test with structural equation modelling (SEM) using a d-sep approach (Shipley, 2009; Lefcheck, 2016). The variables included in the model were long-term climatic conditions, specifically, site-level mean annual precipitation (MAP) and temperature of the wettest quarter (T.q.wet), plot-level soil texture (clay content) and soil microbial biomass. Mean annual precipitation and T.q.wet were obtained from WorldClim (Hijmans et al., 2005) (http://www.worldclim.org/) and together with the experimental treatments were predicted to directly affect soil properties and soil net Nmin (Supplementary Fig. S2). Soil clay content was, in turn, predicted to affect microbial biomass and soil net Nmin. Because we determined microbial biomass prior to incubating the samples in the laboratory or field, we assumed that the abundance of these microbes would be responsible for N process rates and not vice versa (Supplementary Fig. S2). We tested our conceptual model (Supplementary Fig. S2) 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 (Shipley, 2009; Lefcheck, 2016). 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 covariates and experimental treatments detected in the SEMs, we calculated treatment effect sizes, i.e. the differences in potential or realized soil net Nmin between Control and treatments (Fence, NPK, NPK+Fence) and plotted these values against the climate or soil covariates. Finally, we fitted LMMs for the soil variables included in our SEMs, with treatment as the fixed factor, and with site and block as random factors, where block was nested within site.

format: XLS

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

Paper Citation:

Risch Anita C., Zimmermann, Stefan, Moser, Barbara, Schütz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology

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

We assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).

ammonification

display_name: ammonification

climate

display_name: climate

fertilization

display_name: fertilization

global-change

display_name: global-change

grassland

display_name: grassland

herbivore

display_name: herbivore

mineralization

display_name: mineralization

nitrification

display_name: nitrification

nitrogen

display_name: nitrogen

nutrient-network

display_name: nutrient-network

soil

display_name: soil

en: Anthropogenic change and soil net N mineralization


maintainer: Yves Bühler

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

en: Data Access

en:
- DSM (10 cm resolution)
- Ortho (3 cm resolution)
- HS (10 cm resolution)
- original RGB drone images
- Agisoft Metashape Processing Report

All products are also available as a cloud optimized geotiff.

en: To map and assess snow depth on different dates, 9 flights were conducted in the winter season of 2020/21 at the Latschüelfurgga in Davos. The data was captured with a Sony RX1R II mounted on a Wingtra drone and was processed with the Agisoft Metashape software. High-resolution DSMs, orthomosaics and snow height rasters, as well as the original RGB images from each flight are available.

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

uav

display_name: uav

wingtra

display_name: wingtra

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: Photogrammetric Drone Data Latschuelfurgga


en: microwave radiometer l-band davos-laret snow

en: Davos-Laret Data 2019-2020 Winter

en: Publications

en: Snowpit data from SLF

en: In-situ characterization data from Davos-Laret snowpits.

en: Davos-Laret Data 2018-2019 Winter

en: Davos-Laret Data 2017-2018 Winter

en: Davos-Laret Data 2016-2017 Winter

en:
Dataset from the publication "L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory", under review in IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. volume and issue TBD.

Dataset specifics are described in the publication.

davos-laret

display_name: davos-laret

l-band

display_name: l-band

microwave

display_name: microwave

radiometer

display_name: radiometer

snow

display_name: snow


stem-number

title_for_slug: stem-number

en:
Number of stems of living trees and shrubs (standing and lying) starting at 12 cm dbh.

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

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

diameter-classes

display_name: diameter-classes

economic-region

display_name: economic-region

exotic-species-neophytes

display_name: exotic-species-neophytes

forest-type-and-stage-of-development

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

important-exotic-species-neophytes

display_name: important-exotic-species-neophytes

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

number-of-damages

display_name: number-of-damages

ownership

display_name: ownership

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

stage-of-stand-development

display_name: stage-of-stand-development

stem-number

display_name: stem-number

tree-species-woody-species

display_name: tree-species-woody-species

en: Stem number


en: snow-ice-temperature weisfluhjoch snow-density snow-water-equivalent snow-stratigraphy

en: WFJ Snow Profiles 1999-2014

en:
Biweekly manual snow profiles from Weissfluhjoch, Davos, Switzerland.
Version: 2015-09-14T16:25

en: README

media_type: text/plain

en: README file giving use metadata.

format: TXT

en: Dataset of manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland. Typical snow profile measurements and observations are included (temperature, density, grain size, grain type, hardness, wetness), following the guidelines of the The International Classification for Seasonal Snow on the Ground (ICSSG) [Fierz, C., Armstrong, R.L., Durand, Y., Etchevers, P., Greene, E., McClung, D.M., Nishimura, K., Satyawali, P.K. and Sokratov, S.A. 2009. The International Classification for Seasonal Snow on the Ground. IHP-VII Technical Documents in Hydrology N°83, IACS Contribution N°1, UNESCO-IHP, Paris].

snow-density

display_name: snow-density

snow-ice-temperature

display_name: snow-ice-temperature

snow-stratigraphy

display_name: snow-stratigraphy

snow-water-equivalent

display_name: snow-water-equivalent

weisfluhjoch

display_name: weisfluhjoch

en: Manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland


en: climatol extreme-values interpqm homogenisation snow-depth long-term-trends

en: data impact analyses

en:
 data frames containing results from the impact analyses:
* Homer
* Climatol
* interpQM
* original
* trends
* HSavg and HSmax
* R50maxHS

en:
 Input data for the following research article: Impact assessment of homogenised snow depth series on trends
The data consists of separate output files from the following homogenisation methods:
* Climatol
* HOMER
* interpQM
The variable is seasonal mean snow depth (HSavg)

plot.data is an additional data frame containing trends of HSavg (station, method, value, pvalue, altitude)

climatol

display_name: climatol

extreme-values

display_name: extreme-values

homogenisation

display_name: homogenisation

interpqm

display_name: interpqm

long-term-trends

display_name: long-term-trends

snow-depth

display_name: snow-depth

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: Input data for impact assessment of homogenised snow series


maintainer: Matthias Haeni

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

en: Site photo of the field station (JUF)

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

mimetype: image/jpeg

en: Data plot of the field station (JUF)

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

en: Forest station data (JUB)

media_type: text/csv

en: Data file.

format: CSV

mimetype: text/csv

en: Site photo of the forest station (JUB)

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

en: Data plot of the forest station (JUB)

en: Site aerial photo (Jussy)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Jussy in Switzerland where one station is located within a natural broad-leaved forest stand (JUB) with sessile oak (_Quercus petrea_; 90 yrs), aspen (_Populus tremula_; 60 yrs) and European hornbeam (_Carpinus betulus_; 60 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, JUF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Jussy 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: Jussy, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


en: local-hydraulic-resistance laminar-flow permeability pore-shape-factors heterogeneous-porous-media

en: Porous Media 1 2 and 3

en: Direct numerical simulations to obtain local pressure and velocity field.

en: results postprocessing

en: The results of the pore identification and integration. The results are stored as follows. Each individual iso-pressure patch (first two columns) has an identification number Np. There is more information stored than needed, such as which patches are neighbors, and what there positions are, however to assess the local hydraulic resistance you need the integrated pore identification number stored under 'tracks'. All patches belonging to the same pore have the same track number. We stored the local pressure value under 'p_i', integrated flux Q under 'Q_i', total surface area A under 'A_i' and distance dx under 'dx_int', circularity C under 'phi_i'. There are attributes stored with other identifiers such as _p and _l, they refer to the two closest neighboring patches at iso-pressure values p_i+dp and p_i-dp respectively. To obtain the exact results this data has to be preprocessed such as described in the supplementary materials of the paper.

en: Included are three direct numerical simulations results of Stokes flow in three heterogeneous porous media obtained with OpenFoam simulations. In addition we include three data files that contain point-based extracted pores based on the post-processing as reported in the submitted paper "Local hydraulic resistance in heterogeneous porous media" in GRL.

heterogeneous-porous-media

display_name: heterogeneous-porous-media

laminar-flow

display_name: laminar-flow

local-hydraulic-resistance

display_name: local-hydraulic-resistance

permeability

display_name: permeability

pore-shape-factors

display_name: pore-shape-factors

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Hydraulic resistance of pores in porous media using DNS of laminar flow


maintainer: EnviDat

en: metadata-convention meteorological-data-format research-data-management nead csv

en: PyNEAD (future reference implementation)

en: NEAD syntax v0.1 (EBNF)

mimetype: text/plain

en: MeteoIO (future reference implementation)

media_type: text/html

format: HTML

mimetype: text/html

en: NEAD on GitHub

en:
Acknowledgement: The NEAD format includes NetCDF metadata and is proudly inspired by both SMET and NetCDF formats. NEAD is designed as a long-term data preservation and exchange format.

The NEAD specifications were presented at the "WMO Data Conference 2020 - Earth System Data Exchange in the 21st Century" (Virtual Conference).

-----------------------

Summary:
The Non-Binary Environmental Data Archive (NEAD) format is being developed as a generic and intuitive format that combines the self-documenting features of NetCDF with human readable and writeable features of CSV. It is designed for exchange and preservation of time series data in environmental data repositories.

License: The NEAD specifications are released to the public domain under a Creative Commons CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

csv

display_name: csv

metadata-convention

display_name: metadata-convention

meteorological-data-format

display_name: meteorological-data-format

nead

display_name: nead

research-data-management

display_name: research-data-management

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: Non-Binary Environmental Archive Data (NEAD) format


causal-effect-of-mfoz

title_for_slug: causal-effect-of-mfoz

en: the-major-function-oriented-zone fujian-province built-up-land-change selection-bias causality propensity-score-matching

en: Data and Stata code for repeating the results

en:
Data.xlsx is the variables of 952 townships
Table 1-2 NN(1-1)_noreplace_caliper(0.01).do is STATA code to produce the results in Table 1 and 2
Figure 5 Distribution of the propensity scores.R is R code rto produce Figure 5
Figure 5 Standard mean difference.R is R code rto produce Figure 5

en:
Title: Closer to causality: How effective is spatial planning in governing built-up land expansion in Fujian Province, China?

Research objective: The Major Function Oriented Zone (MFOZ), the first strategic spatial plan in China, is developed to achieve a coordinated regional development, through spatial regulation and zoning of development. The MFOZ he MFOZ divided land into four major function-oriented zones: The development-optimized zone, the development-prioritised zone, the development-restricted zone, and the development-prohibited zone. We used propensity score marching to evaluate the effect of the MFOZ on built-up land expansion in Fujian Province over three time intervals (2013–2015, 2013–2018 and 2013–2020).

Data: Data.xlsx contains the variables of 954 towns in Fujian Province.
Town_ID is the town unique ID;
County_ID is the county unique ID;
City_ID is the city unique ID;
MFOZ is the the development-prioritised zone and the development-restricted zone (The development-optimized zone and the development-prohibited zone are excluded);
Builtup_13_15 is the built-up land expansion from 2013 to 2015;
Builtup_13_18 is the built-up land expansion from 2013 to 2018;
Builtup_13_20 is the built-up land expansion from 2013 to 2020;
Dis2water is the Euclidean distance from the town to the nearest waterbody;
Slope is the the average slope within the town;
GDP is the average GDP in 2010 within the town;
Pop is the average population in 2010 within the town;
Road is the average population in 2010 within the town;
Dis2city is the Euclidean distance from the town to the nearest prefectural city centre;
Nei_Arable, Nei_Forest, and Nei_Built.up are the area of arable land, forest land, and built-up land neighbouring town i in 2010.

Method: we used the propensity score matching to compare the changes in the amount of built-up land in the towns of the development-prioritised zone with the matched towns of the development-restricted zone. Additionally, we used three evaluation intervals (2013–2015, 2013–2018 and 2013–2020) to evaluate temporal variation in the causal effect of the MFOZ on built-up land expansion.

built-up-land-change

display_name: built-up-land-change

causality

display_name: causality

fujian-province

display_name: fujian-province

propensity-score-matching

display_name: propensity-score-matching

selection-bias

display_name: selection-bias

the-major-function-oriented-zone

display_name: the-major-function-oriented-zone

en: Causal effect of MFOZ


en: land-use-planning strategic-planning analytical-hierarchy-process planning-efficacy governance

en: The present content is part of the published paper Palka, G., Oliveira, E., Pagliarin, S., & Hersperger, A. M. (2021). Strategic spatial planning and efficacy: an analytic hierarchy process (AHP) approach in Lyon and Copenhagen. European planning studies, 29(6), 1174-1192. It contains the jupyter notebook and sample data to compute Analytical Hierarchy Process, and a report on its use.

analytical-hierarchy-process

display_name: analytical-hierarchy-process

governance

display_name: governance

land-use-planning

display_name: land-use-planning

planning-efficacy

display_name: planning-efficacy

strategic-planning

display_name: strategic-planning

en: Planning efficacy computation based on AHP


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

en: Field station data (SCF)

en: Data file.

en: Further details on the data repository

en: Meta data documentation.

en: Data plot of the field station (SCF)

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

en: Site photo of the forest station (SCB)

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

en: Data plot of the forest station (SCB)

en: Site aerial photo (Schänis)

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

en: Site photo of the field station (SCF)

en: Site photo of the field station from above (viewing downwards the slope).

en: Forest station data (SCB)

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 Schänis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Schänis 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

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Schänis, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1998 onwards


maintainer: Irmi Seidl

en: community-energy switzerland renewable-energy cooperatives energy-cooperatives

en: Raw date (SPSS)

en: Raw data in sav. format (SPSS). For comprehension of the data and variable structure, please consult the modified questionnaire with variable names.

en: Codebook

en: Description of variable attributes and valid n

en: Questionnaire with variable names

en: Modified questionnaire with variable names for each item. Should be used together with the data files (.csv or .sav) to reconstruct the data and variable structures.

en: Raw data (.csv)

en: Raw data in csv. format. For comprehension of the data and variable structure, please consult the codebook and the modified questionnaire with variable names.

en:
 Topic of Survey
The data at hand on energy cooperatives in Switzerland were collected in 2016 as part of the project "Collective financing of renewable energy projects in Switzerland and Germany" of the National Research Programme 71 "Managing Energy Consumption". The cooperatives were surveyed on their organizational structure, their activities in electricity and heat generation, their finances, the political context and their assessments of the future.

 Survey Method
The survey was targeted at all energy cooperatives in Switzerland (this is the basic population). The Swiss Commercial Register was searched for cooperatives and specific keywords in order to determine this basic population and collect addresses. This search in May 2016 resulted in a total of 304 energy cooperatives, to which a questionnaire was sent in July 2016. A pre-test with 8 persons had been carried out before the questionnaire was sent out. The questionnaire was provided in German and French. It was sent by mail and an attached letter referred to a link for the digital version if preferred. The online version was designed with the software "Sawtooth". After three weeks, a first, and after six weeks a second reminder letter was sent to those cooperatives that had not yet completed the questionnaire. The returned hardcopy questionnaires were manually entered into the database and then combined with the electronic data from the online survey. In the course of the survey, the total population was reduced from 304 to 289: in 4 cases the survey was not deliverable, 4 cooperatives had dissolved, 6 were not actually energy cooperatives, 1 case had recently changed its legal form. With a response rate of 47%, the final data set comprises 136 responses (from 77 digital and 59 hardcopy questionnaires). However, not all 136 of the returned questionnaires were filled out completely. We checked for answers that seemed contradictory or incomprehensible. If an error could be clearly identified and the correct answer derived, the answer was adjusted, otherwise the answer was replaced by "missing data".

 Anonymization
Participating cooperatives have been assured that their information will be kept confidential and will only be made public anonymously. For this reason, the data have been anonymized in in order to prevent any identification of individual cooperatives.

 How to Use the Data
* The data are available in CSV and SPSS (sav.) format.
* A codebook and a modified version of the used questionnaire are provided to illustrate the data and variable structure. In the questionnaire, the variable names are assigned to the corresponding questions. In the codebook, further information on these variables (valid n, answer categories) can be found. This information (of the codebook) is already integrated in the SPSS file.

 Current Embargo on Data
These data are currently under embargo and will only be released when the project is completed (not before 2020).

#Additional Information
* The used questionnaire is provided in German and French.
* Descriptive results of the survey were published in a WSL report: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:18943

community-energy

display_name: community-energy

cooperatives

display_name: cooperatives

energy-cooperatives

display_name: energy-cooperatives

renewable-energy

display_name: renewable-energy

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: Energy Cooperatives in Switzerland: Survey Results // Energiegenossenschaften in der Schweiz: Befragungsergebnisse


root-mass-of-live-trees-zell-wutzler

title_for_slug: root-mass-of-live-trees-zell-wutzler

en:
Dry weight (mass) of the belowground part (roots) of living trees and shrubs starting at 12 cm dbh. The dimensions of the roots are determined according to Zell and Wutzler.

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

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

root-mass-of-live-trees-zell

display_name: root-mass-of-live-trees-zell

wutzler

display_name: wutzler

en: Root mass of live trees (Zell, Wutzler)


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

en: Snowdepth maps

format: GeoTIFF

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

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

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

The work is published in:

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology

educ

en: Education, culture and sport

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


en: vegetation-structure forest-management echolocation chiroptera habitat-modelling

en: R scripts for all analyses

en: Complete set of field data and LiDAR data including bat activity measurements

en: Variable description_20220411.csv

en: Description of all variables given in the file 'Felddaten_2020-02-14_Guilde.csv'

en: Habitat shift caused by human impact on vegetation structure poses a great threat to species which are special- ized on unique habitats. Single layered beech forests, the main foraging habitat of Greater Mouse-eared Bats (My- otis myotis), are threatened by recent changes in forest structure. After this species suffered considerable popula- tion losses until the 1970s, their roosts in buildings are strictly protected. However, some populations are still de- clining. Thus, the spatial identification of suitable foraging habitat would be essential to ensure conservation pol- icy. The aim of this study was (a) to verify the relevance of forest structural variables for the activity of M. myotis and (b) to evaluate the potential of LiDAR (Light Detection and Ranging) in predicting suitable foraging habitat of the species. We systematically sampled bat activity in forests close to 18 maternity roosts in Switzerland and applied a generalized linear mixed model (GLMM) to fit the activity data to forest structure variables recorded in the field and derived from LiDAR. We found that suitable forest foraging habitat is defined by single layered for- est, dense canopy, no shrub layer and a free flight space. Most importantly, this key foraging habitat can be well predicted by airborne LiDAR data. This allows for the first time to create nationwide prediction maps of potential foraging habitats of this species to inform conservation management. This method has a special significance for endangered species with large spatial use, whose key resources are hard to identify and widely distributed across the landscape.

chiroptera

display_name: chiroptera

echolocation

display_name: echolocation

forest-management

display_name: forest-management

habitat-modelling

display_name: habitat-modelling

vegetation-structure

display_name: vegetation-structure

envidat

en: EnviDat: the Environmental Data Portal

en: LiDAR metrics predict suitable forest foraging areas of endangered Mouse-eared bats (Myotis myotis)


weather-station-davos-wolfgang

maintainer: Jörg Wieder

title_for_slug: weather-station-davos-wolfgang

en: temperature weather raclets relative-humidity pressure

en: Weather Station Davos Wolfgang

en: The data file contains date and time (YYYY-MM-DD hh:mm:ss) in UTC, temperature (°C), relative humidity (%), pressure (hPa) at 10 m above ground and above sea level. The data was averaged over 10 minutes.

en: The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577).

pressure

display_name: pressure

raclets

display_name: raclets

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

weather

display_name: weather

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.


increment

title_for_slug: increment

en:
Increase in the volume of stemwood with bark of the trees and shrubs starting at 12 cm dbh that have survived between two inventories and of the losses (modelled for the half period), plus the volume of the gains. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.

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

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

avalanches-silvaprotect

display_name: avalanches-silvaprotect

biogeographical-region

display_name: biogeographical-region

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

increment

display_name: increment

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

rockfall-silvaprotect

display_name: rockfall-silvaprotect

site-quality

display_name: site-quality

stage-of-stand-development

display_name: stage-of-stand-development

tree-species-woody-species

display_name: tree-species-woody-species

en: Increment


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

en: LWF plot Bettlachstock. Foto: Matthias Haeni, 2019

en: Bettlachstock Meteorological Data LWF

en:
![alt text](https://www.envidat.ch/dataset/8f67193b-12cf-4871-9a9e-750b816e9d10/resource/b05db334-5bf6-42d6-a985-a5253366259d/download/lwf_bettlachstock.jpg "LWF Bettlachstock")

LWF Plot Bettlachstock

- Community: Bettlachstock / canton SO
- Date of installation: 6 June 1995
- Size of the plot: 1.28 ha
- Altitude: 1101-1196 m
- Mean slope: 66%
- Geology (in German): Kettenjura; Jura: Dogger, oberer Hauptrogenstein
- Soil types (WSL) : Rendzic Leptosols; Calcaric Cambisols
- Woodland association after EK72: 13 h: Cardamino-Fagetum tilietosum
- Main tree species: Fagus sylvatica
- Management system: high forest
- Silvicultural system: forest reserve
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 49.5 cm
- Number of trees BHD >= 12 cm (2011): 632
- Maximum tree age: Fagus sylvatica 170-190 yr - Picea abies 200 yr - Fraxinus excelsior 170 yr - Ulmus glabra 160 yr - Abies alba 190 yr

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

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

educ

en: Education, culture and sport

en: LWF Bettlachstock long-term research site


en: icp-forests swiss-forest-lab long-term-forest-ecosystem-research nitrogen lignin carbon deadwood

en: Data Request

en:
Sampling of deadwood for density and chemical analysis during summer 2009

 Purpose:

Determination of N and C pools of deadwood

 Paper Citation:

 * WEGGLER, K.; DOBBERTIN, M.; JÜNGLING, E.; KAUFMANN, E.; THÜRIG, E., 2012. Dead wood volume to dead wood carbon: the issue of conversion factors. European Journal of Forest Research 131, 1423-1438.

carbon

display_name: carbon

deadwood

display_name: deadwood

icp-forests

display_name: icp-forests

lignin

display_name: lignin

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

nitrogen

display_name: nitrogen

en: Deadwood sampling at Sanasilva and LWF sites


maintainer: Joan Casanelles Abella

en: hylaeus-communis dna-metabarcoding cavity-nesting-bees urban-ecology trap-nest antwerp osmia-bicornis chelostoma-florisomne tartu zurich osmia-cornuta poznan paris plant-pollinator-interactions

en: Larval food composition

en: Explanation data variables

en: Urbanization poses threats and opportunities for the biodiversity of wild bees. A main gap relates to the food preferences of wild bees in urban ecosystems, which usually harbour large numbers of plant species, particularly at the larval stage. This data sets describes the larval food of four wild bee species (i.e. Chelostoma florisomne, Hylaeus communis, Osmica bicornis and Osmia cornuta) and three genera (i.e. Chelostoma sp., Hylaeus sp, and Osmia sp.) common in urban areas in five different European cities (i.e. Antwerp, Paris, Poznan, Tartu and Zurich). This data results from a European-level study aimed at understanding the effects of urbanization on biodiversity across different cities and citiscapes, and a Swiss project aimed at understanding the effects of urban ecosystems in wild bee feeding behaviour. Wild bees were sampled using standardized trap-nests in 80 sites (32 in Zurich and 12 in each of the remaining cities), selected following a double gradient of available habitat at local and landscape scales. Larval pollen was obtained from the bee nests and identified using DNA metabarconding. The data provides the plant composition at the species or genus level of the different bee nests of the studied species in the studied sites of the five European cities. For Hylaeus communis, this is the first study in reporting larval food composition.

antwerp

display_name: antwerp

cavity-nesting-bees

display_name: cavity-nesting-bees

chelostoma-florisomne

display_name: chelostoma-florisomne

dna-metabarcoding

display_name: dna-metabarcoding

hylaeus-communis

display_name: hylaeus-communis

osmia-bicornis

display_name: osmia-bicornis

osmia-cornuta

display_name: osmia-cornuta

paris

display_name: paris

plant-pollinator-interactions

display_name: plant-pollinator-interactions

poznan

display_name: poznan

tartu

display_name: tartu

trap-nest

display_name: trap-nest

urban-ecology

display_name: urban-ecology

zurich

display_name: zurich

envidat

en: EnviDat: the Environmental Data Portal

en: Larval food composition of four wild bee species in five European cities


en: tracers mixing-models stable-isotopes end-member-mixing-analysis emma eemma

en: vignette data

en: A simple data set for the vignette script

en: eemma user's guide

en: A brief user's guide to EEMMA, with illustrations using a simple data set

en: eemma vignette script

en: An R script for the vignettes (simple data analysis demonstrations) shown in the user's guide

mimetype: application/octet-stream

en: An R script that performs ensemble end-member mixing analysis

en:
The R script eemma.R, which implements Ensemble End-Member Mixing Analysis (EEMMA) to estimate source fractions in mixtures, exploiting information contained in time-series correlations among tracer time series.

A brief user's guide, a demonstration script, and a demonstration data set are also provided, to accompany Kirchner, J.W., Mixing models with multiple, overlapping, or incomplete end-members, quantified using time series of a single tracer, Geophysical Research Letters, 2023.

The user's guide is available for public use under Creative Commons CC-BY-SA. Public use of the scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

eemma

display_name: eemma

emma

display_name: emma

end-member-mixing-analysis

display_name: end-member-mixing-analysis

mixing-models

display_name: mixing-models

stable-isotopes

display_name: stable-isotopes

tracers

display_name: tracers

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: eemma.R, an R script for Ensemble End-Member Mixing Analysis


en: biodiversity national-data-centre switzerland lichen swisslichens lichen-distribution lichen-conservation

en: data extract 20191113

en:
This dataset provides distribution data of lichens in Switzerland of the National Data and Information Centre, called Swiss Lichens. SwissLichens is a partner of InfoSpecies, the network of Swiss data and information centres for fauna, flora and fungi. One of its main objectives is to document the spatial and temporal distribution of species in Switzerland.

The SwissLichens database currently contains more than 120’000 georeferenced lichen observations, distributed throughout Swizerland. The oldest observations date back to 1790. A large portion of the records dtae from the last two decades of the last century to the present day. The database is continuously updated with new findings. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature.

Each record consists of the species name, information of the location (Swiss Coordinates, precision of the coordinates, elevation above sea level, municipality and canton), the date of observation, the ecotype (epiphytic, terricol, lignicol, saxicol), as well as information on the conservation status of the species (red list status, conservation priority status, status in the Nature and Cultural Heritage Act). Information on the ecology (habitat, substrate) is partially available.
They are free of charge for research projects and can be requested from InfoSpecies using a form.

Licence under www.infospecies.ch. Data is free of charge for research projects and available on request.

biodiversity

display_name: biodiversity

lichen

display_name: lichen

lichen-conservation

display_name: lichen-conservation

lichen-distribution

display_name: lichen-distribution

national-data-centre

display_name: national-data-centre

swisslichens

display_name: swisslichens

switzerland

display_name: switzerland

educ

en: Education, culture and sport

en: SwissLichens - Distribution of lichens in Switzerland


en: tls terrestrial-laser-scans sea-ice-elevation polar sea-ice

en: Link to post-processed TLS’s of Antarctic sea ice

en: Post Processed TLS's

en: Compressed dat files

en: Data processing documentation

en:
This dataset comprises of a post-processed set of terrestrial laser scans (TLS’s) of Antarctic sea ice obtained during the Sea Ice Physics and Ecosystem Experiment-2 (SIPEX-2, http://seaice.acecrc.org.au/sipex2012/) in September-November 2012. The post-processing steps include the registration of the individual scans into a single 3-dimensional point cloud, the removal of unwanted noise caused by particles in the air (i.e., snow crystals), and the final generation of surface grids based on the cleaned individual point returns. The final product includes the ‘xyz’ coordinates of the individual point measurements, and gridded surfaces covering study areas of 100m x 100 m, and at resolutions of 0.01 m, 0.1 m, 0.25 m, 0.5 m and 1 m for each of the survey dates. Additionally, subgrid statistics that include the mean elevation, standard deviation, minimum and maximum elevations, range, and number of point returns in each gridcell are generated. The final product is provided in space-delimited text files, with the surface grids provided in Digital Terrain Model (DTM) format ready for visualization in any GIS software.

How to cite:
Please also cite the original publication when using this data set.: Trujillo, E., K. Leonard, T. Maksym, and M. Lehning (2016), Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice, J. Geophys. Res. Earth Surf., 121, doi:[10.1002/2016JF003893](https://dx.doi.org/10.1002/2016JF003893).

polar

display_name: polar

sea-ice

display_name: sea-ice

sea-ice-elevation

display_name: sea-ice-elevation

terrestrial-laser-scans

display_name: terrestrial-laser-scans

tls

display_name: tls

envidat

en: EnviDat: the Environmental Data Portal

en: High resolution sea ice surface topography from the SIPEX-2 expedition, East Antarctica, 2012


en: forest-snow snow-hydrology forest-canopy meteorological-data snow-modeling

en: Surface data

en: Surface fields for model domain at 2m spatial resolution, including simulation domain, elevation, and canopy structure datasets

en: Meterorological forcing

en: Mmeteorological forcing data for FSM2 at hourly resolution, corresponding to the location of the 5DF automatic weather station / snow measurement field 5DF. Data is available from 1 Sept 2015 to 21 Aug 2021 (WY 2016-21)

en: This dataset contains surface datasets (in particular canopy structure fields) and meteorological input (water years 2016-2021) required to run the snow model FSM2 over the Fluela valley. Land surface datasets are available for a 1.5x2.5km model domain at 2m spatial resolution, meteorological input at hourly resolution is provided for a point and corresponds to the location of the automatic weather station / snow measurement field 5DF in Davos. Corresponding FSM2 simulations are used and analyzed in the publication 'Canopy structure, topography and weather are equally important drivers of small-scale snow cover dynamics in sub-alpine forests' by Mazzotti et al. (submitted to HESSD). This publication should be cited whenever the dataset is used.

forest-canopy

display_name: forest-canopy

forest-snow

display_name: forest-snow

meteorological-data

display_name: meteorological-data

snow-hydrology

display_name: snow-hydrology

snow-modeling

display_name: snow-modeling

en: Input datasets for forest snow modelling in Fluela valley, WY 2016-21


maintainer: Giulia Francesca Azzurra Donati

en: integrated-modelling ecological-corridors biodiversity circuit-theory human-dominated-landscapes urban-spatial-planning

en: Amphibian whole life cycle predictors - 10m resolution

en:
Amphibian whole life cycle predictors include 6 variable types:
(1)	Topographic – slope [°]: derived from the Swiss Topographical Landscape model (swissTLM 3D 2015) at 2 m resolution (elevation uncertainty ± 0.5m) and, subsequently, aggregated to a median value;
(2)	Hydrologic: two predictors including nearest distance to water [m] (all water-related objects and rasterized lotic, lentic and reedbeds from the regional cadastral maps – AV: Daten der Amtliche Vermessung) and runoff coefficient [C] (between 0 and 1 relating amount of runoff to amount of precipitation, mapped AV: Daten der Amtliche Vermessung data against literature values for different land covers);
(3)	Edaphic – soil moisture variability (not available): can gathered from the spatial modelling of Ecological Indicator Values in Switzerland (Descombes et al., 2020) and resampled from 100m to 10m;
(4)	Vegetation: we calculated the median and standard deviation of the Normalized Difference Vegetation Index (NDVI) from all available cloud free Sentinel-2 pixels available between April and October for the years 2016 to 2019 (Drusch et al., 2012) and the median vegetation height [m] (not available) can gathered from Ginzler and Hobi (2015);
(5)	Land-use derived: includes three predictors, grassland density [grassland/cell] (not available) - can be gathered from Pazur et al. (2021), total grasslands area evaluated using a spatial weight matrix and a 250m neighborhood radius around each cell; urbanization [buildings/cell] – average building density for a 250m neighborhood around each cell based on the AV: Daten der Amtliche Vermessung ; and traffic intensity [vehicles/day] – average daily vehicle count (Nationales Personenverkehrsmodells);
(6)	Movement-ecology related: includes three predictors of proximity – nearest forest distance [m]; nearest rock-gravel and sandy area (RGS) distance [m] and nearest road distance [m] (considering any road type) – all calculated based on the AV: Daten der Amtliche Vermessung maps.

en: Amphibian monitoring data

en: Processed amphibian occurrence data (2017-2019) in the cantons of Aargau and Zürich. The dataset includes information for the following species: _Alytes obstetricans_,_Bombina variegata_, _Bufo bufo_, _Hyla arborea_, _Ichtyosaura alpestris_, _Lissotriton helveticus_, _Lissotriton vulgaris_, _Rana temporaria_, _Salamandra salamandra_, and _Triturus cristatus_.

en: Amphibian breeding sites of national importance - cantons of Aargau and Zürich

en: Amphibian breeding sites of national importance (focal nodes) in the cantons of Aargau and Zürich, 30x30 m resolution, binarised ascii format.

en: Amphibian species specific landscape resistance maps.

en: For each amphibian species we used the weighted average Ensemble model suitability map and converted it to a resistance map using an exponential decay function following (Duflot et al., 2018) using the species-specific mpa threshold previously computed (Preau et al., 2020). The function assigns a resistance value of 1,000 when habitat suitability equals zero, and a value of one when habitat suitability is greater than or equal to the binarization threshold. The exponential shape strengthens the barrier effect of less favorable areas.

en: Binarized "green" and "grey" landcover data (Cantons of Aargau and Zürich)

en: "Green" landcover refers to lawns and vegetated urban surfaces such as, for example private gardens, public parks, cemeteries, green stripes along roadsides, etc. While "grey" landcover refers to impervious grounds such as roads, troittoirs, other impervious. Binarized maps, 30x30m resolution.

en: The data includes (1) amphibian occurrence data (2017-2019) for ten species across the cantons of Aargau and Zürich gathered from the Coordination Center for the Protection of Amphibians and Reptiles of Switzerland (http://www.karch.ch), (2) amphibian whole-life cycle environmental predictors (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related), and (3) local urban "green" and "grey" landcover data which can be used to identify opportunities for Blue-Green Infrastructure (through green or grey transitions) in support of regional landscape connectivity.

biodiversity

display_name: biodiversity

circuit-theory

display_name: circuit-theory

ecological-corridors

display_name: ecological-corridors

human-dominated-landscapes

display_name: human-dominated-landscapes

integrated-modelling

display_name: integrated-modelling

urban-spatial-planning

display_name: urban-spatial-planning

educ

en: Education, culture and sport

envidat

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

en: Amphibian and urban-rural landscape data Swiss Lowlands


yield

title_for_slug: yield

en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh cut between two inventories. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.

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

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

avalanches-silvaprotect

display_name: avalanches-silvaprotect

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

net-increment-star

display_name: net-increment-star

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

rockfall-silvaprotect

display_name: rockfall-silvaprotect

stage-of-stand-development

display_name: stage-of-stand-development

tree-species-woody-species

display_name: tree-species-woody-species

yield-star

display_name: yield-star

en: EnviDat: the Environmental Data Portal

en: Yield*


maintainer: Sofia Pagliarin

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

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

en: Soil sealing Barcelona and Milan different territorial levels


en: potential-evapotranspiration weather precipitation climate aridity

en: 1980-2011 average potential evapotranspiration (in mm per year) at 500m resolution over Switzerland

media_type: image/tiff

en: Long-term (1980-2011) average potential evapotranspiration (in mm per year) at 500m resolution over Switzerland. GeoTIFF raster file can be read in R using the command raster(“pet_ch_longterm_yr_avg.tif”), after installing packages "raster" and "rgdal".

format: TIFF

en: 1980-2011 average precipitation over Switzerland at 500m resolution

en: Long-term (1980-2011) average precipitation (in mm per year) at 500m resolution over Switzerland. GeoTIFF raster file can be read in R using the command raster(“pet_ch_longterm_yr_avg.tif”), after installing packages "raster" and "rgdal".

en: Long-term (1980-2011) average annual precipitation (pcp_ch_longterm_yr_avg.tif) and potential evapotranspiration (pet_ch_longterm_yr_avg.tif) at 500m resolution. Units are mm per year. Files are GeoTIFF rasters, and can be read in R using the command raster("pcp_ch_longterm_yr_avg.tif), after installing packages "raster" and "rgdal".

aridity

display_name: aridity

climate

display_name: climate

potential-evapotranspiration

display_name: potential-evapotranspiration

precipitation

display_name: precipitation

weather

display_name: weather

en: Average precipitation and PET over Switzerland at 500m resolution


en: COST European Snow Booklet (ESB) - DORA Link

en: DORA Link of the COST European Snow Booklet (ESB).

en: European Snow Booklet public metadata

en:
The core of the European Snow Booklet (ESB), the country reports of 38 European countries, are based on information and metadata on snow measurements collected through a comprehensive survey, the ESB Questionnaire, between August 2017 and March 2018. The station metadata (for example location, elevation, measurement start date) from each country was provided by one or several institutions responsible for snow observations. By answering the questionnaire the contacts agreed that the information and metadata provided could be presented in the ESB. For cases where institutions indicated that information on station locations could be made publicly available, the metadata for the variables snow depth (HS), depth of snowfall (HN) and water equivalent of the snow cover (SWE), is additionally provided here for download (METADATA.xlsx).
Please note that not all institutions gave permission to publish their station metadata. This metadata has to be requested from the non-personal institution contacts provided in the METADATA.xlsx file. In some cases, however, no institutional contact is available and thus the contact of the country representative who filled in the questionnaire is used.
For current, updated station metadata we refer to the (non-personal) institutional contacts provided in METADATA.xlsx and/or the institution webpages. Note, however, that we cannot guarantee for completeness of the dataset for all countries because operational networks are prone to changes.

en: European Snow Booklet unpublished metadata

en: The entire station metadata of all European institutions that contributed data is stored in METADATA_unpublished.xlsx. This file, however, cannot be accessed, since not all institutions gave permission to publish their station metadata. If you wish to access the metadata, please send a request to marty@slf.ch stating clearly why you want to access these metadata. NOTE: in this file ONLY station metadata (Lat/Lon/Altitude) is given, NO measured snow depth data.

en: List of authors and list of the editorial board of the European Snow Booklet

en:
Here you can find the list of all lead authors of the different chapters of the European Snow Booklet (ESB).
In addition a list of the editorial board that reviewed the ESB is given.

en: COST European Snow Booklet (ESB)

en: Please find here the web version of the COST European Snow Booklet (ESB).

en:
The European Snow Booklet (ESB) is a book of reference for snow measurements that has been produced through collaboration with many European snow practitioners and snow scientists in the framework of the European Cooperation in Science and Technology (COST) Action ES1404 “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (HarmoSnow)”. The ESB provides a unique collection of information about operational snow observations in the European countries and the methods used to perform basic measurements of snow on the ground: snow depth (HS), depth of snowfall (HN), water equivalent of the snow cover (SWE) and presence of snow on the ground (PSG). Information and station metadata (for example location, elevation) for these basic snow variables were collected through a comprehensive survey, the ESB questionnaire between August 2017 and March 2018. Numerous institutions of 38 European countries provided detailed information describing the status of the operational snow observations and the methods used at the time of the survey. Based on the information provided, a country report was written for each European country. Similarities and differences among the countries, that is, the choice of snow variables to be measured, the measurement principles applied, the number of stations, or the spatial and elevational station distribution are pointed out. Thus the collection of country reports demonstrates the relevance of snow measurements for each country. Thus, the intention of the ESB is to foster better knowledge transfer regarding snow measurements between the snow science and operational communities and to improve the communication of information to the general public.

For detailed information on the European countries, we refer to the ESB, which can be downloaded here (envidat.ch). Please note that the ESB is not a living document and information and station metadata are from August 2017 till March 2018, except for Latvia (metadata updated in December 2018).

depth-of-snowfall

display_name: depth-of-snowfall

harmonisation-of-snow-measurements

display_name: harmonisation-of-snow-measurements

snow-depth

display_name: snow-depth

snow-measurement-methods

display_name: snow-measurement-methods

water-equivalent-of-the-snow-cover

display_name: water-equivalent-of-the-snow-cover

educ

en: Education, culture and sport

envidat

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

en: European Snow Booklet – an Inventory of Snow Measurements in Europe


aerosol-data-weissfluhjoch

title_for_slug: aerosol-data-weissfluhjoch

en: raclets linda inp ice-nucleation particles aerosol

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.

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: 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: 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.

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:
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: the Environmental Data Portal

en: Aerosol Data Weissfluhjoch


en: carbon fauna-data pinus forest soil drought litter pfynwald

en:
Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris).
Data include: (1) properties of soils sampled in 2011 and 2019 (SOC and N concentrations and stocks, soil masses, 13C and 15N natural abundances, C/N ratios, clay content, pH, inorganic C, stoniness, bulk density); (2) litter mass loss and initial litter chemistry of dominant tree species (Quercus, Pinus, Viburnum) from a litter decomposition experiment carried out in 2014-2015; (3) soil fauna abundance sampled in 2015; (4) soil volumetric water content and soil temperature at 10 cm depth measured during the litter decomposition experiment in 2014-2015; (5) soil mesofauna (Acari and Collembola) diversity and community composition from sampling in 2017; (6) irrigation-induced changes in litterfall (2013-2014, 2016-2017), fine-root production (data 2015 from Brunner et al., 2019, Frontiers in Plant Science), annual soil respiration (estimated for 2014-2015), litter mass loss from litter decomposition experiment (May-October 2014), and SOC stocks measured in 2011 and 2019; (7) Moisture dependency of microbial soil respiration (0-10 cm depth, adapted from Joseph et al., 2020 PNAS), soil respiration measured in 2015 and abundance of Acari, Collembola and Lumbricidae sampled in 2015.

carbon

display_name: carbon

drought

display_name: drought

fauna-data

display_name: fauna-data

forest

display_name: forest

litter

display_name: litter

pfynwald

display_name: pfynwald

pinus

display_name: pinus

soil

display_name: soil

educ

en: Education, culture and sport

en: Soil fauna drives SOC storage in a long-term irrigated dry pine forest


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

en: Data Request

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

 Purpose:

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

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

soil-moisture

display_name: soil-moisture

soil-water-content

display_name: soil-water-content

swiss-forest-lab

display_name: swiss-forest-lab

envidat

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

en: TDR soil water content measurements LWF Visp