restoring-grassland-multifunctionality

en:
Please cite this paper together with the citation for the datafile.
Resch, M. C., Schütz, M., Buchmann, N., Frey, B., Graf, U., van der Putten, W. H., Zimmermann, S., Risch, A. C. 2021. Evaluating long-term success in grassland restoration – an ecosystem multifunctionality approach. Ecological Applications 31, e02271.

 Study area
The study was conducted in the Canton of Zurich, Switzerland, in and around two nature reserves Eigental and Altläufe der Glatt (47°27’ to 47°29’ N, 8°37’ to 8°32’ E, 417 to 572 m a.s.l.). All studied grasslands were located with a radius of approximately 4 km. Average monthly temperatures range from 0.7 ± 2.0 °C (January) to 19.0 ± 1.5 °C (July), and monthly precipitation range from 60 ± 42 mm (January) to 118 ± 46 mm (July [maxima]; 1989-2017; MeteoSchweiz 2018). In our study, we focused on semi-dry and semi-wet oligo- to mesotrophic grasslands characterized by high plant species richness and groundwater fluctuations throughout the year (Delarze et al. 2015, see also Resch et al. 2019).
 Experimental design and sampling
A large-scale restoration experiment to expand and reconnect isolated remnants of species-rich grasslands was initiated in the nature reserve Eigental in 1990. Twenty hectares of adjacent intensive grasslands were chosen for restoration. In 1995, three restoration methods of increasing intervention intensities were implemented. The goal of all three methods was to lower the availability of soil nutrients and hence, facilitate ecosystem development towards the targeted nutrient-poor grasslands. These methods were: Harvest only (hay harvest twice a year), Topsoil (removal of the nutrient-rich topsoil), and Topsoil+Propagules (topsoil removal combined with the application of hay from target vegetation). Plant biomass harvest (once a year in late summer/early autumn) commenced in Topsoil and Topsoil+Propagules five years after the soils were removed and is still ongoing today. We measured restoration success by comparing the three restoration methods with intensively managed (Initial) and semi-natural grasslands (Target) 22 years after restoration. Initial grassland sites share the same agricultural history as the restored sites: mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes (Resch et al. 2019). Target sites were the sites from which hay for seeding the Topsoil+Propagules sites was collected. Soil conditions (i.e., soil types, soil texture) were comparable to those found in the restored grasslands (Resch et al. 2019). Additionally, Target sites were selected to represent a variety of semi-natural grasslands, including semi-dry to semi-wet conditions.  In Target grasslands, biomass is harvested once a year in late summer or early autumn. Eleven 5 m x 5 m (25 m2) plots were randomly established in each of the five treatments (in total 55 plots; for a detailed map see Neff et al. 2020). An additional 2 m x 2 m (4 m2) subplot was randomly established at least 2 m away from each 25 m2 plot for destructive sampling. Data sampling took place between June and September 2017.
Vegetation properties
All plant species were identified within the 25 m2 plots (nomenclature: Lauber and Wagner 1996) in mid-June 2017 (in total 250 species). Vegetation structure and plant biomass were assessed diagonally on a transect of 2 m x 10 cm within the 25 m2 plot in early July 2017. We measured the maximum and mean height of the vegetation at the start, middle and end of the transect and calculated the standard deviation of these measures to describe vegetation structural heterogeneity (Schuldt et al. 2019). Thereafter, biomass was clipped on the entire transect to 1 cm height, sorted into five functional groups (graminoids, forbs, legumes, litter, and woody species), dried at 60 °C for 48 h, and weighed (Meyer et al. 2015).
 Aboveground arthropods
Aboveground arthropods were sampled at two locations in each 25 m2 plot in early July 2017 (see also Neff et al. 2020). Briefly, two cylindrical baskets (50 cm diameter, 67 cm height; woven fabric) were thrown simultaneously from outside the plot into two opposite corners. A closable mosquito mesh sleeve was mounted to the top of the baskets and an integrated metal ring at the bottom was fixed to the ground with metal stakes to assure that insects could not escape. A suction sampler (Vortis, Burkhard Manufacturing Co. Ltd., Hertfordshire, England) was then inserted into one of the baskets through the opening of the sleeve and the plot was “vacuumed" twice for 105 seconds with a 30 seconds break. The collected animals were immediately transferred into 70% ethanol. Arthropods were sorted and assigned to 23 taxonomic groups. Holometabolic larvae were lumped into one category while hemimetabolic larvae were grouped separately from adults in the respective taxonomic rank. We used mean values of individuals per plot for total abundance. Aboveground arthropod richness was defined by the number of different taxa to lowest taxonomic level (in total 23 taxa). All taxa were assigned to one of five trophic levels: 1) primary producers, 2) primary consumers, 3) secondary consumers, 4) tertiary consumers, and 5) quaternary consumers.
 Belowground fauna
Sampling of all belowground fauna took place in mid-July 2017. Earthworms were sampled in two 30 cm x 30 cm x 20 cm soil monoliths at two opposite corners of the 25 m2 plot (opposite to aboveground arthropod sampling). The excavated soil monolith was broken by hand, all earthworms collected and immediately transferred in a 4% formaldehyde solution. Thereafter, earthworm individuals were identified to species level (in total 10 taxa; Christian and Zicsi 1999) and species assigned to three functional groups (Bouché 1977).
To assess soil arthropod communities, we randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) in each 4 m2 subplot with a slide hammer corer lined with a plastic sleeve (AMS Samplers, American Falls, Idaho, USA). Soil arthropods were extracted using Berlese-Tullgren funnels (3 mm mesh), starting the day of sampling and lasting 14 days. Individuals were stored in 70% ethanol. Soil arthropods were assigned to 41 taxonomic groups and 4 feeding types. Holometabolic and hemimetabolic larvae were treated as previously described for aboveground arthropods. Belowground arthropod richness refers to the 41 taxonomic groups.
For soil nematode sampling, we randomly collected eight soil cores of 2.2 cm diameter (Giddings Machine Company, Windsor, CO, USA) within each 4 m2 subplot to a depth of 12 cm. The eight cores were combined, gently homogenized, placed in coolers, kept at 4 °C and transported to the laboratory at NIOO in Wageningen (NL) within one week after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink 1960) and prepared for morphological identification and quantification as described by Resch et al. (2019). Nematodes were identified to family level (39 taxa) according to Bongers (1988), assigned to 17 functional groups, 5 feeding types and 5 colonizer-persister (C-P) classes (Yeates et al. 1993, Bongers 1990, Resch et al. 2019).
We randomly collected two more soil cores (2.2 cm diameter x 12 cm depth) within each 4 m2 subplot to determine soil microbial communities. Again, the soil cores were combined, homogenized, placed in coolers and transported to the laboratory at WSL in Birmensdorf (Switzerland) where the metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNeasy PowerMax Soil Kit (Quiagen, Hilden, NRW, GER) according to the manufacturer`s instructions. PCR amplification of the V3-V4 region of the prokaryotic small-subunit (16S) and the ribosomal internal transcribed spacer region (ITS2) of eukaryotes was performed with 1 ng of template DNA utilizing PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates and pooled. The pooled amplicons were sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Quality filtering, clustering into operational taxonomic units (OTUs) and taxonomic assignment were performed as described by Frey et al. (2016) and Adamczyk et al. (2019). We used a customised pipeline largely based on UPARSE (Edgar 2013) implemented in USEARCH v. 9.2 (Edgar 2010). After discarding singletons of dereplicated sequences, clustering into OTUs with 97% sequence similarity was performed (Edgar 2013). Quality-filtered reads were mapped on the filtered set of centroid sequences. Taxonomic classification of prokaryotic and fungal sequences was conducted querying against most recent versions of SILVA (v.132, Quast et al. 2013) and UNITE (v.8, Nilsson et al. 2018). Only taxonomic assignments with confidence rankings equal or higher than 0.8 were accepted (assignments below 0.8 set to unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as eukaryotic OTUs assigned other than fungi were removed prior to data analysis. In addition, prokaryotic and fungal datasets were filtered to discard singletons and doubletons. Thereafter, OTU abundance matrices were rarefied to the lowest number of sequences per community, to normalize the total number of reads and achieve parity between samples (Prokaryota: 29,843 reads; Fungi: 26,690 reads). Finally, prokaryotic and fungal observed richness (number of OTUs) were estimated (Prokaryota: 14,010 OTUs; Fungi: 5,813 OTUs). For prokaryotes, we distinguished five and for fungi six functional types based on lowest taxonomic resolution (Nguyen et al. 2016, Tedersoo et al. 2014).  Belowground taxon richness was defined by the total number of earthworm, arthropod, nematode, fungi, and prokaryote taxa assigned to lowest taxonomic level. Finally, all belowground taxa were assigned to the same five trophic levels as the aboveground arthropods.
 Soil chemical and physical properties, soil nitrogen mineralization
We randomly collected three 5 cm diameter x 12 cm depth soil samples in each 4 m2 subplot with a slide hammer corer (AMS Samplers, American Falls, Idaho, USA), pooled them and then made two subsamples. One was field-fresh and stored at 3 °C until analysis, the other was dried for 48 h at 60 °C and passed through a 4 mm mesh. From the dried sample, we measured soil pH potentiometrically in 0.01 M CaCl2 (soil:solution ratio=1:2; 30 minutes equilibration time). Total and organic carbon content were measured on fine-ground samples (≤ 0.5 mm) by dry combustion using a CN analyzer NC 2500 (CE Instruments, Wigan, United Kingdom). Inorganic carbon of samples with a pH - 6.5 was removed with acid vapor prior to analysis of organic carbon (Walthert et al. 2010). We calculated total soil carbon (C) storage after correcting its content for soil depth, stone content and density of fine earth (see below). Exchangeable cations were determined on another 5 g dry soil sample with 50 mL unbuffered 1 M NH4Cl solution (soil:solution ratio=1:10, end-over-end shaker for 1.5 hours) and measured by an ICP-OES (Optima 7300 DV, Perkin-Elmer, Waltham, Massachusetts, USA). Thereafter, cation exchange capacity (CEC) was calculated as the sum of exchangeable cations and protons (and expressed as mmolc per 1 kg soil) and used to describe nutrient retention capacity in our plots. Concentrations of exchangeable protons were calculated as the difference between total and Al-induced exchangeable acidity as determined by the KCl-method (Thomas 1982).
Ammonium (NH4+) and nitrate (NO3−) were extracted from a 20 g fresh subsample with 80 mL 1M KCl for 1.5 hours on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnemühle FineArt GmbH, Dassel, Germany). NH4+ concentrations were determined colorimetrically by automated flow injection analysis (FIAS 300, Perkin-Elmer, Waltham, Massachusetts, USA). NO3− concentrations were measured colorimetrically according to Norman and Stucki (1981). Potential soil net nitrogen (N) mineralization was assessed during an 8-week incubation period under controlled moisture (60% of field capacity), temperature (20 °C) and light conditions (dark) in the laboratory. We weighed duplicate samples of fresh soil equivalent to 8 g dry soil (24 h at 104 °C) into 50 mL Falcon tubes. Soil samples were extracted for NH4+ and NO3− at the beginning and after eight weeks as described above. Soil net N mineralization was calculated as the difference between the inorganic nitrogen (NH4+ and NO3−) before and after the incubation (Hart et al. 1994), corrected for the total incubation time and represented per day values expressed as mg N kg-1 soil d-1.
To assess soil physical properties, we randomly collected one undisturbed soil core per 4 m2 subplot (5 cm diameter, 12 cm depth) in a steel cylinder that fit into the slide hammer (AMS Samplers, American Falls, Idaho, USA). The cylinder was capped in the field to avoid disturbance. We then measured field capacity in the laboratory. For this purpose, the cylinder and soil therein were saturated in a water bath and drained on a sand/silt-bed with a suction corresponding to 60 cm hydrostatic head. The moist soil was dried at 105 °C to constant weight. Field capacity was calculated by dividing the mass of water by the total mass of wet soil contained at 60 cm hydrostatic head and used to describe water holding capacity. Thereafter, samples were passed through a 4 mm mesh. Fine-earth and skeleton fractions were weighed separately to assess bulk soil density (fine-earth plus skeleton), density of fine earth, and proportion of skeleton. Particle density was determined with the pycnometer method (Blake and Hartge 1986), and total porosity and proportion of fine pores were calculated (Danielson and Sutherland 1986). Clay, silt, and sand contents were quantified with the sediment method (Gee and Bauder 1986).
Surface and soil temperature (12 cm depth, water-resistant digital pocket thermometer; IP65, H-B Instrument, Trappe, Pennsylvania, USA) as well as volumetric soil moisture content (12 cm depth, time domain reflectometry; Field-Scout TDR 300, Spectrum Technologies, Aurora, Illinois, USA) were measured at five random locations within the 4 m2 subplots every month from June to September. We calculated the standard deviation of each temperature and moisture measure over four months to describe seasonal variations. Slope inclination was determined at plot-level via GPS measurements (GPS 1200, Leica Geosystem, Heerbrugg, Switzerland) and categorized into slope gradient classes according to FAO standards (1990). Thickness of the topsoil horizon (equivalent to Ah or Aa horizon) was determined at one soil monolith (30 x 30 x 30 cm3) per 4 m2 subplot in cm and rounded to next integer.
 References
Adamczyk, M., F. Hagedorn, S. Wipf, J. Donhauser, P. Vittoz, C. Rixen, A. Frossard, J. Theurillat, and B. Frey. 2019. The soil microbiome of GLORIA mountain summits in the Swiss Alps. Frontiers in Microbiology 10:1080-1101.
Blake, G.R., and K. H. Hartge. 1986. Particle Density. Pages 377-382 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.
Bongers, T. 1988. De nematoden van Nederland. Stichting Uitgeverij van de Koniklijke Nederlandse Natuurhistorische Verenigung (KNNV), Utrecht.
Bongers, T. 1990. The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia 83:14-19.
Bouché, M. B. 1977. Strategies lombriciennes. Ecological Bulletins 25:122-132.
Christian, E., and A. Zicsi. 1999. Ein synoptischer Bestimmungsschlüssel der Regenwürmer Österreichs (Oligochaeta: Lumbricidae). Die Bodenkultur 50:121-131.
Danielson, R. E., and P. L. Sutherland. 1986. Porosity. Pages 443-461 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.
Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie ‐ Gefährdung ‐ Kennarten, Ott Verlag, Bern.
Edgar, R. C. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461.
Edgar, R. C. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods 10:996–998.
FAO. 1990. Guidelines for soil description, third ed. Land and Water Development Division at the Food and Agriculture Organization of the United Nations (FAO), Rome.
Frey, B., T. Rime, M. Phillips, B. Stierli, I. Hajdas, F. Widmer, and M. Hartmann. 2016. Microbial diversity in European alpine permafrost and active layers. FEMS Microbiology Ecology 92:fiw018.
Gee, G.W., and J. W. Bauder. 1986. Particle-size analysis. Pages 383-411 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.
Hart, S. C, J. M. Stark, E. A. Davidson, and M. K. Firestone. 1994. Nitrogen mineralization, immobilization, and nitrification. Pages 985-1016 in R. W. Weaver, S. Angle, P. Bottomley, D. Bezdicek, S. Smith, A. Tabatabai, and A. Wollum, editors. Methods of soil analysis: Part 2—Microbiological and biochemical properties. Soil Science Society of America (SSSA) Inc., Madison.
Lauber, K., Wagner, G., 1996. Flora Helvetica. Flora der Schweiz. Haupt Verlag, Bern.
MeteoSchweiz, 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Zürich.
Meyer, S. T., C. Koch, and W. W. Weisser. 2015. Towards a standardized rapid ecosystem function assessment (REFA). Trends in Ecology and Evolution 30:390-397.
Neff, F., M. C. Resch, A. Marty, J. Rolley, M. Schütz, A. C. Risch, and M. M. Gossner. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands – a functional approach. Ecological Applications 0:e02133
Nguyen, N.H., Z. W. Song, S. T. Bates, S. Branco, L. Tedersoo, J. Menke, J. S. Schilling, and P. G. Kennedy. 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20:241–248.
Nilsson, R. H., K.-H. Larsson, A. F. S. Taylor, J. Bengtsson-Palme, T. S. Jeppesen, D. Schigel, P. G. Kennedy, K. Picard, F. O. Glöckner, L. Tedersoo, I. Saar, U. Kõljalg, and K. Abarenkov. 2018. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research 47:259–264.
Norman, R. J., and J. W. Stucki. 1981. The Determination of Nitrate and Nitrite in Soil Extracts by Ultraviolet Spectrophotometry. Soil Science Society of America Journal 45:347-353.
Oostenbrink, M. 1960. Estimating nematode populations by some selected methods. Pages 81-101 in J. J. Sasser, and W. R. Jenkins, editors. Nematology. Univ. of North Carolina Press, Chapel Hill.
Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O. Glöckner. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41:590-596.
Resch, M. C., M. Schütz, U. Graf, R. Wagenaar, W.H. van der Putten, and A. C. Risch. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56:1782-1793.
Schuldt, A., A. Ebeling, M. Kunz, M. Staab, C. Guimarães-Steinicke, D. Bachmann, N. Buchmann, W. Durka, A. Fichtner, F. Fornoff, W. Härdtle, L. R. Hertzog, A-N. Klein, C. Roscher, J. Schaller, von G. Oheimb, A. Weigelt, W. Weisser, C.Wirth, J. Zhang, H. Bruelheide, and N. Eisenhauer. 2019. Multiple plant diversity components drive consumer communities across ecosystems. Nature Communications 10:1460.
Tedersoo, L., M. Bahram, S. Põlme, U. Kõljalg, N. S. Yorou, R. Wijesundera, L. Villarreal Ruiz, A. M. Vasco-Palacios, P. Q. Thu, A. Suija, M. E. Smith, C. Sharp, E. Saluveer, A. Saitta, M. Rosas, T. Riit, D. Ratkowsky, K. Pritsch, K. Põldmaa, M. Piepenbring, C. Phosri, M. Peterson, K. Parts, K. Pärtel, E. Otsing, E. Nouhra, A. L. Njouonkou, R. H. Nilsson, L. N. Morgado, J. Mayor, T. W. May, L. Majuakim, D. J. Lodge, S. See Lee, K.-H. Larsson, P. Kohout, K. Hosaka, I. Hiiesalu, T. W. Henkel, H. Harend, L.-D. Guo, A. Greslebin, G. Grelet, J. Geml, G. Gates, W. Dunstan, C. Dunk, R. Frenkhan, L. Dearnaley, A. De Kesel, T. Dang, X. Chen, F. Buegger, F. Q. Brearley, G. Bonito, S. Anslan, S. Abell, and K. Abarenkov. 2014. Global diversity and geography of soil fungi. Science 346:1256688.
Thomas, G.W. 1982. Exchangeable cations. Pages 159-165 in A. L. Page, R. H. Miller, and D. R. Keeney, editors. Methods of Soil Analysis: Part 2—Chemical and microbiological properties. Soil Science Society of America (SSSA) Inc., Madison.
Walthert, L., U. Graf, A. Kammer, J. Luster, D. Pezzotta, S. Zimmermann, and F. Hagedorn. 2010. Determination of organic and inorganic carbon, δ13C, and nitrogen in soils containing carbonates after acid fumigation with HCl. Journal of Plant Nutrition and Soil Sciences 173:207-216.
Yeates, G. W., T. Bongers, R. G. M. de Goede, D. W. Freckman, and S. S. Georgieva. 1993. Feeding habits in soil nematode families and genera – an outline for soil ecologists. Journal of Nematology 25:315-331.

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

en: Restoring grassland multifunctionality

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This dataset contain measurements of solute and stable water isotopes in stream water and precipitation in the Alp catchment and two of its tributaries (between 2015 -2018) . The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l.  The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (precipitation rates, discharge) at daily intervals from several sampling locations.
An updated version of the isotope dataset is available here: https://www.doi.org/10.16904/envidat.242

en: stable-water-isotopes stream-water spatially-distributed-sampling geochemical-solutes precipitation

envidat

approval_status: approved

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.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: Daily data of solute and stable water isotopes in stream water and precipitation in the Alp catchment, Central Switzerland

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

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    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/53f4e1d8-506e-403d-b874-e294c6e8b2eb/resource/7ae45f32-4411-4ad9-ac84-fd8f28dfc933/download/01.daily.isotope.solute.database.alptal-ch-13.05.2019_envidat.xlsx
    Description: The dataset contains daily measurements of volumes, isotopes and solutes of stream water as well as precipitation measured several locations; meteorological variables are added.

geochemical-solutes

display_name: geochemical-solutes

precipitation

display_name: precipitation

spatially-distributed-sampling

display_name: spatially-distributed-sampling

stable-water-isotopes

display_name: stable-water-isotopes

stream-water

display_name: stream-water


en:
This data contain volumes, solutes and isotopes of snowpack outflow measured by a snowmelt lysimeter system at three locations in the southern Alp catchment, situated Central Switzerland.  The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l.
The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (snowpack outflow volumes) at daily intervals from several sampling locations. Additionally, the data measured by the snowmelt lysimeter system are provided in 10-minute resolution.

en: snowmelt-lysimeter stable-water-isotopes snowpack-outflow solutes snowmelt-rate

en: Daily data of the volumes, solutes and isotopes in snowpack outflow measured at three locations in the southern Alp catchment

en:
Resource Title: Daily-Snowmelt.Lysimeter-solute-and-isotope-database_Version2
    Size: 42211381 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/d234a921-9000-4ffc-b723-a663d390b9f8/resource/cb7620a7-6854-4dd9-b11d-290395d64f4b/download/lysimeterdatabase.alptal-ch_27062019_level02.xlsx
    Description: The dataset contains volumes, solutes and stable water isotopes of snowpack outflow at daily resolution.  10-minute data are provided of the measurements taken by three snowmelt lysimeter systems at spatially-distributed locations.

en:
Resource Title: Daily-Snowmelt.Lysimeter-solute-and-isotope-database_Version1
    Size: 3017002 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/d234a921-9000-4ffc-b723-a663d390b9f8/resource/db3801e0-1b14-4290-ab6a-3f1148e9e0ed/download/envidat-daily.isotope.solute.database.alptal-ch-18.07.2019_level02.xlsx
    Description: The dataset contains volumes, solutes and stable water isotopes of snowpack outflow at daily resolution.  10-minute data are provided of the measurements taken by three snowmelt lysimeter systems at spatially-distributed locations.

snowmelt-lysimeter

display_name: snowmelt-lysimeter

snowmelt-rate

display_name: snowmelt-rate

snowpack-outflow

display_name: snowpack-outflow

solutes

display_name: solutes


en:
This dataset contains all snow, canopy and meteorological data presented and used in the publication: Mazzotti, G., Essery, R., Moeser, D. & Jonas T. (2020) 'Resolving spatial variability of forest snow using an energy-balance model with a 1-layer canopy'. Water Resources Research, https://doi.org/10.1029/2019WR026129.
This publication must be cited when using this dataset.

en: snow-modelling forest-structure snow-energy-balance forest-snow snow-distribution

en: Snow depth, canopy structure and meterorological datasets from the Davos area, Switzerland, Winters 2012/13-2014/15, used for high-resolution forest snow modelling

envi

en: Environment

en:
Resource Title: Canopy data
    Size: 383962 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/5369a1d1-dbdd-4397-8409-8b7105b79e8b/resource/11c64977-4469-4786-93e1-d8f0a897bbc9/download/canopy_data.xlsx
    Description: Includes all canopy structure metrics computed at all surveyed locations

en:
Resource Title: Meteo data to drive FSM2 model
    Size: 730523 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5369a1d1-dbdd-4397-8409-8b7105b79e8b/resource/f03a1a6e-261c-4d63-b516-e722ebb0ca8d/download/meteo_data.zip
    Description: Includes meteorological driving data used to run the forest snow model FSM2, processed and parsed in text files that match the format required by FSM2. Separate files are available for each field area (Laret, Drusatscha and Ischlag) and season (2012/13, 2013/14, 2014/15). Each file includes data for the period from October 1st to May 31st at hourly resolution.

format: ZIP

media_type: application/zip

en:
Resource Title: Snow depth data open sites
    Size: 18865 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/5369a1d1-dbdd-4397-8409-8b7105b79e8b/resource/cf970b76-a538-4b2b-b478-d8458dbec9cf/download/hs_data_open.xlsx
    Description: Contains all snow detph data accquired at open sites nearby the forest stands during the same period.

en:
Resource Title: Snow depth data forest sites
    Size: 326390 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/5369a1d1-dbdd-4397-8409-8b7105b79e8b/resource/4a465ad6-8cc2-440b-b211-a4b68b37adde/download/hs_data_forest.xlsx
    Description: Contains all snow depth data acquired at all forest sites during winters 2012/13, 2013714 and 2014/15.

en:
Resource Title: Coordinates data
    Size: 70111 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/5369a1d1-dbdd-4397-8409-8b7105b79e8b/resource/a3df4cb0-7f75-46e5-ae50-ffc0c2fa2a7a/download/coordinates_data.xlsx
    Description: Includes coordinate information of each surveyed point (7 x 276 = 1932 forest points, 2 open areas)

en:
Resource Title: Grid point nomenclature
    Size: 16191 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/5369a1d1-dbdd-4397-8409-8b7105b79e8b/resource/af731be9-f29a-4ff3-89a4-d7c042cde321/download/grid_point_nomenclature.xlsx
    Description: The grid structure of each forest site and the respective nomenclature applied to label individual points within each grid are outlined here.   Each point marks the location of snow depth measurements and co-located canopy structure parameters.

forest-snow

display_name: forest-snow

forest-structure

display_name: forest-structure

snow-distribution

display_name: snow-distribution

snow-energy-balance

display_name: snow-energy-balance

snow-modelling

display_name: snow-modelling


en:
This dataset includes snow depth, canopy height and terrain elevation maps of forest stands in the Grisons (CH) and at Grand Mesa (CO,USA) derived from airborne lidar. Data were acquired i) within a pilot mission of NASA's Airborne Snow Observatory in the Swiss Alps in March 2017 and ii) during NASA’s SnowEx campaign at Grand Mesa in February 2017. Snow depth maps are available for two dates separated by approx.1 week, and include an area of ca. 0.5km2 for each of the three sites Davos, Engadine and Grand Mesa.
All data were presented and analyzed in the publication 'Revisiting Snow Cover Variability and Canopy Structure within Forest Stands: Insights from Airborne Lidar Data' (Mazzotti et al., 2019, WRR, doi: 10.1029/2019WR024898). This publication must be cited when using this dataset.
Paper Citation:
 - _Giulia Mazzotti; William Ryan Currier; Jeffrey S. Deems; Justin M. Pflug; Jessica D. Lundquist; Tobias Jonas (2019). Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data. Water Resources Research, 55, 6198– 6216,  [doi: 10.1029/2019WR024898](https://doi.org/10.1029/2019WR024898)._

en: canopy-height airborne-laser-scanning snow-distribution forest-structure snow-depth

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: EnviDat: the Environmental Data Portal

en: ALS-based snow depth and canopy height maps from flights in 2017 (Grisons, CH and Grand Mesa, CO)

en:
Resource Title: Canopy height maps
    Size: 4159760 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/98ab851f-7d0e-4c96-9eea-79db59cd83a2/resource/3451cb50-b6af-4df6-8b73-aeda7eb01215/download/canopy-height.zip
    Description: Canopy height maps of each of the three field areas Davos, Engadine and Grand Mesa

en:
Resource Title: Snow depth maps
    Size: 3113408 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/98ab851f-7d0e-4c96-9eea-79db59cd83a2/resource/e41d41ca-810c-4ec6-87ef-6daa709f8316/download/snow-depth.zip
    Description: Snow depth maps from two snow-on flights at each of the three field areas Davos (March 20th and 31st 2017), Engadien (March 20th and 31st, 2017) and Grand Mesa (February 8th and 16th, 2017)

en:
Resource Title: Readme
    Size: 997 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/98ab851f-7d0e-4c96-9eea-79db59cd83a2/resource/a8109354-2581-4863-bcf5-301f3dc361a8/download/readme.txt
    Description:

format: TXT

media_type: text/plain

en:
Resource Title: Terrain elevation maps
    Size: 2545725 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/98ab851f-7d0e-4c96-9eea-79db59cd83a2/resource/9c82946c-09f9-4815-974d-31828e45f439/download/terrain-elevation.zip
    Description: Digital elevation model computed from snow-off flights for each of the field areas Davos, Engadine and Grand Mesa

airborne-laser-scanning

display_name: airborne-laser-scanning

canopy-height

display_name: canopy-height

snow-depth

display_name: snow-depth


en:
Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds.
All metadata can be found in the attached documents.

en: photosynthesis gas-exchange acclimation pinus-sylvestris vpd fluorescence drought

en: 2013-2020 gas exchange at Pfynwald

en:
Resource Title: Methods ACi curves
    Size: 731 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/c7536938-120e-4975-97e4-e40649637634/download/methodsmetadata_acicurves.csv
    Description: Parameters and methods for CO2 response curves of Photosynthesis.

Metatdata format by
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

format: CSV

media_type: text/csv

en:
Resource Title: Methods survey
    Size: 596 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/229fbf92-a0ff-44b5-b799-b55fabe2b99c/download/methodsmetadata_point.csv
    Description: Parameters and methods for point measurements / survey measurements

en:
Resource Title: Methods light response
    Size: 607 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/c5ef8bc5-79ab-4aec-9e02-d031e1e7d976/download/methodsmetadata_lightcurves_darkresp.csv
    Description: Methods and parameters for light response curves

en:
Resource Title: Methods fluorescence
    Size: 534 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/1d2343e4-bdf9-4ff0-9582-0c9ef11e139e/download/methodsmetadata_fluoresence.csv
    Description: Parameters and methodology for Fluorescence measurements

Metadata format from
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en:
Resource Title: Instrument details
    Size: 1174 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/e7342279-bf44-451b-afa2-78cb77babf5e/download/instrumentdetails_pfynwald.csv
    Description: Instrument details for all LiCor instruments used during collection of dataset.

Metatdata created following guidelines from
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en:
Resource Title: Methods diurnal measurements
    Size: 593 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/45f2d648-386c-42bb-b766-6f804e7c7de6/download/methodsmetadata_diurnal.csv
    Description: Parameters and methodology for diurnal measurements

Metadata format from
Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en:
Resource Title: Pfynwald gas exchange
    Size: 4072706 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/3d0f2a0a-c365-4f15-a8e7-312943cbcad6/download/pfynwald_gasexchange_201306_202010.csv
    Description: Full dataset of Pfynwald gas exchange data taken in the period from June 2013 to October 2020.
All metdata can be found in attached documents.
Data uploaded after quality control using guidelines by Ely et al (2021). A reporting format for leaf-level gas exchange data and metadata. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2021.101232

en:
Resource Title: Variable names and units
    Size: 15839 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/bfedd747-6d88-44b4-8aba-3094af8766eb/resource/39614a56-a982-4123-a8d6-e9aaa51c838a/download/variable_names_units.xlsx
    Description: Variable names, description and units for the Pfynwald Gas exchange data

acclimation

display_name: acclimation

drought

display_name: drought

fluorescence

display_name: fluorescence

gas-exchange

display_name: gas-exchange

photosynthesis

display_name: photosynthesis

pinus-sylvestris

display_name: pinus-sylvestris

vpd

display_name: vpd


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

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

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: EnviDat: the Environmental Data Portal

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

envi

en: Environment

en:
Resource Title: Barcelona_S1
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    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/2a6f3cb5-a88e-4abe-865a-12a6fa2bd838/download/barcelona_s1.asc
    Description:

format: pgp-keys

en:
Resource Title: Barcelona_S3
    Size: 51044440 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/78f91bc4-cec9-4e30-9db9-a1c2cb4ba546/download/barcelona_s3.asc
    Description:

en:
Resource Title: Valencia_S2
    Size: 6759489 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/af2a0484-7e5e-4cc3-963c-c14ef137131a/download/valencia_s2.asc
    Description:

en:
Resource Title: Madrid_S2
    Size: 25343034 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/5d6fb94e-2557-4428-aae5-30aebd2048a1/download/madrid_s2.asc
    Description:

en:
Resource Title: Zaragoza_S1
    Size: 20968481 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/bf187ffd-59a7-4018-b7f8-d843cd03737e/download/zaragoza_s1.asc
    Description:

en:
Resource Title: Legend simulated maps
    Size: 906 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/927372ee-e8b2-4f36-abc3-7367b112b4fb/download/legend.txt
    Description:

en:
Resource Title: Valencia_S4
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    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/ad431d08-3481-4b61-9d4c-a7b75e6c04e9/download/valencia_s4.asc
    Description:

en:
Resource Title: Madrid_S3
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    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/dfb0ce17-3c3c-4c40-a406-5fbbd69d7859/download/madrid_s3.asc
    Description:

en:
Resource Title: Zaragoza_S2
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    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/7b5ad8ca-1f63-4492-89ad-d8e902c4b102/download/zaragoza_s2.asc
    Description:

en:
Resource Title: Barcelona_S4
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    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/0da0d9eb-d2a7-41b0-a050-1a34770fc7e1/download/barcelona_s4.asc
    Description:

en:
Resource Title: Madrid_S4
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    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/21a40989-9a12-403a-9dff-15d37a59caf6/download/madrid_s4.asc
    Description:

en:
Resource Title: Madrid_S1
    Size: 25387983 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/bc4b8ca6-76a2-43b0-9a5b-4c43394b3fda/download/madrid_s1.asc
    Description:

en:
Resource Title: Barcelona_S2
    Size: 51045666 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/a555ffc0-0266-4987-b70e-e7fddb0abdf2/download/barcelona_s2.asc
    Description:

en:
Resource Title: Zaragoza_S3
    Size: 20961753 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/46cd42ea-2ea9-4881-9056-0c80433cdced/download/zaragoza_s3.asc
    Description:

en:
Resource Title: Zaragoza_S4
    Size: 20914512 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/c5dc051d-16e0-49cc-99cb-3bc105aaa390/download/zaragoza_s4.asc
    Description:

en:
Resource Title: Valencia_S1
    Size: 6769425 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/b0e6fefc-b2ca-48ee-bbaf-a478e4f027ee/download/valencia_s1.asc
    Description:

en:
Resource Title: Valencia_S3
    Size: 6757947 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/572a6da6-b2fd-4635-bba6-dd2756c4632c/resource/94d52c98-ae61-4a1d-8b51-75d8b2994a04/download/valencia_s3.asc
    Description:

digital-zoning-plans

display_name: digital-zoning-plans

land-use-change

display_name: land-use-change

land-use-simulations

display_name: land-use-simulations

land-use-transformations

display_name: land-use-transformations

spatial-planning

display_name: spatial-planning


en: Static input data (topography, landuse and soiltype) for the WRF preprocessing system WPS is provided for Switzerland and its neighboring countries between 45-49 N and 4-12 E. The data is provided at a resolution of 1 s. Topography is based on the Aster dataset, while landuse is extracted from the Corine landuse dataset. Soil type is set to silty clay loam for the entire domain. This static input data is valid for WRF and CRYOWRF.

en: cryowrf wrf topography static-data landuse cosmo-wrf

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: EnviDat: the Environmental Data Portal

en: High resolution static data for WRF over Switzerland

envi

en: Environment

en:
Resource Title: landuse_1s
    Size: 21215915 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d3db35e-eb6b-4e7f-a88e-733c363133c9/resource/e3ef8012-d73d-4ad4-b51e-b327f5a6bcf9/download/landuse_1s.zip
    Description: Static data for WRF - Landuse

en:
Resource Title: Documentation
    Size: 166718 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/9d3db35e-eb6b-4e7f-a88e-733c363133c9/resource/793e2a23-a8a5-4bbc-8497-d00eb2b9f28a/download/documentation-wrf_static_data_for_alps.pdf
    Description: Documentation of the dataset

format: PDF

media_type: application/pdf

en:
Resource Title: soiltype_bot_1s
    Size: 403644 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d3db35e-eb6b-4e7f-a88e-733c363133c9/resource/c63e5b14-d5b7-45f5-ba0d-57bc72394e0e/download/soiltype_bot_1s.zip
    Description: Static data for WRF - Soiltype bottom

en:
Resource Title: namelist.wps
    Size: 1411 bytes
    Format: application/vnd.ms-works
    URL: https://www.envidat.ch/dataset/9d3db35e-eb6b-4e7f-a88e-733c363133c9/resource/2b73cbec-bfba-4788-bfd8-5cf6dae371dd/download/namelist.wps
    Description: Example namelist.wps to use with the 1s static data

en:
Resource Title: topo_1s
    Size: 458474836 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d3db35e-eb6b-4e7f-a88e-733c363133c9/resource/ab9f6e89-9101-4250-bb4c-b8e5e8e2363f/download/topo_1s.zip
    Description: Static data for WRF - Topography

en:
Resource Title: soiltype_top_1s
    Size: 403644 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d3db35e-eb6b-4e7f-a88e-733c363133c9/resource/33bc67ca-6ee6-440e-985b-46ce84e724b0/download/soiltype_top_1s.zip
    Description: Static data for WRF - Soiltype top

en:
Resource Title: GEOGRID.TBL-aster-121-1
    Size: 15835 bytes
    Format: No Info
    URL: https://www.envidat.ch/dataset/9d3db35e-eb6b-4e7f-a88e-733c363133c9/resource/246daa44-7f9a-4666-9aba-e8dbc244c148/download/geogrid.tbl-aster-121_1
    Description: Example GEOGRID.TBL to use with the 1s static data;
Note about naming:
* 121 -> 1-2-1 topography smoothing applied to dataset
* 1 -> one smoothing cycle applied to the data (needs to be changed depending on the chosen resolution to avoid too steep slope angles)

cosmo-wrf

display_name: cosmo-wrf

cryowrf

display_name: cryowrf

landuse

display_name: landuse

static-data

display_name: static-data

topography

display_name: topography

wrf

display_name: wrf


en:
Data on dissolved organic and inorganic phosphorus and nitrogen concentrations in leachates and their corresponding
fluxes from the litter layer, the Oe/Oa horizon, and the A horizon of two German beech forest sites.
Leachate samples were taken in April 2018, July 2018, October 2018, Feb./Mar. 2019, and July 2019
with zero-tension lysimeters after artificial irrigation. Soil samples were taken in July 2019.
For more details please refer to the publication.

en: nitrogen soil-temperature dissolved-organic-phosphorus zero-tension-lysimeters phosphorus nutrient-fluxes seasons beech forest-topsoil fertilization

en: Phosphorus and nitrogen leaching from beech forest soils

beech

display_name: beech

dissolved-organic-phosphorus

display_name: dissolved-organic-phosphorus

fertilization

display_name: fertilization

forest-topsoil

display_name: forest-topsoil

nitrogen

display_name: nitrogen

nutrient-fluxes

display_name: nutrient-fluxes

phosphorus

display_name: phosphorus

seasons

display_name: seasons

soil-temperature

display_name: soil-temperature

zero-tension-lysimeters

display_name: zero-tension-lysimeters


en:
In 2013, the Institute for Applied Plant Biology (IAP) started a monitoring programme to study the development and the spatial variation of the ash dieback disease with the aim to find some partially resistant European ash trees (Fraxinus excelsior).
We collaborate as co-authors for the publication: Spread and Severity of Ash Dieback in Switzerland - Tree Characteristics and Landscape Features Explain Varying Mortality Probability (Klesse et al. 2021 in frontiers)

en: iap monitoring fraxinus ash-dieback forest

en: Monitoring of ash trees as part of the Intercantonal Forest Observation Programme

ash-dieback

display_name: ash-dieback

forest

display_name: forest

fraxinus

display_name: fraxinus

iap

display_name: iap

monitoring

display_name: monitoring


en:
This poster was originally created for the swissuniversities Open Science Action Plan: Kick-Off Forum, and showed to the audience on 17.10.2019. It illustrates how the environmental data portal EnviDat provides the tools for fostering Open Science and Reproducibility of scientific research at WSL.
Supporting open science is a highly relevant user requirement for EnviDat and for implementing FAIR (Findability, Accessibility, Interoperability and Reusability) principles at dataset level.
EnviDat encourages WSL scientists to complement data publication with a complete description of research methods and the inclusion of the open source software, code or scripts used for processing the dataset or for obtaining the published results. By openly publishing open software (e.g. as Jupyter notebooks) alongside research data sets, researchers can contribute to mitigate reproducibility issues.
EnviDat also promotes and supports, where possible and practical, the publication of software as Jupyter notebooks. Jupyter notebooks provide a solution for improved documentation and interactive execution of open code in a wide range of programming languages (Python, R, Octave/Matlab, Java or Scala). These programming languages are widely used in environmental research at WSL and well supported by the Jupyter-compatible kernels. We have sucessfully interfaced EnviDat-hosted notebooks with the WSL High-Performance Computing (HPC) Linux Cluster through a JupyterHub/JuypterLab beta installation on the HPC cluster implemented in close collaboration with the WSL IT-Services.
For existing software that cannot be easily migrated to Jupyter Notebooks, the Open Science and Reproducibility is assisted by containerisation. We have proven that several Singularity containers can successfully run on WSL's HPC cluster.
Finally, the researchers can upload the data/results complemented by code (e.g. as Jupyter Notebooks, or Singularity containers) and any additional documentation in EnviDat. Consequently, they will receive a DOI for the entire dataset, which they can reference in their science paper in order to publish a more reproducible research.
_License_: This poster is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 "No Rights Reserved" international license. You can reuse this poster in any way you want, for any purposes and without restrictions.

en: open-science reproducibility open-source hpc jupyter-notebooks open-data research-data-management

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: Open Science Support at the Swiss Federal Research Institute WSL. The EnviDat Concept

hpc

display_name: hpc

jupyter-notebooks

display_name: jupyter-notebooks

open-data

display_name: open-data

open-science

display_name: open-science

open-source

display_name: open-source

reproducibility

display_name: reproducibility

research-data-management

display_name: research-data-management


en: We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland,  sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

en: switzerland cropland phenology remote-sensing agriculture vegetation-indices habitat grassland machine-learning sentinel-2

envidat

en: EnviDat: the Environmental Data Portal

en: Cropland and grassland map of Switzerland based on Sentinel-2 data

envi

en: Environment

en:
Resource Title: Cropland and grassland map of Switzerland  (v.1.0)
    Size: 773453532 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/0bb5fc4c-8331-4851-81fb-6c8b5f331207/download/ch_cropgrassmap_v2_4.tif
    Description: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; other LC classes are masked out from the map

format: TIFF

media_type: image/tiff

en:
Resource Title: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6  (v1.0)
    Size: 20469824 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/b358e0ad-50c8-45a8-b4fd-98ccf6d59532/download/comb_sen2maskbuiltup06.tif
    Description: !!! Please check out the newer version in the repository !!!
Mask of unsealed surfaces of Switzerland based on 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6

en:
Resource Title: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6 (v1.5)
    Size: 21028763 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/b583e418-92c3-4e34-9f1b-de7f8823234c/download/comb_sen2maskbuiltup06.tif
    Description: Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6.

en:
Resource Title: (current version) Cropland, grassland, and shrubland map of Switzerland (v.1.5)
    Size: 46196707 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/014565ae-748b-4f64-bf14-53443386cf56/download/ch_cropgrassshrubmap_v1_5.tif
    Description: (1) cropland; (2) grassland; (3) shrubland; other LC classes are masked out from the map

en:
Resource Title: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.5)
    Size: 22951009 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/1433230e-25d2-40e4-b512-f9216f158c62/download/comb_sen2maskbuiltup07.tif
    Description: Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.7.

en:
Resource Title: Cropland, grassland, and shrubland map of Switzerland (v1.0)
    Size: 773453532 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/63a7c7f1-af24-49eb-a082-2f9244034b19/download/ch_cropgrassmap_v2_4_withshrubs.tif
    Description: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; (3) shrubland; other LC classes are masked out from the map

en:
Resource Title: (current version) Cropland and grassland map of Switzerland (v.1.5)
    Size: 36873826 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/05a74913-fbe7-477a-9d8a-d1e4f97c30c2/download/ch_cropgrassmap_v1_5.tif
    Description: (1) cropland; (2) grassland; other LC classes are masked out from the map

en:
Resource Title: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.5)
    Size: 12925 bytes
    Format: text/javascript
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/44ff8fb6-ac93-4278-a645-9a1e81d1ce2f/download/sentinel12_wholech_croplandgraslandshrubland_published_v2.js
    Description: The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland. Compare to the previous version the newer version includes the shadow masking procedure which might be important in the mountainous areas.

format: javascript

en:
Resource Title: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.0)
    Size: 10103 bytes
    Format: text/javascript
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/5b8620ec-a059-4f05-a131-c559049e5a44/download/sentinel12_wholech_croplandgraslandshrubland_published_v1.js
    Description: !!! Please check out the newer version in the repository !!! The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland

en:
Resource Title: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.0)
    Size: 22351282 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/00ff9ac3-c96b-4cb5-a300-b205ad9698cc/resource/4643ef04-e299-4ed2-b03d-f16624ce2f29/download/comb_sen2maskbuiltup07.tif
    Description: !!! Please check out the newer version in this repository !!! Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.7.

agriculture

display_name: agriculture

cropland

display_name: cropland

grassland

display_name: grassland

habitat

display_name: habitat

machine-learning

display_name: machine-learning

phenology

display_name: phenology

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-indices

display_name: vegetation-indices


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

en: switzerland studibach groundwater alptal time-series

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

alptal

display_name: alptal

groundwater

display_name: groundwater

studibach

display_name: studibach

time-series

display_name: time-series


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

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

envidat

en: EnviDat: the Environmental Data Portal

en: Predicted cloud droplet numbers Davos Wolfgang

en:
Resource Title: Predicted cloud droplet numbers (kappa=0.25) Weissfluhjoch
    Size: 313834 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e5a506af-7ff0-47ad-afe2-36de4b4e1ea9/resource/b5c4c596-2dd4-45f4-b79f-61f91d82aad0/download/predicted_droplets_wop_kappa0.25.csv
    Description: In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.25 is assumed, which is the upper limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

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

en:
Resource Title: Predicted cloud droplet numbers (kappa=0.1) Davos Wolfgang
    Size: 312210 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e5a506af-7ff0-47ad-afe2-36de4b4e1ea9/resource/245ebcc2-4dd5-4339-9f76-b61411fa103b/download/predicted_droplets_wop_kappa0.1.csv
    Description: In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.1 is assumed, which is the lower limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

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

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

wolfgangpass

display_name: wolfgangpass


en:
Cloud Condensation Nuclei (CCN) data: A Droplet Measurement Technologies (DMT) single-column continuous-flow streamwise thermal gradient chamber (CFSTGC; Roberts and Nenes, 2005) was deployed at the measurement site Weissfluhjoch (2700 m a.s.l., LON: 9.806475, LAT: 46.832964) to record the in-situ CCN number concentrations between February 24 and March 8 2019 for different supersaturations (SS). To account for the difference between the ambient (~735 mbar) and the calibration pressure (~800 mbar), the SS reported by the instrument is adjusted by a factor of 0.92. The CFSTGC was cycled between 6 discrete SS values ranging from 0.09% to 0.74%, producing a full CCN spectrum every hour. The raw CCN measurements are filtered to discount periods of transient operation and whenever the room temperature housing the instrument changed sufficiently to induce a reset in column temperature. Additional information can be found in Section 2.1.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).
Hygroscopicity data: The CCN number concentration measurements were directly related to the size distribution and total aerosol concentration data measured by the Scanning Mobility Particle Size Spectrometer (SMPS) instrument at the same station (https://www.envidat.ch/dataset/aerosol-data-weissfluhjoch) to infer the particles hygroscopicity parameter (kappa). For each SMPS scan, the particles critical dry diameter (Dcr) is estimated by integrating backward the SMPS size distribution, until the aerosol number matches the CCN concentration observed for the same time period as the SMPS scan. Assuming the particle chemical composition is internally mixed, the kappa is determined from Dcr and SS, applying Köhler theory. Additional information can be found in Section 2.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).
Predicted cloud droplet numbers: Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from the SMPS instrument deployed at Weissfluhjoch. The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at Davos Wolfgang and are extracted for the altitude of interest, being 1100 m above ground level for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

en: cloud-droplets cloud-modeling ccn supersaturation raclets hygroscopicity dmt-ccn-chamber sigma-w weissfluhjoch

en: CCN, hygroscopicity, predicted cloud droplet numbers Weissfluhjoch

envi

en: Environment

en:
Resource Title: Hygroscopicity parameter Weissfluhjoch
    Size: 344260 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ecb08acd-01d0-40e6-ac42-584aab9fc27f/resource/5032c15d-ac68-4e8f-822b-3503a34db837/download/hygroscopicitywfj.csv
    Description: The aerosol hygroscopicity parameter is derived combining the CCN and SMPS size distribution data collected between February 24 and March 8 2019 at Weissfluhjoch. The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The instrument supersaturation (SS%)
* The calculated hygroscopicity parameter (kappa)
* The calculated critical dry diameter of the particles (Dcr) in nm.

Missing values are indicated by "-999".

en:
Resource Title: CCN data Weissfluhjoch
    Size: 79738661 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ecb08acd-01d0-40e6-ac42-584aab9fc27f/resource/80b5fb31-8f82-4648-bc82-d11373bd186a/download/ccnspectrumwfj.csv
    Description: In-situ CCN measurements were conducted between February 24 and March 8 2019 at Weissfluhjoch. The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* Filtered CCN number concentrations (in cmˉ³) measured at 6 discrete supersaturations (SS) ranging from 0.09% to 0.74%. The name of each CCN column is indicative of the SS developed within the instrument.
* Instrument characteristics such as sample temperature (in °C) and pressure (in mbar)
* A code that refers to the reason of filtering.

Missing or filtered CCN values are indicated by "-999", while a declaration of each "Error_code" can be found in the following list:

_Error_code_:

__0000__: Measurements without error

__1000__: Period of transient operation after a supersaturation change

__2000__: Sharp change (decrease) in the room temperature housing the instrument, which induces a reset in column temperature

__3000__: Irregular values in the room temperature housing the instrument

__9999__: Missing data

en:
Resource Title: Predicted cloud droplet number concentrations Weissfluhjoch
    Size: 256123 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ecb08acd-01d0-40e6-ac42-584aab9fc27f/resource/f732a28e-10b1-411f-a497-b784e1b716af/download/predicted_droplets_wfj_kappa0.25.csv
    Description: In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Weissfluhjoch station between February 24 and March 8 2019. For this, a hygroscopicity parameter of 0.25 is assumed, according to the CCN-derived values. The data file contains:

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

ccn

display_name: ccn

dmt-ccn-chamber

display_name: dmt-ccn-chamber

hygroscopicity

display_name: hygroscopicity

weissfluhjoch

display_name: weissfluhjoch


en:
Reference Elevation Model of Antarctica (REMA) topography and AntarcticaLC2000 landuse data
are now available as static data input for the Weather Research and Forecasting model (WRF). Topography
and landuse are made available at a spatial resolution of 1 km. This documentation describes the methods
applied to convert REMA and AntarcticaLC2000 to WRF readable format and shows how this improves
the representation of the Antarctic topography and landuse categories over coastal Antarctic regions.

en: topography cryowrf wrf antarctica landuse

en: REMA topography and AntarcticaLC2000 for WRF

en:
Resource Title: WPSinput
    Size: 15462767 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/886f1b73-459a-4d41-aca5-0af9dc86ae7b/resource/13f9afab-ef62-4454-965f-19b3e6288cc7/download/wpsinput.zip
    Description: Input files to run the Weather Research and Forecasting model (WRF) preprocessing system WPS with REMA topography and AntarcticaLC2000 landuse data (see Documentation for details).

en:
Resource Title: Scripts
    Size: 24621 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/886f1b73-459a-4d41-aca5-0af9dc86ae7b/resource/97db282f-c028-4d1e-8bf9-6cda65aa5f7a/download/scripts.zip
    Description: Scripts to run the preprocessing to produce WRF binary tiles from REMA and AntarcticaLC2000 for topography and landuse (see Documentation for procedure).

en:
Resource Title: Documentation
    Size: 2136909 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/886f1b73-459a-4d41-aca5-0af9dc86ae7b/resource/ddb02d97-7e22-4aca-ab56-29f57ef2ad8e/download/documentation_rema_antarcticalc2000.pdf
    Description: Documentation about how the provided dataset was produced including details to reproduce the dataset.

antarctica

display_name: antarctica

cryowrf

display_name: cryowrf

landuse

display_name: landuse

topography

display_name: topography

wrf

display_name: wrf


en:
To validate the critical crack length as implemented in the snow cover model SNOWPACK, PST experiments were conducted for three winter seasons (2015-2017) at two field site above Davos, Switzerland. This dataset contains manually observed snow profiles and stability tests. Furthermore, corresponding SNOWPACK simulations are included.
These data were analyzed and results were published in Richter et al. (2019).
Please refer to the Readme file for further details on the data.
These data are the basis of the following publication:
Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019.

en: snowpack snow-stability-evaluation avalanche-forecast snow avalanche

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: EnviDat: the Environmental Data Portal

en: Validating and improving the critical crack length in SNOWPACK

en:
Resource Title: Stability tests
    Size: 6975 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/98cbbb6e-bfad-4b71-b920-e3def95143d3/download/stability_tests.zip
    Description: Stability tests including csv-files with CT, ECT and PST experiments for each year and each field site.

en:
Resource Title: Extracted data
    Size: 197562 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/42774021-73e4-49b2-a714-b192a0e3cc14/download/extracted_data.zip
    Description: Extracted data contain validation and results published in Richter et al. (2019).

en:
Resource Title: Manually observed snow profiles
    Size: 27333 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/74bf515b-cd6b-4e97-be6d-4356f0af6d3d/download/manual_snow_profiles.zip
    Description: Manually observed snow profiles as csv-files containing depth [cm], thickness [cm], density [kg m-3], hand hardness, first grain type, second grain type, first grain size [mm], and second grain size [mm] of each layer.

en:
Resource Title: SNOWPACK simulations
    Size: 21903741 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/6b0dc93f-34d1-48e2-bf69-3f9254e34692/download/snowpack.zip
    Description: This dataset contains all SNOWPACK simulations, including input files, setup and output files.

en:
Resource Title: README
    Size: 18771 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/b6cfb0c3-2bb3-4fa1-859d-5861983aa785/download/readme.txt
    Description: README file explaining data structure and content

avalanche

display_name: avalanche

avalanche-forecast

display_name: avalanche-forecast

snow

display_name: snow

snow-stability-evaluation

display_name: snow-stability-evaluation

snowpack

display_name: snowpack


en:
Observational data used to quantitatively describe the key elements describing avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size.
The data set consists of
- Rutschblock test results (Switzerland)
- Extended Column Test results (Switzerland, Norway)
- Avalanche occurrence data (Switzerland, Norway).
The data were extracted from the respective operational databases of the national avalanche warning services in Switzerland (WSL Institute for Snow and Avalanche Research SLF Davos, Switzerland) and Norway (The Norwegian Water Resources and Energy Directorate NVE).
For further information regarding the data, please refer to the publication or contact the author.

en: rutschblock-test switzerland extended-column-test avalanche-danger avalanche-observations norway

en: Stability tests, avalanche observations, Switzerland, Norway

envi

en: Environment

en:
Resource Title: avalanche occurrence data (Norway, Sitzerland) - avalanche sizes per day and warning region
    Size: 1514094 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/23823196-1d56-47d1-b6bd-cb0add4dbd7b/download/avalsize_used_in_manuscript.csv
    Description:

en:
Resource Title: Rutschblock data (Switzerland)
    Size: 221029 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/30e5287d-6bc2-4810-bdc2-422976023486/download/rb_data_used_in_manuscript.csv
    Description:

en:
Resource Title: ECT data (Norway, Switzerland)
    Size: 241792 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/7618bcce-8c14-463d-8c4d-1dc4f5365085/download/ect_data_used_in_manuscript.csv
    Description:

en:
Resource Title: avalanche occurrence data (Norway, Sitzerland) - largest avalanche size per day and warning region
    Size: 256120 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/accef652-f988-4d75-8aff-4c4c06c24a74/download/maxavalsize_used_in_manuscript.csv
    Description:

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland


en: The WFJ_ICE_LAYERS dataset contains multi-instrument snowpack measurements at high temporal resolution, which enable to monitor the formation of deep ice layers due to preferential water flow, at the Weissfluhjoch research site, Davos, Switzerland. It covers the winter 2016/2017, with a focus on the early melting season. This dataset includes traditional snowpack profiles (weekly resolution, 15/11/2016-29/05/2017), SnowMicroPen penetration resistance profiles (daily resolution, 01/02/2017-19/04/2017), snow temperatures measured at different heights in the snowpack (half-hourly resolution, 01/03/2017-15/04/2017) and the water front height derived from an upward-looking ground penetrating radar (3-hour resolution, 04/03/2017-08/04/2017). The measurements are complemented by initialization files for SNOWPACK model simulations with the ice reservoir parameterization at Weissfluhjoch for the winter 2016/2017.

en: weissfluhjoch ice-layers snow-stratigraphy water-front snow-temperature snowmicropen

en: WFJ_ICE_LAYERS: Multi-instrument data for monitoring deep ice layer formation in an alpine snowpack

en:
Resource Title: WFJ_ICE_LAYERS dataset
    Size: 4289873 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/2bf03a26-ee90-4e13-8ed0-63fbfada5733/resource/06794e9d-cbac-4a14-9d3b-a06f7f6c2061/download/wfj_ice_layers_dataset.zip
    Description:

en:
Resource Title: README
    Size: 476 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/2bf03a26-ee90-4e13-8ed0-63fbfada5733/resource/55d88599-ae7a-4ba6-af25-9544893de2be/download/readme.txt
    Description:

ice-layers

display_name: ice-layers

snow-stratigraphy

display_name: snow-stratigraphy

snow-temperature

display_name: snow-temperature

snowmicropen

display_name: snowmicropen

water-front

display_name: water-front

weissfluhjoch

display_name: weissfluhjoch


sensitivity-of-modeled-snow-instability

en: We investigated the sensitivity of modeled snow instability to meteorological input data using SNOWPACK. We therefore used input data from the automatic weather station at the Weissfluhjoch field site for the year 2016-2017. We investigated three scenarios and performed 14'000 simulations for each scenario.  The dataset contains extracted output data from modeled SNOWPACK simulations, including setup files to reproduce the simulations. For further information read the README file.

en: snow-instability snow-cover-modeling snowpack numerical-avalanche-forecasting global-sensitivity-analysis

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: EnviDat: the Environmental Data Portal

en: Sensitivity of modeled snow instability

title_for_slug: sensitivity-of-modeled-snow-instability

en:
Resource Title: Case SL
    Size: 277264559 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/716fdeb9-b2e2-4613-bba3-691eff467c1b/resource/7635f568-e96c-4901-a385-c369150aaad6/download/case_sl.zip
    Description: Extracted output data for case SL. Here biases were introduced to meteorological input data during the period of slab formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en:
Resource Title: Reference run
    Size: 9909880 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/716fdeb9-b2e2-4613-bba3-691eff467c1b/resource/7eb0f753-02d2-4592-b822-cf91745ea196/download/reference.zip
    Description: Reference run for the sensitivity analysis at Weissfluhjoch field site for winter season 2016-2017. Meteorological data from the automatic weather stations are used. No biases were introduced to the input data.

en:
Resource Title: Case ALL
    Size: 346790131 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/716fdeb9-b2e2-4613-bba3-691eff467c1b/resource/fd47ae3d-77a9-4f79-8c95-57b11de523bb/download/case_all.zip
    Description: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the whole simulation period. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en:
Resource Title: Manually observed snow profiles
    Size: 12725 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/716fdeb9-b2e2-4613-bba3-691eff467c1b/resource/5bd23cea-d4e3-4f87-8b0c-bfa3dc82afec/download/manual_profiles.zip
    Description: Manually observed snow profiles at Weissfluhjoch field site for winter season 2016-2017, including layer depth, hand hardness, grain size and grain shape for each layer.

en:
Resource Title: Case WL
    Size: 342942228 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/716fdeb9-b2e2-4613-bba3-691eff467c1b/resource/84ee8947-54aa-40d6-86bb-b6e0ffc14d25/download/case_wl.zip
    Description: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the period of weak layer formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

global-sensitivity-analysis

display_name: global-sensitivity-analysis

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability


en:
This dataset provides distribution data of fungi in Switzerland of the National Data and Information Centre, called  [SwissFungi](https://swissfungi.wsl.ch/en/index.html). SwissFungi is a partner of [InfoSpecies](https://www.infospecies.ch/de/), the network of Swiss data and information centres for [fauna](http://www.cscf.ch/cscf/de/home.html), [flora](https://www.infoflora.ch/en/) and [fungi](https://swissfungi.wsl.ch/en/index.html). One of its main objectives is to document the spatial and temporal distribution of species in Switzerland.
The SwissFungi database currently contains more than 670'000 geo-referenced fungi observations, distributed throughout Switzerland. The oldest observations date back to 1770. A large portion of the records are from the last two decades of the last century to the present day. The database is continuously updated with new fungi records. 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.
The data from the distribution atlas of fungi in Switzerland are available for research and practice (nature conservation projects, environmental impact assessments etc.) and can be obtained via an [application form](https://www.infospecies.ch/de/assets/content/documents/Formular_Datenanfrage20190625.pdf). Please note the tariffs for data requests and submit your request directly to the [InfoSpecies](https://www.infospecies.ch/de/) office. Applications are usually answered within two working weeks. Details on the use of data are regulated in the current guidelines of the national data centers. Please note that the data center SwissFungi is not able to verify all incoming fungal records completely for a correct identification or coordinate errors and therefore cannot guarantee the correctness of the information.
License under [InfoSpecies](https://www.infospecies.ch/de/). Data is free of charge for research projects and available on request.

en: biodiversity fungi-conservation national-data-center records-database fungi swissfungi

en: SwissFungi - Records Database for the Fungi of Switzerland

envi

en: Environment

biodiversity

display_name: biodiversity

fungi

display_name: fungi

fungi-conservation

display_name: fungi-conservation

national-data-center

display_name: national-data-center

records-database

display_name: records-database

swissfungi

display_name: swissfungi


digitizing-historical-plague

en: We present newly digitized data on 6,929 plague outbreaks that occurred between 1347 and 1900 AD across Europe. The data base on an inventory initially published 1976. For georeferencing the information of Tele Atlas 2009 was used. The coordinates are in the reference systems ETRS89 and WGS84.

en: plague-inventory plague-mapping palaeo-epidemiology bubonic-plague historical-plague yersinia-pestis black-death plague-outbreaks

en: Digitizing historical plague

title_for_slug: digitizing-historical-plague

black-death

display_name: black-death

bubonic-plague

display_name: bubonic-plague

historical-plague

display_name: historical-plague

palaeo-epidemiology

display_name: palaeo-epidemiology

plague-inventory

display_name: plague-inventory

plague-mapping

display_name: plague-mapping

plague-outbreaks

display_name: plague-outbreaks

yersinia-pestis

display_name: yersinia-pestis


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 one meteorological station in Alpthal in Switzerland which is located within a natural coniferous forest (ALB) with Norway spruce (_Picea abies_; 180-230 yrs) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Alpthal is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

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

en:
Resource Title: Data plot of the forest station (ALB)
    Size: 277476 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/3bb25386-f2d8-4fda-b7fa-86ea4be16535/resource/50d91991-2164-4f48-9bff-7dd1ecd9f379/download/albplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Forest station data (ALB)
    Size: 7164766 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb25386-f2d8-4fda-b7fa-86ea4be16535/resource/5109ff84-b671-41af-a243-34f1eda1fc88/download/albforest.csv
    Description:

en:
Resource Title: Site aerial photo (Alpthal)
    Size: 7983732 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/3bb25386-f2d8-4fda-b7fa-86ea4be16535/resource/c331a75a-6b89-4924-8896-6582b74c7284/download/alfront.jpg
    Description: Site aerial photo from the Alpthal forest station. Source: Google. 2016. Site aerial photo from the Alpthal forest station. Retrieved November 11, 2016, from https://www.google.ch/maps

format: jpeg

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