en:
Recent publications about declines in arthropod biomass, abundance and species diversity
raise concerns and call for measures. Agricultural intensification is likely one cause for the negative trends. But rare long-term arthropod surveys conceal trends in arthropod communities in agricultural land. Here, we report about a standardized sampling of arthropod fauna in a Swiss agricultural landscape, repeated over 32 years (1987, 1997 and 2019). We sampled 8 sites covering 4 semi-natural and agricultural habitat types. Four trap types  were used to capture a wide range of flying and ground dwelling arthropods between May and July. Over the three sampling periods, 58’255 specimens of 1’343 species were analysed. Mean arthropod biomass, abundance and species richness per trap was significantly higher in 2019 than in prior years and gamma diversity of the study area was highest in 2019. Biomass and abundance increased stronger in the flight traps than in the pitfall traps. The implementation of agri-environmental schemes has improved habitat quality since 1993, while landscape composition and pesticide and fertilizer use remained stable over the study period, both contributing to the findings. The results of this study contrast with outcomes of comparable investigations and highlight the importance of further long-term investigations on arthropod dynamics.
Data are provided on request to contact person against bilateral agreement.

en: insect-decline long-term time-series switzerland biodiversity

envidat

en: EnviDat: the Environmental Data Portal

en: Arthropod biomass, abundance and species richness trends over 32 years in the agricultural Limpach valley, Switzerland

en:
Resource Title: Master thesis of Regula Gygax, 1999, at University of Zürich
    Size: 5380429 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/bc0a4717-49ee-4120-aa6c-8c7dec773577/download/regula-gygax-1999-diplomarbeit-uni-zh.pdf
    Description: The study is comparing in 1997 the trapping efficiency of a newly developed combination trap with traditional window traps and yellow pan traps. Study locations in the Limpach valley matched with a subset from a larger study, which was conducted in 1987.

en:
Resource Title: Body size lookup-table
    Size: 17437 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/56e5590a-b1aa-4ba5-8ad2-6f839c6b3b85/download/koerpergroessen_carabidae_edjf.csv
    Description: Carabidae

en:
Resource Title: Body size lookup-table
    Size: 7775 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/71e46f93-be9a-4805-b83a-3033447ff616/download/koerpergroessen_aculeata_edjf.csv
    Description: Aculeata

en:
Resource Title: Body size lookup-table
    Size: 44183 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/2a8f6d75-ff31-41cc-bed4-7b90b6d579f0/download/koerpergroessen_staphylinidae_edjf.csv
    Description: Staphylinidae

en:
Resource Title: R code
    Size: 38710 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/888eb6a0-e541-4296-ba26-b43a87a6a938/download/20220818_dataanalysisdef.r
    Description: R code for data analyses

en:
Resource Title: Body size lookup-table
    Size: 2057 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/4f8eb204-a9be-41d5-86a4-a2bb2c394169/download/koerpergroessen_syrphidae_edjf.csv
    Description: Syrphidae

en:
Resource Title: Species number and abundances per trap
    Size: 56123 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/1f32e64c-1d0f-44ac-a584-7c62c053d185/download/trap_data_publication20210221.csv
    Description: Sum of species and abundances listed per trap

en:
Resource Title: Body size lookup-table
    Size: 1782 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/290e0cfa-857f-4ddd-940d-96076d736419/download/koerpergroessen_formicidae_edjf.csv
    Description: Formicidae

en:
Resource Title: Species incidence per year
    Size: 30771 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/96edac02-f93b-44e3-95e0-b9e7c2dcddf1/download/incidence_freq_20210221.csv
    Description: Total abundance of species per year

en:
Resource Title: Fused catching data 1987, 1997, 2019
    Size: 5216910 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/373100d8-43f4-476b-9678-d40cc4fa47b0/download/limpach198719972019_artind.csv
    Description: Complete dataset of species and abundances per year an trap

en:
Resource Title: Input data - species list and trophic guilds
    Size: 163154 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/44cb422b-d09d-49b7-94fd-3b3648ad4fce/download/artliste_mittrophicguild.csv
    Description: List of found species and associated trophic guild

en:
Resource Title: Sites and traps
    Size: 8117 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/325305be-ff93-48c4-8c16-a3a4e37663ab/download/limp_sites_lookup.csv
    Description: Lookup table of trap and site locations

en:
Resource Title: Body size lookup-table
    Size: 617 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/bb63365b-4971-4992-af92-cdcfbf49e7c8/download/koerpergroessen_neuropteroidea_edjf.csv
    Description: Neuropteroidae

en:
Resource Title: Capture data with size and mass
    Size: 2804030 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/310ccd7a-b1d0-4ab1-99e4-1ba70e552fc5/download/data_publication_20210221.csv
    Description: Complete capture data including body sizes and biomass values

en:
Resource Title: Limpach1987+1997+2019_ArtInd.csv
    Size: 5148268 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/a0d555bb-1dc3-4c39-884c-a6366438809a/download/limpach198719972019_artind.csv
    Description: Data extract from WSL Fauna Database containing arthropod species and abundance data from 1987, 1997, 2019

en:
Resource Title: Climate data
    Size: 267 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/b53622ce-6ad7-4def-b009-e5d21ceeae5a/download/climaaddition.csv
    Description: Additional climate data on climate trend

en:
Resource Title: Climate data
    Size: 44128 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/e1d5afa9-530a-4b4e-99e3-d43b6ca613cc/download/climate_data_879719_jan_july.csv
    Description: Climate data of the respective sampling years

en:
Resource Title: Body size lookup-table
    Size: 6456 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/5ed0e1e9-bd33-4c8b-ac7e-8c4ec47deafc/download/koerpergroessen_heteroptera_edjf.csv
    Description: Heteroptera

en:
Resource Title: Body size lookup-table
    Size: 16314 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/ba8c3d6b-b809-4e42-a0e4-6a858a9c7636/download/koerpergroessen_coleoptera_edjf.csv
    Description: Coleoptera

en:
Resource Title: R code
    Size: 14746 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/0e13f98d-7529-451b-80fc-72d6a2ecf76b/download/20201111_dataediting.r
    Description: R code for data editing

en:
Resource Title: Body size lookup-table
    Size: 47769 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/91417163-72ac-4f71-9c80-b6fed1f39218/download/koerpergroessen_araneae_edjf.csv
    Description: Araneae

en:
Resource Title: Limpach1987+1997+2019_ArtInd.sql
    Size: 4269 bytes
    Format: application/x-sql
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/38baef03-5b46-4894-ba32-e0f6307c7b62/download/limpach198719972019_artind.sql
    Description: SQL query executed on WSL Fauna Database to retrieve the data

biodiversity

display_name: biodiversity

insect-decline

display_name: insect-decline

long-term

display_name: long-term

switzerland

display_name: switzerland

time-series

display_name: time-series


$oid: OBSOLETE

en:
This dataset contains all data, on which the following publication below is based.
**Paper Citation**:
Neff, F., Resch, M. C., Marty, A., Rolley, J. D., Schütz, M., Risch, A. C, Gossner, M. M. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands: a functional approach. Ecological Applications, 30, e02133. [10.1002/eap.2133](https://doi.org/10.1002/eap.2133)
Please cite this paper together with the citation for the datafile.
 Methods
 Study site
The study area is situated within and nearby to Eigental nature reserve (47°27’36” to 47°29’06” N, 8°37’12” to 8°37’44” E, 461 to 507 m a.s.l.) in the vicinity of Zurich airport (Canton Zurich, Switzerland). Mean annual precipitation and temperature is 903 ± 136 mm and 9.14°C ± 0.50°C (mean ± SD for 2007-2017 (*MeteoSchweiz 2018*)). In 1967, the Eigental nature reserve was established to protect small and isolated remnants of species-rich, semi-natural grasslands (roughly 12 ha), which were embedded in an otherwise intensively managed landscape. It is characterized by oligo- to mesotrophic Molinion (semi-wet, matrix species *Molinia caerulea*) and Mesobromion (semi-dry, matrix species *Bromus erectus*) meadows (*Delarze et al. 2015*), reflecting small-scale habitat heterogeneity, mainly due to site-specific groundwater levels and slope inclination. As in most Central European grasslands, management is necessary to prevent shrub and tree invasions as well as to secure low levels of available soil nutrients and thus to maintain these species-rich habitats ([*Poschlod and WallisDeVries 2002*](https://doi.org/10.1016/S0006-3207(01)00201-4)).
In 1990, the government of the Canton Zurich decided to enlarge the Eigental nature reserve as a counter measure against degradation and biodiversity loss in semi-natural grasslands due to overutilization and the excessive input of nutrients (mostly nitrogen). Eleven patches of adjacent intensively managed grassland (in total roughly 20 ha) were targeted to be transformed into semi-natural grasslands. As a first restoration measure, fertilization was ceased, and biomass harvested three times to remove excessive soil nutrients from the original system and thus benefit plant species with low competitive ability on the long run. In 1995, the restoration efforts were increased and a large-scale experiment comprising three restoration measures with increasing intervention intensities was implemented:
- **Harvest only**: Initial restoration measures were continued with mowing and removing of the aboveground biomass two times a year (early summer and autumn).
- **Topsoil**: Removal of topsoil, depending on the thickness of the A horizon the upper 10 to 20 cm, in four randomly selected areas within the eleven patches in late autumn 1995. The size of the restoration area depended on individual patch size (2700 to 7000 m2).
- **Topsoil + Propagules**: Plant propagules were added on half of the area where topsoil was removed via application of fresh, seed-containing hay and hand-collected propagules of target species originating from semi-dry and semi-wet species-rich grasslands with local and regional provenance (within radius of 7 to 30 km) (1995, 1996, 1997).
Management of *Topsoil* and *Topsoil + Propagules* started five years after treatment implementation and included yearly mowing and removing of aboveground biomass (late summer or early autumn). The experiment was complemented with intensively managed grassland sites that share the same agricultural history as the restored sites (**Initial**; swards dominated by *Lolium perenne*, *L. multiflorum* and *Trifolium repens*): mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes. Finally, sites were selected in target semi-dry and semi-wet grasslands (**Target**) located within the Eigental nature reserve and another nature reserve nearby (Altläufe der Glatt; 47°28’29” to 47°27’41” N, 8°31’56” to 8°32’26” E, 418 to 420 m a.s.l.). The selected target sites are mown and aboveground biomass removed once a year in late summer or early autumn. For each of the five treatments, we selected eleven plots (5 m × 5 m) spread across the sites. Altogether, the experiment included 55 plots.
 Arthropod sampling
Aboveground arthropods were sampled using suction sampling on four consecutive days in early July 2017 before the grasslands were mown. Arthropods were sampled in two locations on each 5 m × 5 m plot, once in the south-western and once in the north-eastern corner to account for possible spatial heterogeneity within the plots. Arthropods were sorted to order or lower taxonomic levels and individuals were identified to species level. We focused on three groups (Hemiptera: Auchenorrhyncha, Hemiptera: Heteroptera, Orthoptera),
 Functional traits
We used two sets of functional traits in this study.
**Morphometric traits**:
Body volume, body shape, hind femur shape, hind/front leg ratio, wing length, leg length, antenna length and eye width. We used trait measurements from [*Simons et al. (2016)*](http://dx.doi.org/10.1890/15-0616.1) and [*Neff et al. (2019)*](https://doi.org/10.1007/s10980-019-00872-1) and complemented them with measurements on study specimens. These measurements were conducted using a high-resolution measuring stereo microscope (Leica DVM6, Leica Microsystems) including automated high-resolution photo stacking with the software Leica Application Suite X (LAS X, © 2018 Leica Microsystems CMS GmbH) and Leica Map Premium (Leica Microsystems, © 1996-2017 Digital Surf) at WSL Birmensdorf. The eight morphometric traits were calculated from direct measurements of body parts on specimens of all sampled species. From each species, we measured at least one female and one male specimen. Additionally, for species that show wing dimorphism, we included the different wing morphs and weighted them by their prevalence reported in literature. For few species, of which not all wing morphs were available for measurements (10 cases), we estimated relative wing length from congeneric species or from the literature.
**Life-history traits**
Based on an existing data set collected by [*Gossner et al. (2015)*](http://dx.doi.org/10.1890/14-2159.1). We included traits describing different life-history characteristics of herbivore insect species, namely: feeding specialization, feeding tissue, hibernation stage and number of generations per year, which are related to insect species’ vulnerability to changes in plant community composition, microhabitat use and disturbance tolerance. To represent potential changes in habitat moisture with abandonment of intensive land use (e.g., change in ground-water level), we also included two traits related to preferred habitat moisture of the study species: moisture preference, describing species’ optimum habitat moisture, and moisture range, which describes the species’ range of preferable moisture conditions.
 References
Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten. 3rd ed. Ott, Bern.
Gossner, M. M., N. K. Simons, R. Achtziger, T. Blick, W. H. O. Dorow, F. Dziock, F. Köhler, W. Rabitsch, and W. W. Weisser. 2015. A summary of eight traits of Coleoptera, Hemiptera, Orthoptera and Araneae, occurring in grasslands in Germany. Scientific Data 2:150013.
MeteoSchweiz. 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Zürich.
Neff, F., N. Blüthgen, M. N. Chisté, N. K. Simons, J. Steckel, W. W. Weisser, C. Westphal, L. Pellissier, and M. M. Gossner. 2019. Cross-scale effects of land use on the functional composition of herbivorous insect communities. Landscape Ecology 34:2001–2015.
Poschlod, P., and M. F. WallisDeVries. 2002. The historical and socioeconomic perspective of calcareous grasslands—lessons from the distant and recent past. Biological Conservation 104:361–376.
Simons, N. K., W. W. Weisser, and M. M. Gossner. 2016. Multi-taxa approach shows consistent shifts in arthropod functional traits along grassland land-use intensity gradient. Ecology 97:754–764.

en: restoration long-term-experiment topsoil-removal functional-traits functional-diversity grassland insects diversity

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: Grassland restoration: insects and insect traits

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

en:
Resource Title: Insect traits
    Size: 11167 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e4752eb3-24f1-48b5-a6c4-e0a4134af08d/resource/bddffb02-61f5-44b1-b96a-92bb043415b4/download/insect_traits.csv
    Description: Functional traits of all herbivorous insect species sampled in the study.

- **Species**: Insect species name (following [Fauna Europaea](https://fauna-eu.org))
- **Author**: Author and year of species' description (following [Fauna Europaea](https://fauna-eu.org))
- **Group**: Insect group as used in the analyses
- **Body volume**: Body volume determined by body length, body width and body height (in mm$^3$)
- **Body_shape**: Ratio of body length to body width
- **Hind_femur_shape**: Ratio of hind-femur length to hind-femur width
- **Hind_front_leg_ratio**: Ratio of front-leg length to hind-leg length (leg length = femur length + tibia length)
- **Wing_length_rel**: Ratio of wing length to body length
- **Leg_length_rel**: Ratio of front-leg length to body length (leg length = femur length + tibia length)
- **Antenna_length_rel**: Ratio antenna length to body length
- **Eye_width_rel**: Ratio of eye width to head width (Auchenorrhyncha, Heteroptera) / to head height (Orthoptera)
- **Feeding_specialization**: Insect specialization on food source (*m*: monophagous, *o*: oligophagous, *p*: polyphagous)
- **Feeding_tissue**: Feeding tissue used for feeding (*c*: chewers, *x*: xylem, *p*: phloem, *m*: mesophyll, *m-p/r*: mesophyll as well as phloem and/or reproductive organs, *r*: reproductive organs, *se*: ripe seeds)
- **Hibernation**: Hibernation stage (*e*: egg, *e/n*: egg and nymph, *n*: nymph, *a*: adult)
- **Generations**: Number of generations per year (*1.5* stands for *1-2* generations per year)
- **Moisture_pref_mean**: mean of preferred habitat moistures (*1*: xerophilous, *2*: moderately xerophilous, *3*: moderately mesohygrophilous, *4*: mesohygrophilous, *5*: moderately hygrophilous, *6*: hygrophilous, *7*: moderately hydrophilous, *8*: hydrophilous)
- **Moisture_pref_range**: range of preferred habitat moistures (indicating degree of specialization)

format: CSV

media_type: text/csv

en:
Resource Title: Insect abundance
    Size: 33818 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e4752eb3-24f1-48b5-a6c4-e0a4134af08d/resource/2d60fcd6-a2f1-45fd-a450-3d560eb43822/download/insect_abundance.csv
    Description: Abundance of adult insects recorded in the samplings on all experimental sites

- **Plot**: ID of Plot
- **Treatment**: Experimental treatment
- **Species**:  Insect species name (following [Fauna Europaea](https://fauna-eu.org))
- **Author**: Author and year of species' description (following [Fauna Europaea](https://fauna-eu.org))
- **Group**: Insect group as used in the analyses
- **Number_Adults**: Number of adult individuals recorded
- **Herbivore**: Logical indicating whether the species is feeding as a herbivore (*TRUE*) or not (*FALSE*)

en:
Resource Title: Plot information
    Size: 4938 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e4752eb3-24f1-48b5-a6c4-e0a4134af08d/resource/04c37490-b0c2-4681-aded-068fa4ee8290/download/plot_info.csv
    Description: Information on the experimental plots.

- **Plot**: ID of Plot
- **Treatment**: Experimental treatment
- **Landuse_history**: recent agricultural use for Initial; agricultural use before restoration for all restored sites
- **Last_tilled**: Last time the site was tilled
- **Intensity**: Frequency and intensity of mowing and manuring (*intensity1*: mowing once and no manure, i*ntensity2*: 2-3 times mowing and no manure, *intensity3*: 2-3 times mowing and manuring, *intensity4*: 4-5 times mowing and manuring)
- **Slope**: slope gradient according to classes defined by FAO (1990)
- **X**: X coordinate of the plot (CH1903)
- **Y**: Y coordinate of the plot (CH1903)
- **Cluster**: Spatial cluster of the plot

diversity

display_name: diversity

functional-diversity

display_name: functional-diversity

functional-traits

display_name: functional-traits

grassland

display_name: grassland

insects

display_name: insects

long-term-experiment

display_name: long-term-experiment

restoration

display_name: restoration

topsoil-removal

display_name: topsoil-removal


en:
This data set includes information on all observed and simulated snow profiles that were used to train and validate the
random forest model described in Mayer et al. (2022). The RF model was trained to assess snow instability from simulated snow stratigraphy. The data set contains observed snow profiles from the region of Davos (DAV subset, 512 profiles) and from all over Switzerland (SWISS subset, 230 profiles). For each observed snow profile, there is a corresponding simulated profile which was obtained using meteorological input data for the numerical snow cover model SNOWPACK. The information on the observed snow profile contains a Rutschblock test result including the depth of the failure interface. As part of the study described in Mayer et al. (2022), each observed snow profile was manually compared to its simulated counterpart and the simulated layer corresponding to the Rutschblock failure layer was identified.
The data are provided in the following form: one file each per observed and simulated snow profile (2x512 files DAV, 2x230 files SWISS), two files (1 file DAV, 1 file SWISS) containing the observed information on snow instability, the allocation between observed and simulated failure layer, and all features extracted from the simulated weak layers that were used to develop the RF model.

en: snowpack failure-layer snowpack-simulation snow-stratigraphy snow-profiles snow-instability rutschblock

en: Observed and simulated snow profile data

en:
Resource Title: SWISS data set: Properties of observed snow profiles and simulated failure layers
    Size: 105662 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f109f639-c6c8-46d6-82fd-6a5dbc2bb114/resource/8307de98-3d32-40de-97e8-e938a026cf2b/download/properties_observed_and_simulated_swiss.csv
    Description: For each profile pair from the SWISS data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. Please note that only those profile pairs which fulfilled the similarity criteria (column similarity_obs_sim = 1, 121 profiles) were used to validate the RF model. The content of the columns is explained in Table 1 of the ReadMe file.

en:
Resource Title: ReadMe
    Size: 140674 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/f109f639-c6c8-46d6-82fd-6a5dbc2bb114/resource/2156a195-28ea-418e-ad7e-4af7b5337d7a/download/readme.pdf
    Description: ReadMe file explaining data structure and content

format: PDF

media_type: application/pdf

en:
Resource Title: Observed snow profiles
    Size: 261432 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f109f639-c6c8-46d6-82fd-6a5dbc2bb114/resource/89fd369e-f7d9-49b1-8d24-f9fe3b15cd08/download/observed_snow_profiles.zip
    Description: Observed snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding simulated profile in the "simulated_profiles" folder. One row corresponds to one observed snow layer. The content of the columns is described in Table 2  of the ReadMe file.

format: ZIP

media_type: application/zip

en:
Resource Title: DAV data set: Properties of observed snow profiles and simulated failure layers
    Size: 228035 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f109f639-c6c8-46d6-82fd-6a5dbc2bb114/resource/98972dcd-f6bb-4391-8dfa-1ce22cd3d7cc/download/properties_observed_and_simulated_dav.csv
    Description: For each profile pair from the DAV data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. The content of the columns is explained in Table 1 of the ReadMe file. Only those profile pairs which fulfilled predefined similarity criteria (column
"similarity_obs_sim" = 1) and which belonged to the stability classes "stable" and "unstable" (column "stability_class"
= 0 or 1) were used to train the RF model. Please note that for each of the two snow profiles with RB score 7 (profile
IDs 7228 and 9526), three additional layers were included in this data set to obtain a balanced set of training data (73
unstable and 73 stable profiles).

en:
Resource Title: Simulated snow profiles
    Size: 8635143 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f109f639-c6c8-46d6-82fd-6a5dbc2bb114/resource/adaf78bb-27a6-4ab9-ae40-ff114e683496/download/simulated_snow_profiles.zip
    Description: Simulated snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding observed profile in the "observed_profiles" folder. One row corresponds to one simulated snow layer. The content of the columns 1-7 is described in Table 3 of the ReadMe file. The remaining columns (8-31) contain all features that were used in the development of the RF model and  are described in Table 1 of the ReadMe file.

failure-layer

display_name: failure-layer

rutschblock

display_name: rutschblock

snow-instability

display_name: snow-instability

snow-profiles

display_name: snow-profiles

snow-stratigraphy

display_name: snow-stratigraphy

snowpack

display_name: snowpack

snowpack-simulation

display_name: snowpack-simulation


en: Compiled data on the impacts of seven important NNTs (Acacia dealbata, Ailanthus altissima, Eucalyptus globulus, Prunus serotina, Pseudotsuga menziesii, Quercus rubra, Robinia pseudoacacia) on physical and chemical soil and biodiversity in Europe, and summarise commonalities and differences. A total of 107 publications considered, studies referred to biodiversity attributes and soil properties: 2804 lines and 30 rows.

en: biodiversity soil-impacts pairwise-stand-comparisons biogeography forest-management

en: Impact of non-native tree species in Europe on soil properties and biodiversity: a review

en:
Resource Title: headers
    Size: 10213 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/74062fb9-588e-462b-9125-26bc8cb7d1e6/resource/4c8d584a-399a-48a9-a20a-c42f71b08dbf/download/headers.xlsx
    Description: Description of headers for data_wohlgemuth_etal_Neobiota_2022

format: XLS

en:
Resource Title: references
    Size: 24932 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/74062fb9-588e-462b-9125-26bc8cb7d1e6/resource/f55b90cb-3baa-4557-82e9-d870a1d02655/download/references.xlsx
    Description: Bibliography of all references cited in data_wohlgemuth_etal_Neobiota_2022.

en:
Resource Title: data_wohlgemuth_neobiota_2022
    Size: 729955 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/74062fb9-588e-462b-9125-26bc8cb7d1e6/resource/84c48309-eef7-4b07-baa2-59a0a1d75015/download/wohlgemuth_etal_neobiota_2022.csv
    Description: Baseline data

biodiversity

display_name: biodiversity

biogeography

display_name: biogeography

forest-management

display_name: forest-management

pairwise-stand-comparisons

display_name: pairwise-stand-comparisons

soil-impacts

display_name: soil-impacts


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

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

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

envi

en: Environment

en:
Resource Title: StaticData
    Size: 28859 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/86b121da-35f3-4096-88cd-1324ee511ac8/download/staticdata.zip
    Description: Scripts to process geographical/static data (see Section 3 in COSMOWRFdocumentation.pdf).
- ascii_to_bin_landuse.f90
- ascii_to_bin_soiltype.f90
- ascii_to_bin_topo_wgs84_ws2sf1.f90
- see_slopes.ncl
- soiltype_writing.py
- toposmooth_wgs84_cosmobound.ncl
- write_geogrid.o

en:
Resource Title: WRFrun
    Size: 8787 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/168aba72-d098-4d3b-a4d8-445ad91d5d10/download/wrfrun.zip
    Description: Scripts to run COSMO-WRF simulation (see Section 6 in COSMOWRFdocumentation.pdf)

en:
Resource Title: WPSSetup
    Size: 30162 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/a4f6bae9-dee7-4359-8c07-527ebbc6040f/download/wpssetup.zip
    Description: Scripts that need to be changed to compile WPS (see COSMOWRFdocumentation.pdf, Section 4)

en:
Resource Title: MeteorologicalData
    Size: 8317 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/54f99acd-e86a-4513-b1ea-d59d678f13ed/download/meteorologicaldata.zip
    Description: Scripts to process meteorological data (see Section 2 in COSMOWRFdocumentation.pdf).

en:
Resource Title: WPSrun
    Size: 6718 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/47a716bd-a1e5-4220-b9cc-53ef80089c7b/download/wpsrun.zip
    Description: Scripts to run the WRF pre-processing (WPS) for COSMO-WRF simulation (see Section 5 in COSMOWRFdocumentation.pdf)

en:
Resource Title: Postprocessing
    Size: 3782 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/316468d8-3fe9-4a1c-95e0-6595a4862396/download/postprocessing.zip
    Description: Postprocessing scripts to map WRF output on swiss coordinates (see Section 7 in COSMOWRFdocumentation.pdf)

en:
Resource Title: COSMO-WRF documentation v1.0
    Size: 275721 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/3a038630-044e-4769-9dd4-9032d7f337eb/download/cosmowrfdocumentation.pdf
    Description: Gerber, Franziska; Sharma, Varun (2018). Running COSMO-WRF on very-high resolution over complex terrain. EnviDat. doi:10.16904/envidat.35

Documentation about the procedure to run COSMO-WRF

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

high-resolution

display_name: high-resolution

meteoswiss

display_name: meteoswiss

numerical-simulation

display_name: numerical-simulation


intratrait

en:
This data set was used to test whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries (Switzerland, Australia, New Zealand, China) in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species’ height ratio, its height relative to its nearest neighbour.
The following traits and parameters were measured and are available in this data set:
As an indication of plant stature, we measured vegetative and generative height, where vegetative height was distance from soil to highest vegetative leaf and generative height was distance to the highest point on the reproductive shoot. As a measure of reproductive investment, we noted the presence of flowers on the randomly chosen individuals (see below). As a measure of individual and genet basal area, we measured individual plant and patch diameters, in two dimensions (along the largest diameter and perpendicular to it). In clonal plant species, plant diameter was equivalent to an individual rosette, whereas patch diameter referred to the whole genet and could represent the size of a tuft, tussock or cushion. For genera with more singular growth forms (e.g., some Gentiana species) plant and patch diameter were the same. The two diameter measurements were made at right angles, allowing estimates of patch and plant areas to be calculated as an ellipse (i.e., area = 0.5 a 0.5 b Π). All traits were measured on ten randomly selected individuals per site. Flower count data was considered in a binary fashion on a per individual basis (because for some species individuals only produce one flower when flowering) so that the presence or absence of flower(s) was a nominal value between 0 and 10 for each species at each site. We then collected at least three leaves (up to 30 for small and light leaves) from each of the first three individuals selected from each species for determination of leaf dry matter content (LDMC) and specific leaf area (SLA). For calculations of LDMC and SLA, fresh leaves were scanned on a flatbed scanner to determine leaf area. Leaves were then weighed on a balance to a precision of +/- 0.001g, prior to being air dried and reweighed with a balance to a precision of +/- 0.0001g. LDMC was calculated by dividing dry leaf mass by fresh leaf mass. SLA was calculated by dividing leaf area by dry leaf mass. Additionally, within an area of 10 cm diameter around the target individual, we determined the tallest neighbouring species and measured its vegetative and generative height, and estimated the percent cover of the target species, other vegetation, rock, and bare soil.
For more details see Rixen et al. 2022, Journal of Ecology.

en: climate-change plant-traits neighbour-interactions elevation-gradient cold-adapted-plants elevation-range

en: intratrait

title_for_slug: intratrait

en:
Resource Title: Rixen_etal_2022_JEcol_AllTraitData
    Size: 3679833 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/58a5f3e7-89e3-41c9-a880-5971ecaceab5/resource/d6845438-a41c-49a6-be0a-254e258a9acd/download/rixen_etal_2022_jecol_alltraitdata.csv
    Description: Plant trait data along elevation gradients on mountains

en:
Resource Title: R code Rixen_etal_2022_JEcol_AllTraitData
    Size: 74099 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/58a5f3e7-89e3-41c9-a880-5971ecaceab5/resource/bcff4cc4-e77a-4018-8a88-7b0f38d295e6/download/final_code_lme4_supplement.r
    Description:

climate-change

display_name: climate-change

cold-adapted-plants

display_name: cold-adapted-plants

elevation-gradient

display_name: elevation-gradient

elevation-range

display_name: elevation-range

neighbour-interactions

display_name: neighbour-interactions

plant-traits

display_name: plant-traits


en:
There are many sources providing atmospheric weather station data for the Antarctic continent. However, variable naming, timestamps and data types are highly variable between the different sources. The published python code intends to make processing of different AWS sources from Antarctica easier. For all datasets that are taken into account variables are renamed in a consistent way. Data from different sources can then be handled in one consistent python dictionary.
The following data sources are taken into account:
* AAD: Australian Antarctic Division (https://data.aad.gov.au/aws)
* ACECRC: Antarctic Climate and Ecosystems Cooperative Research Centre by the Australian Antarctic Division
* AMRC: Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub/aws/q1h/)
* BAS: British Antarctic Survey (ftp://ftp.bas.ac.uk/src/ANTARCTIC_METEOROLOGICAL_DATA/AWS/; https://legacy.bas.ac.uk/met/READER/ANTARCTIC_METEOROLOGICAL_DATA/)
* CLIMANTARTIDE: Antarctic Meteo-Climatological Observatory by the italian National Programme of Antarctic Research (https://www.climantartide.it/dataaccess/index.php?lang=en)
* IMAU: Institute for Marine and Atmospheric research Utrecht (Lazzara et al., 2012), https://www.projects.science.uu.nl/iceclimate/aws/antarctica.ph
* JMA: Japan Meteorological Agency (https://www.data.jma.go.jp/antarctic/datareport/index-e.html)
* NOAA: National Oceanic and Atmospheric Administration (https://gml.noaa.gov/aftp/data/meteorology/in-situ/spo/)
* Other/AWS_PE: Princess Elisabeth (PE), KU Leuven, Prof. N. van Lipzig, personal communication
* Other/DDU_transect: Stations D-17 and D-47 (in transect between Dumont d’Urville and Dome C, Amory, 2020)
* PANGAEA: World Data Center (e.g. König-Langlo, 2012)
Important notes
* Information about data sources is available. Some downloading scripts are included in the provided code. However, users should make sure to comply with the data providers terms and conditions.
* Given changing download options of the differnent institutions the above links may not permanently work and data has to be retrieved by the user of this dataset.
* No quality control is applied in the provided preprocessing software - quality control is up to the user of the datasets. Some dataset are quality controlled by the owner.
Acknowledgements
--------------------------
We thank all the data providers for making the data publicly available or providing them upon request. Full acknowledgements can be found in Gerber et al., submitted.
References
---------------
Amory, C. (2020). “Drifting-snow statistics from multiple-year autonomous measurements in Adélie Land, East
Antarctica”. The Cryosphere, 1713–1725. doi: 10.5194/tc-14-1713-2020
Gerber, F., Sharma, V. and Lehning, M.: CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB, JGR - Atmospheres, submitted.
König-Langlo, G. (2012). “Continuous meteorological observations at Neumayer station (2011-01)”. Alfred Wegener
Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, doi: 10.1594/PANGAEA.
775173

en: cryowrf consistent python aws antarctica

en: Preprocessing Antarctic Weather Station (AWS) data in python

antarctica

display_name: antarctica

aws

display_name: aws

consistent

display_name: consistent

cryowrf

display_name: cryowrf

python

display_name: python


rorcc

en:
The dataset "RoRCC" consists of simulation-based results on climate change impacts on Alpine RoR power production; it is based on 21 Swiss RoR power plants, with a total production of 5.9 TWh a-1. The dataset contains the following information:
1) metadata on the RoR power plants under consideration, 2) annual and seasonal production potential scenarios under into three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020–2049, T2: 2045–2074, T3: 2070–2099), 3) annual and seasonal streamflow scenarios, 4) annual and seasonal production loss due to environmental flow requirements, 5) annual and seasonal the technical increase potential (via design discharge optimisation) and 6) annual and seasonal changes in the hydrological cycle.

en: environmental-flow run-of-river-power-plants climate-change hydropower design-discharge

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: RoRCC

title_for_slug: rorcc

design-discharge

display_name: design-discharge

environmental-flow

display_name: environmental-flow

hydropower

display_name: hydropower

run-of-river-power-plants

display_name: run-of-river-power-plants


en:
Based on the detailed tree stump inventory implemented in the Swiss NFI5 (https://www.lfi.ch/lfi/lfi.php), a study was conducted to obtain an accurate assessment of the stumps pool in the Swiss NFI over the last 30 years and to identify its significance for the total dead wood (DW) pool. The NFI5 includes a detailed stump inventory to improve accuracy and completeness of the above-ground DW- pool. Based on available data, stump volume estimates were derived at different accuracies to evaluate the contribution to the total DW-pool over time.
The study found that in Swiss Forests the contribution of stumps to total DW-pool is approximately 25%, and that applying simplifying assumptions to estimate stump volume can result in a significant underestimation of the true size of this pool. This study demonstrates that stumps can be a significant proportion of DW in forests, which should be accounted for in order to improve accuracy and completeness of NFI estimates and derived data such as C stocks for greenhouse gas reporting.
The study is published in Annals of Forest Science (2022) 79:34, https://doi.org/10.1186/s13595-022-01155-7 (open access).
The data can be obtained from the authors upon reasonable request.

en: biomass volume protection-forest carbon national-forest-inventory biodiversity

en: Stumps as a dead wood resource in forests - data based on the Swiss National Forest Inventory

envi

en: Environment

en:
Resource Title: Artikel - Annals of Forest Science
    Size: 1673213 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/dd13b5a9-758e-4416-ac74-446370307374/download/didionabegg2022.pdf
    Description:

en:
Resource Title: NFI5
    Size: 488946 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/b27ae215-7eff-4546-9d08-b0c15d82a6e0/download/nfi5.zip
    Description: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI5

en:
Resource Title: NFI4
    Size: 526483 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/94d92a0e-4b3a-43e0-9c1b-1293acda5938/download/nfi4.zip
    Description: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI4

en:
Resource Title: NFI1
    Size: 42999 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/9f931dea-8d9b-4d16-b8bd-d933a98e8d9e/download/nfi1.zip
    Description: Data on dead standing and downed trees for the NFI1

en:
Resource Title: ReadMe_Files
    Size: 1278 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/8de64d6a-4d7a-4233-a5ab-c4485b1f40a6/download/readme_files.csv
    Description: Files and contents

en:
Resource Title: NFI3
    Size: 482387 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/f8dc0cfb-50f1-4f74-b428-27b8a690e406/download/nfi3.zip
    Description: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI3

en:
Resource Title: Lanz 2014
    Size: 105307 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/72f417d6-3d19-4790-831c-9511f2169b8d/download/international_de.pdf
    Description: A short overview over stump assessment (field protocol) in some European NFIs

en:
Resource Title: ReadMe_Variables
    Size: 3109 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/82cad928-4741-415e-a3c7-f19ca4a0d72e/download/readme_variables.csv
    Description: Variable definitions for the datasets NFI1, NFI2, NFI3, NFI4 and NFI5

en:
Resource Title: NFI2
    Size: 78643 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/69071e62-9dc2-4b61-91c1-322b0c340a60/download/nfi2.zip
    Description: Data on dead standing and downed trees for the NFI2

en:
Resource Title: Site Description
    Size: 510752 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/723bfcc7-f2b5-4030-94d5-86bac0f52250/download/sites_2022-03-09.csv
    Description: information on NFI sites throughout the five NFIs considered in this study

biodiversity

display_name: biodiversity

biomass

display_name: biomass

carbon

display_name: carbon

national-forest-inventory

display_name: national-forest-inventory

protection-forest

display_name: protection-forest

volume

display_name: volume


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

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

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

en:
Resource Title: Manual snow pits and other snow density measurements
    Size: 30761 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/b234793d-1a5f-47f8-a7e2-c9e6099dbb7a/download/snowpits.txt
    Description: Manual snow pits and other snow density measurements

format: TXT

media_type: text/plain

en:
Resource Title: Documentation SnowMicroPen measurements
    Size: 94028379 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/4c1ab2e9-cfad-463b-bf94-b9d55794371a/download/documentation.zip
    Description: This *.zip archive contains all available documentation of the SnowMicroPen measurements, including the mapping of the SMP measurements in the terrestrial laser scan field at PEA (see documentation for 20161220) and at the Hammarryggen Ice Rise (see documentation for 20190104 and 20190111).

en:
Resource Title: Python code exported from the Jupyter notebook for processing the SnowMicroPen files
    Size: 5439 bytes
    Format: text/x-python
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/80f9bd9a-ba5f-461d-87ad-b0d65b0bb7ed/download/smp.py
    Description: Python code exported from the Jupyter notebook for processing the SnowMicroPen files

en:
Resource Title: List of SnowMicroPen measurements
    Size: 182060 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/f993bafd-41b1-4978-884b-ee7e721a7f95/download/smp_list.txt
    Description: List of all SnowMicroPen measurements, including date and location, and processing status. This list has been generated by the python code (see other resources in this dataset).

en:
Resource Title: SnowMicroPen pts files and processed txt files
    Size: 579091098 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/3072110f-5284-4b7c-84c7-81ed98d3e6ee/download/smp_files.zip
    Description: Zip archive containing the acquired *.pts files (binary files recorded by the SnowMicroPen), and the converted *.txt files using the SMP.ipynb jupyter notebook (see the python code in the other resources in this dataset)

en:
Resource Title: Jupyter notebook for processing the SnowMicroPen files
    Size: 7240 bytes
    Format: application/x-ipynb+json
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/b18e5359-0134-47d6-aaf9-9f5a04a71543/download/smp.ipynb
    Description: Jupyter notebook for processing the SnowMicroPen files.

microstructure

display_name: microstructure

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen


en: This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the "WaMos meets LFI" (WML) project.

en: visual-attractiveness nfi-photos online-questionnaire-survey perception-of-ecosystem-services adults-and-teenagers

envidat

en: EnviDat: the Environmental Data Portal

en: Relationship between physical forest characteristics, visual attractiveness and perception of ecosystem services in urban forests

envi

en: Environment

en:
Resource Title: Questionnaire Italian
    Size: 10955256 bytes
    Format: application/pdf
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    Description:

en:
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    Description:

en:
Resource Title: Dataset in csv-format
    Size: 5628069 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/ced93b4f-1424-4a54-ad92-753d96133fab/download/umfrage_mitlfi2_2.csv
    Description:

en:
Resource Title: Questionnaire German
    Size: 15478868 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/31a62f4f-08ae-4baa-98af-f742ea96bc1d/download/umfrageerwachsenedeutsch.pdf
    Description:

en:
Resource Title: Codebook to SPSS-file
    Size: 325674 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/8a7f9645-ca1b-4fb3-8d9c-94240c0e235e/download/codebookwmlbilderstudie.pdf
    Description:

en:
Resource Title: Questionnaire French
    Size: 16449968 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/ee508ecd-8d0f-4be5-b985-69acfdc3fb24/download/umfrageerwachsenefranzosisch.pdf
    Description:

adults-and-teenagers

display_name: adults-and-teenagers

nfi-photos

display_name: nfi-photos

online-questionnaire-survey

display_name: online-questionnaire-survey

perception-of-ecosystem-services

display_name: perception-of-ecosystem-services

visual-attractiveness

display_name: visual-attractiveness


disdrometer-data-laret

en:
A laser optical disdrometer (Parsivel&178; , OTT Hydromet) was used to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms.
The dataset contains information on precipitation amount and type for the period of February 7 to March 29 2019 in Laret.

en: precipitation disdrometer raclets hydrometeors visibility

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: Disdrometer Data Laret

title_for_slug: disdrometer-data-laret

disdrometer

display_name: disdrometer

hydrometeors

display_name: hydrometeors

precipitation

display_name: precipitation

raclets

display_name: raclets

visibility

display_name: visibility


en:
The wind-driven saltation of sand and snow shapes dunes and ripples, generates dust emission, and erodes the surface of the Antarctic ice sheet. Here, we use a model based on the discrete element method to simulate grain-flow interactions and study the effect of particle cohesion on saltation dynamics.
The data contains the model output of granular splash simulations and saltation simulations. Granular splash, the main particle entrainment process in saltation, occurs upon impact of saltating particles with the granular bed. We performed Monte Carlo simulations of granular splash for loose sand grains and for cohesive ice grains. The analysis indicate that different values of cohesion have significant effects not on the number of splashed grains, on the ejection velocity, and the rebound velocity.
In our saltation simulations, we trigger particle movement with a single splash event at the inlet section section and let the system evolve until steady state. Our results show that saltation over cohesive surfaces is difficult to initiate but easy to sustain at low wind speed. The occurrence of transport thus depends on the history of the wind speed, a phenomenon known as hysteresis. We also show that saltation over cohesive surfaces presents higher mass fluxes but requires longer distances to saturate, which increases the size of the smallest stable surface ripples. Our model results have implications for large-scale aeolian processes on Earth and Titan, where sand grains are thought to be very cohesive.

en: cohesion saltation snow wind sediment sand

en: Saltation of cohesive granular materials

cohesion

display_name: cohesion

saltation

display_name: saltation

sand

display_name: sand

sediment

display_name: sediment

snow

display_name: snow

wind

display_name: wind


disdrometer-data-gotschnagrat

en: A laser optical disdrometer (Parsivel² , OTT Hydromet) was deployed at Gotschnagrat (LON: 9.849, LAT: 46.859) to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms. The dataset contains information on precipitation amount and type for the period of February 11 to March 27 2019 at Gotschnagrat.

en: disdrometer raclets hydrometeors visibility precipitation

en: Disdrometer Data Gotschnagrat

title_for_slug: disdrometer-data-gotschnagrat


en:
This repository contains the source code of the analysis presented in the related paper. The code can be found in the following github repository: https://github.com/Chelmy88/temporal_downscaling
This code can be used to perform temporal downscaling of meteorological time series from daily to hourly time steps and to perform the quality assessment described in the paper.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

en: delta-change r-script quantile-mapping alpine-regions imis python switzerland temporal-downscaling climate-change-scenarios meteoswiss

envidat

en: EnviDat: the Environmental Data Portal

en: Source code for: Climate change scenarios at hourly time-step over Switzerland from an enhanced temporal downscaling approach

envi

en: Environment

en:
Resource Title: 2_assessment
    Size: 12817 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/73aa91a9-61b6-473c-9962-8d4c28016191/resource/8990361d-87b0-4320-9d79-7788832ffa1d/download/2_assessment.zip
    Description: Python and R scripts to perform the data quality analysis

en:
Resource Title: 1_downscaling
    Size: 13945 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/73aa91a9-61b6-473c-9962-8d4c28016191/resource/3aa56f6a-4e60-4ba7-866a-e27b06315bea/download/1_downscaling.zip
    Description: Python scripts to perform the temporal downscaling

en:
Resource Title: README
    Size: 2098 bytes
    Format: text/markdown
    URL: https://www.envidat.ch/dataset/73aa91a9-61b6-473c-9962-8d4c28016191/resource/0ebfca71-d997-4586-93fe-7b6fcce8bc0f/download/readme.md
    Description:

format: markdown

en:
Resource Title: LICENSE
    Size: 35149 bytes
    Format: No Info
    URL: https://www.envidat.ch/dataset/73aa91a9-61b6-473c-9962-8d4c28016191/resource/f8e60185-27c1-4abf-ba3e-e272d86c52c8/download/license
    Description:

alpine-regions

display_name: alpine-regions

climate-change-scenarios

display_name: climate-change-scenarios

delta-change

display_name: delta-change

imis

display_name: imis

meteoswiss

display_name: meteoswiss

python

display_name: python

quantile-mapping

display_name: quantile-mapping

r-script

display_name: r-script

switzerland

display_name: switzerland

temporal-downscaling

display_name: temporal-downscaling


en:
Dataset description-br /-
This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following:
 * a)	Barcelona city boundaries
 * b)	Barcelona metropolitan area, Àrea Metropolitana de Barcelona (AMB)
 * c)	Barcelona greater city (Urban Atlas)
 * d)	Barcelona functional urban area (Urban Atlas)
 * e)	Milan city boundaries
 * f)	Milan metropolitan area, Piano Intercomunale Milanese (PIM)
 * g)	Milan greater city (Urban Atlas)
 * h)	Milan functional urban area (Urban Atlas)-br /-
In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2). -br /- -br /-
-br /- Dataset composition-br /-
The dataset is provided in .csv format and is composed of: -br /-
_IMD15_BCN_MI_Sources.csv_: Information on data sources -br /-
 _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona:
 * a)	Barcelona city boundaries (label: bcn_city)
 * b)	Barcelona metropolitan area, Àrea metropolitana de Barcelona (AMB) (label: bcn_amb)
 * c)	Barcelona greater city (Urban Atlas) (label: bcn_grc)
 * d)	Barcelona functional urban area (Urban Atlas) (label: bcn_fua)-br /-
 _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan:
 * e)	Milan city boundaries (label: mi_city)
 * f)	Milan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim)
 * g)	Milan greater city (Urban Atlas) (label: mi_grc)
 * h)	Milan functional urban area (Urban Atlas) (label: mi_fua)-br /-
_IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h). -br /-
Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM. -br /-
In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included:
 * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan);
 * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area);
 * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness;
 * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.-br /- -br /-
-br /- Further information on the Dataset-br /-
This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.-br /-
 1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster.
 2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA.
 3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.-br /-
Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h). -br /-
Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been ‘crossed’ (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following:
 * Clipping: Arctoolbox - Data management tools - Raster - Raster Processing - Clip. The ‘input’ file is the HRL IMD raster file as described in point 1) and the ‘output’ file is each of the spatial/territorial files. The option "Use Input Features for Clipping Geometry (optional)” was selected for each of the clipping.
 * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox - Data management tools - Raster - Raster properties - Delete Raster Attribute Table and then through Arctoolbox - Data management tools - Raster - Raster properties - Build Raster Attribute Table; the "overwrite" option has been selected. -br /-
-br /- Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal - Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.-br /-
The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.

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

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: Soil sealing Barcelona and Milan different territorial levels

en:
Resource Title: IMD15_BCN_MI
    Size: 9649908 bytes
    Format: .mpk
    URL: https://www.envidat.ch/dataset/34fc9c0c-1c90-4f87-b3a9-76cc0e888eda/resource/feb17f6a-9d3d-49d7-b072-654e3d210975/download/imd15_bcn_mi.mpk
    Description: Shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h), see Dataset description

en:
Resource Title: IMD15_BCN_MI_Sources
    Size: 2730 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/34fc9c0c-1c90-4f87-b3a9-76cc0e888eda/resource/d023c81b-4318-492a-abbf-44e429e9855d/download/imd15_bcn_mi_sources.csv
    Description: Information on data sources

en:
Resource Title: IMD15_MI
    Size: 9229 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/34fc9c0c-1c90-4f87-b3a9-76cc0e888eda/resource/e4750d6b-e92c-4266-8d15-a04a673ba248/download/imd15_mi.csv
    Description: 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan

en:
Resource Title: IMD15_BCN
    Size: 9402 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/34fc9c0c-1c90-4f87-b3a9-76cc0e888eda/resource/b49ab377-1058-44b0-9db3-eae5af4bb518/download/imd15_bcn.csv
    Description: 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona

barcelona

display_name: barcelona

copernicus

display_name: copernicus

imperviousness

display_name: imperviousness

milan

display_name: milan

soil-sealing

display_name: soil-sealing

urban-atlas

display_name: urban-atlas


en:
This data set provides estimates of annual fresh water fluxes related to sea-ice formation from ocean freezing and snow-ice formation, sea-ice melting, lateral transport of sea ice in the Southern Ocean over the period 1982 to 2008.It is derived from a mass balance calculation of local sea-ice volume change and divergence from satellite data and sea-ice reconstructions. The mass balance is calculated on a daily basis and fluxes are then integrated over the entire year, where a year is defined from March to February of the next year (i.e. from March 1982 to February 2009). This approach combines multiple products of sea-ice concentration (Cavalieri & Parkinson, 2008;Comiso, 1986; Meier et al., 2013), sea-ice thickness (Kurtz & Markus, 2012; Massonnet et al., 2013; Worby et al., 2008), and sea-ice drift (Fowler et al., 2013; Kwok 2005; Schwegmann et al., 2011). For a detailed description of the method see Haumann et al. (2016). The data set is derived to estimate large-scale (regional to basin-scale) fluxes on an annual basis. Our confidence is reduced on a grid cell basis, such as for single coastal polynyas, where the method and underlying data induce large, unknown uncertainties.
_Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues._
Related publication: http://www.nature.com/nature/journal/v537/n7618/full/nature19101.html (doi:10.1038/nature19101)
Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues.

en: satellite cryospheric-science physical-oceanography sea-ice-volume climate-change mass-balance polar salt salinity melting forcing freezing sea-ice freshwater-flux transport southern-ocean antartic sea-ice-divergence

en: Antarctic sea-ice freshwater fluxes associated with freezing, transport, and melting

en:
Resource Title:  AnIceFlux_v01_clim_mean_1982-2008.nc
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    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/b16a0901-3abe-4da0-b61e-cdb80c59ba63/resource/6a315e6d-4c92-4f85-b131-92fd87853b65/download/aniceflux_v01_clim_mean_1982-2008.nc
    Description: Data in NetCDF4 CF-1.6 format

en:
Resource Title: Explanatory Document
    Size: 379128 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/b16a0901-3abe-4da0-b61e-cdb80c59ba63/resource/a2d8430f-dc92-4a03-b3d6-6661b553bdd1/download/aniceflux_v01.pdf
    Description:

en:
Resource Title: AnIceFlux_v01_annual_1982-2008.nc
    Size: 1493303 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/b16a0901-3abe-4da0-b61e-cdb80c59ba63/resource/5de37dd3-e6a7-41b4-b97a-21b14734b861/download/aniceflux_v01_annual_1982-2008.nc
    Description: Data in NetCDF4 CF-1.6 format

en:
Resource Title: Link to Related Publication
    Size: 1 bytes
    Format: application/pdf
    URL: http://www.nature.com/articles/nature19101.epdf?author_access_token=dtLqMQ3Ss6p16tymoagae9RgN0jAjWel9jnR3ZoTv0PH2BR1NrVw3zxcEnl7ViU5pKp8iHAlc_ntQx82EiRMqFRuGOR0VueOUVoGWzy8lSzML6YUBUMMZ5f4LYCdK8Ca
    Description:

antartic

display_name: antartic

climate-change

display_name: climate-change

cryospheric-science

display_name: cryospheric-science

forcing

display_name: forcing

freezing

display_name: freezing

freshwater-flux

display_name: freshwater-flux

mass-balance

display_name: mass-balance

melting

display_name: melting

physical-oceanography

display_name: physical-oceanography

polar

display_name: polar

salinity

display_name: salinity

salt

display_name: salt

satellite

display_name: satellite

sea-ice

display_name: sea-ice

sea-ice-divergence

display_name: sea-ice-divergence

sea-ice-volume

display_name: sea-ice-volume

southern-ocean

display_name: southern-ocean

transport

display_name: transport


en:
The R script IRFnnhs.R, which efficiently estimates impulse response functions for environmental systems that are nonlinear, nonstationary, or heterogeneous, based on their input and output time series.
Scripts and results for a series of benchmark tests are also provided, to accompany Kirchner, J.W., Impulse response functions for heterogeneous, nonstationary, and nonlinear systems, estimated by deconvolution and demixing of noisy time series, _Sensors_, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022.

en: greens-function nonlinear-deconvolution irf impulse-response-function time-series-analysis transfer-function arma-noise deconvolution de-mixing system-identification irfnnhsr

envidat

en: EnviDat: the Environmental Data Portal

en: Impulse response functions for nonlinear nonstationary and heterogeneous systems

envi

en: Environment

en:
Resource Title: README file for IRFnnhs.R and benchmark demonstration scripts
    Size: 1315 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/c093641a-874e-4f8e-a04d-62a07d3855e1/resource/de609e80-41d7-41d8-8c73-a58d2c2fdc83/download/irf-code-folder-readme.txt
    Description:

en:
Resource Title: Benchmark demonstration scripts and results
    Size: 27341717 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c093641a-874e-4f8e-a04d-62a07d3855e1/resource/b2a57bb7-8466-4b71-9837-d500e8fa0226/download/benchmark-demonstration-scripts-and-results.zip
    Description:

en:
Resource Title: Impulse Response Function script
    Size: 123160 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/c093641a-874e-4f8e-a04d-62a07d3855e1/resource/7fb322da-6bd9-4e68-a8e7-33383b1db36c/download/irfnnhs.r
    Description:

arma-noise

display_name: arma-noise

de-mixing

display_name: de-mixing

deconvolution

display_name: deconvolution

greens-function

display_name: greens-function

impulse-response-function

display_name: impulse-response-function

irf

display_name: irf

irfnnhsr

display_name: irfnnhsr

nonlinear-deconvolution

display_name: nonlinear-deconvolution

system-identification

display_name: system-identification

time-series-analysis

display_name: time-series-analysis

transfer-function

display_name: transfer-function


modeling-snow-failure-with-dem

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

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

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: Modeling snow failure with DEM

title_for_slug: modeling-snow-failure-with-dem

en:
Resource Title: Read-me
    Size: 174785 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/a3ace234-0794-4b75-bf17-3790744ed665/resource/8142e5f2-74cd-49ae-b64f-87d9600b8b53/download/read_me.pdf
    Description: The read-me file contains a description of all the data provided in the zip file.

en:
Resource Title: Python file
    Size: 32003 bytes
    Format: text/x-python
    URL: https://www.envidat.ch/dataset/a3ace234-0794-4b75-bf17-3790744ed665/resource/bcd1cf92-6c73-4bf4-83fb-96b8fc7c93b9/download/plot_figures.py
    Description: Python file to reproduce the figures in Bobillier et al. (2019)

en:
Resource Title: Dataset
    Size: 268629 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a3ace234-0794-4b75-bf17-3790744ed665/resource/73b649d7-b035-4815-b97a-bd8ecdf97a27/download/data.zip
    Description: The zip file contains the data underlying the figures presented in Bobillier et al. (2019). The figures can be reproduced with the python file, which is also provided.

discrete-element-modelling

display_name: discrete-element-modelling

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-micro-mechanics

display_name: snow-micro-mechanics


en:
This dataset includes discharge and rainfall measurements and deuterium compositions of streamflow, rainfall and groundwater, for four rainfall events and three baseflow snapshot campaigns in the Studibach (Alptal, Switzerland). More specifically, we present the following data:
- Specific discharge at the catchment outlet at 5-minute resolution (mm per hour);
- Rainfall at 5-minute resolution (mm per hour);
- Rainfall deuterium composition (‰);
- Stormflow deuterium composition (‰);
- Groundwater and baseflow deuterium compositions (‰).
For the files containing rainfall and discharge timeseries (QP), and rainfall and streamwater deuterium compositions ("Deuterium_Rainfall" and "Deuterium_Streamwater"), we added the corresponding event identifier (A, B, C or extra) to the file names. For the files containing the groundwater and baseflow deuterium values ("Deuterium_Snapshot") we added the sample collection date to the file name. We included the X and Y coordinates for each data point (coordinate system: CH1903 LV3) as well as the date and time (UTC).
More information on the data collection and preparation can be found in Kiewiet et al. (in review). A detailed description of the baseflow snapshot campaigns can also be found in Kiewiet et al., 2019.

en: runoff stable-water-isotopes groundwater streamflow alptal

en: Discharge, rainfall, and deuterium compositions of streamwater, rainwater and groundwater, for four rainfall events in the Studibach, Alptal, Switzerland

en:
Resource Title: Deuterium streamwater extra event
    Size: 1907 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/c09c3cd0-6a4b-4e3d-aa6e-7541241be20a/download/deuterium_streamwater_extraevent.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date and time of sample collection (UTC; year-month-day, hours:minutes)
Q d.D: Deuterium composition (per mil) of streamwater sampled at the catchment outlet (697234,210472); the first sample is used as pre-event streamflow sample

en:
Resource Title: Deuterium streamwater event B
    Size: 1300 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/2e8bed6f-98be-47ef-8976-2df51703db3f/download/deuterium_streamwater_eventb.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date and time of sample collection (UTC; year-month-day, hours:minutes)
Q d.D: Deuterium composition (per mil) of streamwater sampled at the catchment outlet (697234,210472); the first sample is used as pre-event streamflow sample

en:
Resource Title: Precipitation and discharge event C
    Size: 8146 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/684ec1fc-df96-42ab-ba3c-0b4f981776ad/download/qp_eventc.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
With any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
Q: Discharge in mm per hour at catchment outlet (697234,210472)
P: Rainfall in mm per hour averaged for two rain gauges (697494,210675) and (698004, 210680)

en:
Resource Title: Deuterium streamwater event C
    Size: 1246 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/2b90dbcd-8fd7-440d-b0e0-4b724d688dc5/download/deuterium_streamwater_eventc.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date and time of sample collection (UTC; year-month-day, hours:minutes)
Q d.D: Deuterium composition (per mil) of streamwater sampled at the catchment outlet (697234,210472); the first sample is used as pre-event streamflow sample

en:
Resource Title: Deuterium rainfall event B
    Size: 880 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/98b1bd86-e4a4-46f9-a75a-e871cab67aad/download/deuterium_rainfall_eventb.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
P d.D: Deuterium composition of the rainfall samples (per mil)
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)

en:
Resource Title: Deuterium streamwater event A
    Size: 1273 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/2add59a1-5621-4b0b-949e-e144e66de9e8/download/deuterium_streamwater_eventa.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date and time of sample collection (UTC; year-month-day, hours:minutes)
Q d.D: Deuterium composition (per mil) of streamwater sampled at the catchment outlet (697234,210472); the first sample is used as pre-event streamflow sample

en:
Resource Title: Precipitation and discharge event A
    Size: 13862 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/2d765186-0ae0-4b43-bc34-cbad9edba879/download/qp_eventa.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
With any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
Q: Discharge in mm per hour at catchment outlet (697234,210472)
P: Rainfall in mm per hour averaged for two rain gauges (697494,210675) and (698004, 210680)

en:
Resource Title: Deuterium groundwater and baseflow October 2016
    Size: 2170 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/73fc4834-881f-4547-9b7b-52ca2be61f71/download/deuterium_snapshot_oct2016.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

Date: Date (year-month-day)
Location: Well number or outlet
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)
riparian: riparian wells (i.e. from the riparian-like cluster as presented in Kiewiet et al. (2019)) are marked a one, all other wells are marked a zero
d.D (per mil): Deuterium composition (per mil)

en:
Resource Title: Deuterium groundwater and baseflow October 2017
    Size: 2208 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/cc4b2f2b-537d-49e2-ac0a-c82216ad99d9/download/deuterium_snapshot_oct2017.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

Date: Date (year-month-day)
Location: Well number or outlet
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)
riparian: riparian wells (i.e. from the riparian-like cluster as presented in Kiewiet et al. (2019)) are marked a one, all other wells are marked a zero
d.D (per mil): Deuterium composition (per mil)

en:
Resource Title: Deuterium groundwater and baseflow August 2017
    Size: 2022 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/287f9bcc-b876-4170-bce7-1e508982c8b9/download/deuterium_snapshot_aug2017.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

Date: Date (year-month-day)
Location: Well number or outlet
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)
riparian: riparian wells (i.e. from the riparian-like cluster as presented in Kiewiet et al. (2019)) are marked a one, all other wells are marked a zero
d.D (per mil): Deuterium composition (per mil)

en:
Resource Title: Deuterium rainfall extra event
    Size: 2228 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/2f6feb1a-ccfa-4aac-bacd-335760905550/download/deuterium_rainfall_extraevent.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
P d.D: Deuterium composition of the rainfall samples (per mil)
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)

en:
Resource Title: Precipitation and discharge event B
    Size: 9733 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/51bf0d6f-a68c-4e75-b5a1-1c81bbc7a9ea/download/qp_eventb.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
With any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
Q: Discharge in mm per hour at catchment outlet (697234,210472)
P: Rainfall in mm per hour averaged for two rain gauges (697494,210675) and (698004, 210680)

en:
Resource Title: Deuterium rainfall event C
    Size: 1044 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/993b5d45-b0aa-4bf8-a9b9-1a8fc950ba7a/download/deuterium_rainfall_eventc.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
P d.D: Deuterium composition of the rainfall samples (per mil)
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)

en:
Resource Title: Deuterium rainfall event A
    Size: 960 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/712f8d2e-129c-4ed4-ae19-eab3d2836511/download/deuterium_rainfall_eventa.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
P d.D: Deuterium composition of the rainfall samples (per mil)
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)

en:
Resource Title: Precipitation and discharge extra event
    Size: 45996 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd1a478e-23f4-49e5-a240-c1985147bf27/resource/6983ae41-90ad-4000-9b13-d6a4942e38cb/download/qp_extraevent.csv
    Description: This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
With any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
Q: Discharge in mm per hour at catchment outlet (697234,210472)
P: Rainfall in mm per hour averaged for two rain gauges (697494,210675) and (698004, 210680)

alptal

display_name: alptal

groundwater

display_name: groundwater

runoff

display_name: runoff

stable-water-isotopes

display_name: stable-water-isotopes

streamflow

display_name: streamflow


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

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

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: Species distribution maps of Fagales and Pinales (GDPlants)

envi

en: Environment

en:
Resource Title: Species range maps of species in Fagales and Pinales (with enough occurrences)
    Size: 37517772 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/aee8c2bd-ca0b-4de0-a41f-940dc11984e6/resource/6c946cd2-e08b-44e0-8fda-4ab01b6b43b9/download/species_maps.zip
    Description: species_map.zip includes species 1690 range maps generated based on the integrated method described in Lyu et al., (2022). File names with (polygon) indicate that this map is only generated by polygon mapping, as there are not enough occurrences for SDMs.

en:
Resource Title: Occurrence maps for species of Fagales and Pinales with few records
    Size: 32315283 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/aee8c2bd-ca0b-4de0-a41f-940dc11984e6/resource/9239265c-dba9-413d-9fc6-40beb3cb7368/download/point_buffers.zip
    Description: Point_buffer.zip includes 267 occurrence maps of species that do not have enough occurrence data for polygon mapping.

fagales

display_name: fagales

pinales

display_name: pinales

species-distribution-model

display_name: species-distribution-model

species-occurrence

display_name: species-occurrence

species-range-map

display_name: species-range-map

species-richness

display_name: species-richness


en:
These data include all avalanches that were mapped in the region of Davos, Switzerland during the winters 1998-1999 to 2018-2019 (21 years), in total 13,918 avalanches, and the corresponding forecast danger level valid on the day of avalanche occurrence, 3533 days and danger ratings in total.
This avalanche activity data set was analysed and results published by Schweizer et al. (2020). They found that the
number of avalanches per day strongly increased with increasing danger level, but avalanche size was poorly related  to avalanche danger level.
The data are provided in two files: the first includes the avalanche data (13,918 records); the second includes the avalanche activity per day (3533 records).
Please refer to the Read-me file for further details on the data.
These data are the basis of the following publication:
Schweizer, J., Mitterer, C., Techel, F., Stoffel, A. and Reuter, B., 2020. On the relation between avalanche occurrence and avalanche danger level. The Cryosphere, 14, 737-750, https://doi.org/10.5194/tc-14-737-2020.

en: avalanche-activity avalanche-forcasting avalanche-size danger-level snow-avalanche

en: Snow avalanche data Davos, Switzerland, 1999-2019

en:
Resource Title: Readme
    Size: 159745 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/ce11efbe-4dac-4ff5-9a3d-f01e2c573292/resource/f8d6835f-3ef6-4fe6-94a2-6b7f7b5827ce/download/read_me.pdf
    Description: The Readme file contains the description of the two data files.

en:
Resource Title: Avalanche activity data
    Size: 929518 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ce11efbe-4dac-4ff5-9a3d-f01e2c573292/resource/39e6dd9e-9b52-439e-a4d1-5e17a8e18eca/download/data_set_2_danger_avalanches.csv
    Description: This data file includes data on the avalanche activity per day aggregated from the avalanche data.

en:
Resource Title: Snow avalanche data
    Size: 936455 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ce11efbe-4dac-4ff5-9a3d-f01e2c573292/resource/4c2b7c38-a874-45fc-9833-fdf83823067b/download/data_set_1_avalanche_observations_wi9899_to_wi1819_davos.csv
    Description: This data file includes data on the avalanches mapped in the region of Davos.

avalanche-activity

display_name: avalanche-activity

avalanche-forcasting

display_name: avalanche-forcasting

avalanche-size

display_name: avalanche-size

danger-level

display_name: danger-level