maintainer: Michael Lehning

en: Data Source

en: Hourly Gridded Wind Power Time Series

en: Description

en: Full Data Description

en: Coordinates of Grid Points

en:
This dataset consist of simulated hourly power production from an Enercon E82 Turbine at 100 m hub-height. It describes the hourly power output a 1MW turbine would produce in each 0.01° grid cell for the years 2016 and 2017.

100 m wind speed data was taken from the COSMO-1 model (Consortium for Small-scale Modeling 2017), which has a 0.01° horizontal resolution. The domain covered is the whole of Switzerland, with the exclusion of lakes. As such, the number of 0.01◦ pixels within Switzerland amounts to 48657.
Conversion to power output was done based on the power curve of the Enercon E82 Turbine. As power output is lower at altitude due to lower air density, we corrected for this effect as described in (Kruyt et al. 2017).

Please cite the following paper in connection with the dataset:

Paper Citation:
 > _Bert Kruyt, Jérôme Dujardin, and Michael Lehning: Improvement of wind power assessment in complex terrain: The case of COSMO-1 in the Swiss Alps, Front. Energy Res., [doi:10.3389/fenrg.2018.00102] (https://doi.org/10.3389/fenrg.2018.00102)_

mountain-wind-potential

display_name: mountain-wind-potential

topography

display_name: topography

wind-energy

display_name: wind-energy

educ

en: Education, culture and sport

en: Dataset on Cosmo-1 based Energy Potential in Swiss Alps


maintainer: EnviDat Support

en: microclimate ecology radiation-transfer statistical-interpolation climate-change biodiversity forest

state: active

type: dataset

media_type: application/zip

en: Predicted microclimate air temperatures at 5 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

format: ZIP

en: Predicted microclimate air temperatures at 100 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Predicted microclimate topsoil temperatures at 5 cm below ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate-change impacts on biodiversity and ecosystem functioning. Here we combine a national network of microclimate temperature measurements with a novel radiative transfer model to map monthly minimum, mean and maximum temperatures during the vegetation period at a 10 meter spatial resolution across Switzerland. The temperature measurements took place in 107 sampling plots distributed across different habitat types, with 62 plots in forests, 22 below trees outside forests, and 23 in open grasslands. In each plot we measured temperature in the topsoil (-5cm), as well as in the air at 5cm and 100cm height above ground. Spatial interpolation was achieved by using a hybrid approach based on linear mixed effects models with input from detailed radiation estimates that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography and vegetation height. Our data reveals strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 °C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 °C. We also found that in open grasslands, maximum temperatures at 5 cm above ground are on average 3.4 °C warmer than that of macroclimate, suggesting that in such habitats heat exposure close to the ground is often underestimated when using macroclimatic data. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by RSME’s ranging from 1.18 to 3.43 °C, with minimum temperatures generally being predicted more accurately than maximum temperatures. The microclimate maps presented here enable a more biologically relevant perspective when analysing climate-species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

biodiversity

display_name: biodiversity

climate-change

display_name: climate-change

ecology

display_name: ecology

forest

display_name: forest

microclimate

display_name: microclimate

radiation-transfer

display_name: radiation-transfer

statistical-interpolation

display_name: statistical-interpolation

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Monthly topsoil and near surface microclimate temperature data for Switzerland


en: ground-vegetation climate-change forest fagus-sylvatica forest-management air-pollution

en: Site Information

en: This table contains information about the 254 forest sites used in the publication of Scherrer et al. 2024.

format: XLS

en: Taxa Data

en: This table contains information about the individual taxa found in the historic and current vegetation relevés in Scherrer et al. 2024.

en: This dataset comprised 254 historical (1937 – 1948) and resurveyed (2019 – 2022) vegetation relevés of beech-dominated forests sites in the Swiss Jura mountains. The data contains species lists as well as site information.

air-pollution

display_name: air-pollution

fagus-sylvatica

display_name: fagus-sylvatica

forest-management

display_name: forest-management

ground-vegetation

display_name: ground-vegetation

en: Resurveyed vegetation releves of beech forests in the Swiss Jura Mountains


en: nutrient mass-balance sustainability soil beech

en: With this study, our aim was to estimate the nutrient fluxes relevant for assessing nutrient sustainability as accurately as possible and to calculate nutrient balances for alternative forest management scenarios. Furthermore, we tested whether mapping units from existing geologic maps can serve as a basis for forest practitioners to estimate nutrient sustainability or whether more detailed data is needed. Positive fluxes include deposition and weathering, while negative fluxes include losses due to leaching and nutrient removal through timber harvesting in the balance. Weathering and leachate losses were modeled with a geochemical model. The SwissStandSim model was used to simulate the biomass growth under different harvesting and silvicultural strategies, allowing sustainability to be assessed for each nutrient at a given intensity of use. This assessment was made per rotation period of 100 years based on two criteria: i) nutrient supply; and ii) total stocking volume.

beech

display_name: beech

mass-balance

display_name: mass-balance

nutrient

display_name: nutrient

soil

display_name: soil

sustainability

display_name: sustainability

educ

en: Education, culture and sport

en: Nutrient sustainability in beech forests


data-interactive-avalanche-segmentation

title_for_slug: data-interactive-avalanche-segmentation

en: interactive deep-learning segmentation avalanche snow

en:
Images and corresponding avalanche annotations used for interactive avalanche segmentation:
SLF train, vali and test
WebNew
GroundPic
UserPic
UIBK dataset: List of images used
Pictures are from SLF (mostly from the Dischma Cams) if not otherwise indicated. If used, the data needs to be properly cited!

avalanche

display_name: avalanche

deep-learning

display_name: deep-learning

interactive

display_name: interactive

segmentation

display_name: segmentation

snow

display_name: snow

en: Data interactive avalanche segmentation


maintainer: Dirk Nikolaus Karger

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

en: CHELSA V1.2 (deprecated)

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

format: GeoTIFF

en: CHELSA Technical Documentation

media_type: application/pdf

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

format: PDF

en: CHELSA V2.1 (current)

en: CHELSA Version 2.1 based on ERA5 forcings and CMIP6

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

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

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

bioclim

display_name: bioclim

climate

display_name: climate

climate-change

display_name: climate-change

climate-layers

display_name: climate-layers

climatology

display_name: climatology

free-climate-data

display_name: free-climate-data

precipitation

display_name: precipitation

temperature

display_name: temperature

envidat

en: EnviDat: the Environmental Data Portal


maintainer: Giulia Francesca Azzurra Donati

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

en: Amphibian species specific landscape resistance maps.

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

format: SERVICE

en: Amphibian monitoring data

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

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

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

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

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

en: Amphibian whole life cycle predictors - 10m resolution

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

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

biodiversity

display_name: biodiversity

circuit-theory

display_name: circuit-theory

ecological-corridors

display_name: ecological-corridors

human-dominated-landscapes

display_name: human-dominated-landscapes

integrated-modelling

display_name: integrated-modelling

urban-spatial-planning

display_name: urban-spatial-planning

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

en: Amphibian and urban-rural landscape data Swiss Lowlands


en: artificial-intelligence biological-invasions computer-vision automatic-detection plants invasive-species

en: Detected occurrences of Senecio inaequidens along highways on the Swiss Plateau. The number of image tiles in which the species was recognized is shown (Fig. 25 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

en: Detected occurrences of Ailanthus altissima along highways on the Swiss Plateau. To reduce false-positives, only images in which the species was recognized in at least three of 16 image tiles are considered as occurrences (Fig. 38 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

en: Detected occurrences of Senecio inaequidens along highways on the Swiss Plateau. To reduce false-positives, only images in which the species was recognized in at least three of 16 image tiles are considered as occurrences (Fig. 40 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

en: Detected occurrences of Ailanthus altissima along highways on the Swiss Plateau. The number of image tiles in which the species was recognized is shown (Fig. 23 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

en:
Transport infrastructure such as **highways** are important corridors for the spread of invasive species. In this project, an automated detection and mapping of **two invasive alien plant species** was developed and applied along highways in the **Swiss Central Plateau**, based on **artificial intelligence (AI, Deep Learning)**. The two target species are **tree of heaven *(Ailanthus altissima)*** and **narrow-leaved ragwort *(Senecio inaequidens)***.
The data is part of the project report and shows the mapping results in **interactive maps**. Technically speaking, these are **self-contained HTML files** that can be displayed with standard web browsers. To reduce the file size, the downloads are provided in zip format.
**Report**: [https://www.mobilityplatform.ch/de/research-data-shop/product/1690](https://www.mobilityplatform.ch/de/research-data-shop/product/1690)
**Project**: [https://www.wsl.ch/en/projects/automated-detection-of-invasive-alien-plants-along-highways/](https://www.wsl.ch/en/projects/automated-detection-of-invasive-alien-plants-along-highways/)

artificial-intelligence

display_name: artificial-intelligence

automatic-detection

display_name: automatic-detection

biological-invasions

display_name: biological-invasions

computer-vision

display_name: computer-vision

invasive-species

display_name: invasive-species

plants

display_name: plants

educ

en: Education, culture and sport

en: Automated detection of invasive alien plants along highways


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

en: information on ingredients of national dishes

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

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

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

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

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

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

agro-food-trade

display_name: agro-food-trade

food-preferences

display_name: food-preferences

food-taste-similarity

display_name: food-taste-similarity

latent-semantic-analysis

display_name: latent-semantic-analysis

national-dishes

display_name: national-dishes

envidat

en: EnviDat: the Environmental Data Portal

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


maintainer: Jürg Schweizer

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

en: Avalanche activity data

media_type: text/csv

en: This data file includes data on the avalanche activity per day aggregated from the avalanche data.

format: CSV

mimetype: text/csv

en: Readme

en: The Readme file contains the description of the two data files.

mimetype: application/pdf

en: Snow avalanche data

en: This data file includes data on the avalanches mapped in the region of Davos.

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.

avalanche-activity

display_name: avalanche-activity

avalanche-forcasting

display_name: avalanche-forcasting

avalanche-size

display_name: avalanche-size

danger-level

display_name: danger-level

snow-avalanche

display_name: snow-avalanche

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: Snow avalanche data Davos, Switzerland, 1999-2019


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

en: Site photo of the forest station (SCB)

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

en: Forest station data (SCB)

en: Data file.

en: Site aerial photo (Schänis)

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

en: Field station data (SCF)

en: Data plot of the forest station (SCB)

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

en: Further details on the data repository

en: Meta data documentation.

en: Data plot of the field station (SCF)

en: Site photo of the field station (SCF)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Schänis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Schänis is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

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


maintainer: Paraskevi Georgakaki

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

en: WRF outputs

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

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

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

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

en: Observational data

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

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

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

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

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

blowing-snow

display_name: blowing-snow

ice-crystal-number-concentration

display_name: ice-crystal-number-concentration

ice-water-content

display_name: ice-water-content

jungfraujoch

display_name: jungfraujoch

liquid-water-content

display_name: liquid-water-content

secondary-ice-production

display_name: secondary-ice-production

swiss-alps

display_name: swiss-alps

wrf

display_name: wrf

envidat

en: EnviDat: the Environmental Data Portal

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

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


en: landslide-early-warning soil-temperature soil-moisture soil-water-matrix-potential soil-water-content

en: In-situ soil moisture measurements, Wasen i.E., steep slope, 2019-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, and two additional soil temperature measurements (oC), air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2019-Dec 2023.
Location: steep hillslope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: Detailed description of soil moisture measurment sites, soil properties and sensor installations (in German)

en: In-situ soil moisture measurements, Napf, 2021-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each. Meteorological variables such as air temperature or precipitation can be received from MeteoSwiss.
Time intervall: 1 hour.
Measurement period: June 2021-Dec 2023.
Location: flat terrain, meadow (official MeteoSwiss station)
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Ranflüh, 2021-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, as well as air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2021-Dec 2023.
Location: hillslope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Wasen i.E., flat location, 2019-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, and two additional soil temperature measurements (oC), air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2019-Dec 2023.
Location: flat terrain, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Langnau i E, 2021-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each. Meteorological variables, such as air temperature or precipitation can be received from MeteoSwiss.
Time intervall: 1 hour. Measurement period: June 2021-Dec 2023.
Location: flat terrain, meadow (measurement site of MeteoSwiss)
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Trub, 2021-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, as well as air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2021-Dec 2023.
Location: slope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: In the context of landslide early warning research, soil moisture (e.g. volumetric soil water content and soil water potential) has been measured at six locations in the Napf-region (Emmental, central Switzerland) since April 2019. Here, we provide the hourly mean values of all sensors from all 6 locations after a comprehensive data control, filtering and aggregation (from 10-min to 1-hour values). A sub-set of these data (for the locations Wasen i.E.) has previously been published in envidat.ch at https://www.doi.org/10.16904/envidat.369 applying the same data post-processing method. The dataset is complemented with meteorological data (locally measured air temperature and precipitation) as well as soil temperature. A detailed description of the sites and measurement installations can be found in a separate report (attached with the data).

landslide-early-warning

display_name: landslide-early-warning

soil-moisture

display_name: soil-moisture

soil-temperature

display_name: soil-temperature

soil-water-content

display_name: soil-water-content

soil-water-matrix-potential

display_name: soil-water-matrix-potential

en: In-situ soil moisture measurements Napf-region


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

en: TreeMig Simulation Environment and Input Data

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

en: TreeMig Tutorial

en: Tutorial for using the TreeMig framework; updated

en: Description input data

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

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

bioclimatic-variables

display_name: bioclimatic-variables

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-model

display_name: forest-model

gui

display_name: gui

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-interaction

display_name: species-interaction

species-migration

display_name: species-migration

treemig

display_name: treemig

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Elisabeth Hafner

en: avalanche-mapping remote-sensing avalanche reliability avalanche-size-estimation

en: AvalancheReliabilityData

en: The .zip contains all data used to conduct the reliability study as well as the results, a detailed description of the included files is contained in the included pdf. All geospatial data is provided in the Swiss Coordinate System: CH1903+ / LV95 (EPSG 2056). For any use of the data the proper citation provided is obligatory!

mimetype: application/x-zip-compressed

en:
This dataset contains the data used and collected to investigate the reliability of avalanche size estimates and avalanche outlines (see related publications). Namely this is

 Avalanche size estimates

* the 10 pictures depicting avalanches
* the survey results from the 170 participants

 Avalanche outlines mapped from oblique photographs
* the pictures of the six avalanches
* the avalanche outlines mapped by the 10 participants
* the reference mapped from the georeferenced photographs

 Avalanche outlines from remotely-sensed imagery
* the orthophotos in the four different resolutions (2m, 1m, 50cm, 25cm)
* the area of interest (AoI) delimiting the research area
* the avalanche mappings in each resolution for all five participants
* the shade masks used to separate illuminated and shaded areas

The acquisition of the orthophotos has been partially supported by the Swiss National Science Foundation (SNF; Grant N°200021_172800).

avalanche

display_name: avalanche

avalanche-mapping

display_name: avalanche-mapping

avalanche-size-estimation

display_name: avalanche-size-estimation

reliability

display_name: reliability

remote-sensing

display_name: remote-sensing

en: Data reliability study: avalanche size estimates and outlines


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

en: Data plot of the forest station (LAB)

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

en: Site aerial photo (Lausanne)

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

mimetype: image/jpeg

en: Site photo of the field station (LAF)

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

en: Forest station data (LAB)

en: Data file.

en: Field station data (LAF)

en: Site photo of the forest station (LAB)

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

en: Data plot of the field station (LAF)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Lausanne in Switzerland where one station is located within a natural mixed forest stand (LAB) with European beech (_Fagus sylvatica_; 160-170 yrs), European silver fir (_Abies alba_; 160-170 yrs) and Norway spruce (_Picea abies_; 160-170 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, LAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lausanne is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

en: Lausanne, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards


en: uav dsm photogrammetry drone-data wingtra

en: Data Access

en:
- DSM 10cm resolution
- Orthomosaic 3cm resolution
- Agisoft Metashape Processing Report
- original RGB images

All generated products are also available as a cloud optimized geotiff.

en: We conducted four drone flights in Davos Wolfgang Arelen, in 2020/21 and 2022 to obtain data for the generation of DSMs and orthomosaics at a high resolution. The data was processed with the Agisoft Metashape Professional Software.

drone-data

display_name: drone-data

dsm

display_name: dsm

photogrammetry

display_name: photogrammetry

uav

display_name: uav

wingtra

display_name: wingtra

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Photogrammetric Drone Data Wolfgang Arelen


maintainer: Lyu

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

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

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

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

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

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

fagales

display_name: fagales

pinales

display_name: pinales

species-distribution-model

display_name: species-distribution-model

species-occurrence

display_name: species-occurrence

species-range-map

display_name: species-range-map

species-richness

display_name: species-richness

en: Species distribution maps of Fagales and Pinales (GDPlants)


maintainer: Achille Capelli

en: swe lwc snow gps snow-water-equivalent gnss liquid-water-content

en: Snow profiles data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: Snow profiles data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Daily observer data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Webcam images

en: Webcam images showing some typical differences in the snow conditions at the four sites (Küblis, Klosters, Laret and Weissfluhjoch) for two time periods (14-18.3.2019 and 27.1-11.2.2020). Both time periods included a precipitation event with partial rain-on–snow occurring at the lower locations.

en: GNSS derived data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: Snow and meteo data from automatic sensors for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Snow and meteo data from automatic sensors from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: GNSS derived SWE, LWC and HS from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-20

en: Daily data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: GNSS derived SWE, LWC and HS from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-20

en: Daily data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Profile data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: Daily observer data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Snow and meteo data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Daily observer data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: Manual profiles

en: Data of the traditional manual profiles for the locations Weissfluhjoch, Klosters and Küblis. For the location of Küblis there are no traditional manual profile data. We provided the data as both PDF and .CAAML format (See https://niviz.org/ or http://caaml.org/Schemas/V5.0/Profiles/SnowProfileIACS/CAAMLv5_SnowProfileIACS.pdf). Moreover, we added LWC and density profiles when available (all locations).

en: Daily data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: GNSS derived SWE, HS, and LWC for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Snow and meteo data from automatic sensors for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: Daily data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Snow profiles data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: This database contains GNSS derived snow water equivalent (SWE), liquid water content (LWC), and snow height (HS) and reference data collected during the two winter 2018-2020 at 4 sites Weissfluhjoch (2540 m asl, 46°49’47’’ N, 9°48’34’’E), Laret (1515 m asl, . 46°50’2’’N, 9°52’17’’E), Klosters (1200 m asl, 46°51’49’’N, 9°53’17’’E), and Küblis (815 m asl, 46°54’48’’N, 9°46’54’’E).

gnss

display_name: gnss

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

lwc

display_name: lwc

snow

display_name: snow

snow-water-equivalent

display_name: snow-water-equivalent

en: Snow water equivalent measurements with low-cost GNSS receivers along a steep elevation gradient in the East-ern Swiss Alps


en: timeseries precipitation minimum-temperature century maximum-temperature air-temperature climate-layers free-climate-data climate

en: CHELSA Technical Documentation

en: As the CHELSA project is continuously expanded, certain new parameters, time scales etc. are not included anymore in the original publication (Karger et al. 2017). We are therefore provide this document as a guideline of the current climatic parameters available and will expand it as new parameters or time periods will become available. Please refer to the version history for changes made in the document.

en: Data Access

en:
CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum. Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.

CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

air-temperature

display_name: air-temperature

century

display_name: century

climate

display_name: climate

climate-layers

display_name: climate-layers

free-climate-data

display_name: free-climate-data

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

timeseries

display_name: timeseries

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: CHELSAcruts -  High resolution temperature and precipitation timeseries for the 20th century and beyond


en: jupyter buffer roads gis geospatial density

en: Road Density Calculation (Python Notebook) online

en: Plots

en: Random plot locations

mimetype: application/zip

en: Road data from OSM for the surroundings of Menzberg, Switzerland

en:
This dataset contains the road and plot data used for the geospatial analysis example showcased in "Fostering Open Science at WSL with the EnviDat Environmental Data Portal", a contribution to the 5th Open Source Geospatial Research and Education Symposium (OGRS), 2018.
The example uses Jupyter Notebook to calculate road densities in the neighbourhood of sample plot locations with Python. Road data were extracted from OpenStreetMap, while the point data (sample plots) were generated manually.

buffer

display_name: buffer

density

display_name: density

geospatial

display_name: geospatial

gis

display_name: gis

jupyter

display_name: jupyter

roads

display_name: roads

en: Dataset for OGRS 2018 publication


causal-effect-of-mfoz

title_for_slug: causal-effect-of-mfoz

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

en: Data and Stata code for repeating the results

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

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

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

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

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

built-up-land-change

display_name: built-up-land-change

causality

display_name: causality

fujian-province

display_name: fujian-province

propensity-score-matching

display_name: propensity-score-matching

selection-bias

display_name: selection-bias

the-major-function-oriented-zone

display_name: the-major-function-oriented-zone

en: Causal effect of MFOZ


dischmex-meteorological-measurements

maintainer: Rebecca Mott

title_for_slug: dischmex-meteorological-measurements

en: dischma meteorological-measurements meteo-station meteorological-data dischmex

en: Meteorological measurements 2014

en: Location of the meteorological stations

en: Meteorological measurement 2014

en: data

en: Overview sensors

en: Meteorological measurement 2015

en: Meteorological measurements 2016

en:
Meteorological measurements recorded in the Dischma valley from 2014-2016. In 2014 and 2015 we used 11 mobile weather stations from sensorscope to record meteorological parameter in the upper Dischma valley in the closer surroundings of the Gletschboden area. The meteorological stations are eqiupped with at least air temperature/humidity, wind velocity and wind direction sensors. Some stations are additionally equipped with precipitation, shortwave radiation and snow surface temperature sensors.
Three transects were installed at different aspects and were equipped with air temperature/humidity and wind sensors. Transect 1 (stations 2-4) provides meteorological Information on an east-north-east facing slope at elevations ranging between 2100 m and 2500 m. Transect 2 (stations 5-7) provides meteorological Information on a south-west slope and transect 3 (stations 8-10) on a north-west slope. Station 1 is fully equipped with meteorological sensors (temperature/humidity, wind, IR, up and downwand shortwave radiation and precipitation).
In 2016, mobile stations from sensorscope were replaced with six permanent meteorological stations. Meteorological stations 1-3 are equipped with an air temperature/humidity sensor, two wind speed sensors, a wind direction sensor and an incoming and outgoing shortwave radiation sensor. Stations 4 and 6 are equipped with an air temperature/humidity sensor and a wind speed/direction sensor. Station 5 is a equipped with an air temperature/humidity sensor, a wind speed/direction sensor, a snow surface temperature sensor, an incoming and outgoing shortwave radiation sensor and an incoming longwave radiation sensor.

dischma

display_name: dischma

dischmex

display_name: dischmex

meteo-station

display_name: meteo-station

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

en: DISCHMEX - Meteorological measurements


en: terrestrial-laser-scans gridded-data terrestiral-laserscanning depth-of-snowfall antarctica

en: Scan from 4 January 2019, 10 cm resolution

en: Scan from 27 December 2018, 1 mm resolution

en: Scan from 4 January 2019, 1 mm resolution

en: Scan from 21 December 2018, 1 mm resolution

en: Scan from 2 January 2019, 10 cm resolution

en: Scan from 11 January 2019, 1 mm resolution

en: Scan from 21 December 2018, 10 cm resolution

en: Scan from 27 December 2018, 10 cm resolution

en: Deprecated: Scan from 11 January 2019, 10 cm resolution, version 1

en: This resource is deprecated. It contains erroneous data.

en: Scan from 11 January 2019, 10 cm resolution, version 2

en: Scan from 2 January 2019, 1 mm resolution

en:
Surface topography maps (spatial extent: 400 m x 400 m) obtained at approximately 300 m from the top of the Hammarryggen Ice Rise in Dronning Maud Land, East Antarctica, using a Riegl VZ-6000 Terrestrial Laser Scanner (TLS). Scans were obtained on 5 days in the 2018-2019 Austral summer: on December 21 and 27, and January 2, 4 and 11. By using reflectors installed on bamboo poles, scans were registered with respect to the reflectors, such that the difference between two successive scans reveals the spatial patterns of erosion and deposition of snow. On each scan day, we used multiple scan positions to create one combined point cloud. After applying an octree filter on the point cloud, a 3D surface was obtained. For each day, the dataset contains a 1 mm and a 10 cm octree filter resolution file, only including points in a 400 m x 400 m area centered around the scan positions.

Notes:

* All files in the dataset are in the same coordinate system. However, this coordinate system is arbitrary (i.e., not related to any global coordinate system).

* From the installed reflectors, 4 reflectors could be used over the full period. The scan accuracy is generally higher within the area enclosed by the reflectors.

* The scans from January 2 were found to have exhibited small tilt during the scan and are of lesser accuracy.

* By walking along fixed corridors, disturbance of the snow was limited.

antarctica

display_name: antarctica

depth-of-snowfall

display_name: depth-of-snowfall

gridded-data

display_name: gridded-data

terrestiral-laserscanning

display_name: terrestiral-laserscanning

terrestrial-laser-scans

display_name: terrestrial-laser-scans

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Terrestrial laser scans on Hammarryggen Ice Rise, Dronning Maud Land, East Antarctica


en: lichens bryophytes flechten garten diversitat moose bryolich

en: Im Rahmen des Bryolich-Projekts “Moose und Flechten in Gärten” wurden möglichst vollständige Artenlisten von Moosen und Flechten in 26 bzw. 7 Gärten um Wohnhäuser erstellt. In 5 dieser Gärten wurden beide Artengruppen erfasst. Zusätzlich wurden Informationen über unterschiedliche Lebensräume innerhalb der Gärten (z.B. Fläche mit Gehölzen, Fläche von Blumenbeeten) und weitere Garten-Charakteristika (z.B. Fläche des Gartens, Alter des Gartens, Einsatz von Dünger) erfasst. Details zum Projekt und den erfassten Daten können im zugehörigen Artikel (Bergamini et al. 2024) und auf der Webseite von Bryolich gefunden werden (https://www.bryolich.ch/mfig/mfig_de.html).

bryolich

display_name: bryolich

bryophytes

display_name: bryophytes

diversitat

display_name: diversitat

flechten

display_name: flechten

garten

display_name: garten

lichens

display_name: lichens

moose

display_name: moose

en: Daten Bryolich-Projekt Moose und Flechten in Gärten


mountain-permafrost-hydrology

maintainer: Jeannette Noetzli

title_for_slug: mountain-permafrost-hydrology

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

en: DORA Persistent URL

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

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

climate-change-impacts

display_name: climate-change-impacts

ice-rich-permafrost

display_name: ice-rich-permafrost

mass-movements

display_name: mass-movements

monitoring-techniques

display_name: monitoring-techniques

mountain-permafrost-hydrology

display_name: mountain-permafrost-hydrology

permafrost-degradation

display_name: permafrost-degradation

en: Mountain Permafrost Hydrology


en: cosmo-wrf precipitation-variability orographic-precipitation complex-terrain snow-precipitation

en: StationData

en: Snow depth maps over Dischma valley for 26 January 2016 and 9 March 2016 and snow depth differences between the two dates.

en:
The data presented here corresponds to the publication "Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain" (Gerber et al., 2018a), which investigates the precipitation variability of snow precipitation in the central northern part of the Grisons (CH) and the publication "The importance of near-surface winter precipitation processes in complex alpine terrain" (Gerber et al., 2018b). The dataset contains:

* WRFsimulations: WRF simulation output for simulations with 4x (14x) terrain smoothing with an output timestep of 30 min/5 min and horizontal grid spacings of 1350 m, 450 m, 150 m and 50 m (currently: data available upon request).
* StationData: Meteorological station data of 18 meteorological stations in the central northern part of the Grisons with 30 minute resolution for the period 1 January 2016 till 1 May 2016.
* ADS80data: Photogrammetrically determined snow depth distribution data over the Dischma valley for the 26 January 2016 and 9 March 2016. Snow heights are corrected for buildings, vegetation (> 1m), outliers, and pixles, which are obivously snow-free on the pictures (Bühler et al., 2015). In addition the snow depth differences (snow depth on 9 March 2016 minus snow depth on 26 January 2016) are provided.

For more details about the simulation and observation data, see Gerber et al., 2018 and Gerber and Sharma (2018).

Publications:

Bühler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and Ginzler, C.: Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9, 229–243, doi:10.5194/tc-9-229-2015, 2015.

Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137–3160, doi:10.5194/tc-12-3137-2018, 2018.

Gerber, F., Mott, R. and Lehning, M.: The importance of near-surface winter precipitation processes in complex alpine terrain, Journal of Hydrometeorology, accepted, 2018.

Gerber, F., and Sharma, V.: Running COSMO-WRF on very-high resolution over complex terrain. Laboratory of Cryospheric Sciences CRYOS, École Polytechnique Fédérale de Lausanne EPFL, Lausanne, Switzerland. doi:10.16904/envidat.35, 2018.

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

orographic-precipitation

display_name: orographic-precipitation

precipitation-variability

display_name: precipitation-variability

snow-precipitation

display_name: snow-precipitation

en: DISCHMEX - High-resolution WRF simulations in complex alpine terrain and station measurements


en: conservation soil-organisms ecosystem-protection eco-fauna database lichens info-fauna soil-quality fungi fauna soil-dependency red-list soil-biodiversity

en: Overview over the soil dependency and endangerment status of different groups of organisms.

en: Raw dataset of species and their soil dependency as well as endangerment status.

en: Proportion of endangered species grouped by the four soil dependency classes.

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

Paper Citation:
Frey B., Maurer C., Schneider K. (2023) Informationen zum Boden anhand der Gebundenheit der Rote-Liste Arten BAFU. Bundesamt für Umwelt BAFU. 26 p.

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

Soil is the habitat and basis of life for countless creatures. The great importance of soil organisms for the fertility of our soils is widely recognized, but information is sparse on their distribution and possible endangerment in Switzerland. In the present project, target species, i.e. species that are closely linked to the soil, were identified, and for the the first time, information was gathered on the degree of endangerment of the soil ecosystem in Switzerland on the basis of Red List species. For eleven Red List groups, the species' soil dependency was determined, i.e. whether their life cycle includes stages in or on the soil. The assessment of soil dependency was made using four classes, which enabled the classification not only of insects but also of vertebrates, land snails, lichens and fungi.

conservation

display_name: conservation

database

display_name: database

eco-fauna

display_name: eco-fauna

ecosystem-protection

display_name: ecosystem-protection

fauna

display_name: fauna

fungi

display_name: fungi

info-fauna

display_name: info-fauna

red-list

display_name: red-list

soil-biodiversity

display_name: soil-biodiversity

soil-dependency

display_name: soil-dependency

soil-organisms

display_name: soil-organisms

soil-quality

display_name: soil-quality

educ

en: Education, culture and sport

en: Information on soil based on the soil dependency of the FOEN red list species


maintainer: Nander Wever

en: air-temperature snow-depth longwave-radiation wind-speed precipitation-amount shortwave-radiation snow-ice-temperature wind-direction humidity

en: Version 6

en:
Deprecated version.

Release notes:

- Missing ISWR for 2009-09-30 and 2010-04-30 has been replaced by data from WFJ Gipfel

This dataset is the basis for the Weissfluhjoch dataset in ESM-SnowMIP (doi: http://dx.doi.org/10.16904/16).

en: Version 5

en:
Deprecated version. Release notes:

Update: using the incoming and outgoing short- and longwave radiation data from the new processing chain from 2010 onwards, filling also data gaps in pre-2010 data.

Update: dataset extended to 2016-10-04T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 2

en:
Deprecated version. Release notes:

Bug fix: in v1, incoming radiation components were taken from WFJ_Gipfel station, instead of WFJ Versuchsfeld.

en: SNOWPACK ini file (new syntax)

en: This ini-file can be used to run SNOWPACK simulations with the WFJ data set, using the new MeteoIO Filter, Generator and Interpolation syntax.

en: SNOWPACK sno file

en: This sno-file can be used to run SNOWPACK simulations with the WFJ data set, using version 3.4.0 or higher.

en: Version 7 (current)

en:
Release notes:

Bug fix: in previous versions, snow lysimeter data from September 2010 onwards was erroneously reported for UTC+2, instead of UTC+1 (i.e., reported one hour later than should have been).

Update: dataset extended to 2017-07-11T00:30.

en: Undercatch correction

en: This document describes how the undercatch correction for the Weissfluhjoch measurement site was determined.

en: Version 4

en:
Deprecated version. Release notes:

Update: dataset extended to 2016-09-02T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 3

en:
Deprecated version. Release notes:

Bug fix: in version 2, the undercatch correction was not applied correctly

Bug fix: in version 2, snow lysimeter data was missing for extended periods of time

Update: dataset extended to 2016-01-02T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: All versions

en: Version 1

en:
Deprecated version. Release notes:

First release of dataset.

en: SNOWPACK ini file (old syntax)

en: This ini-file can be used to run SNOWPACK simulations with the WFJ data set, using version 3.4.0 or higher.

en:
Dataset of meteorological and snowpack measurements from the automatic weather station at Weissfluhjoch, Davos, Switzerland, suitable for driving snowpack models. The dataset contains standard meteorological measurements, and additionally snowpack runoff data from a snow lysimeter. Where possible, data is quality checked and missing data are replaced from backup sensors from the measurement site itself, or (in only a few cases) from the MeteoSwiss weather station at 470 m distance and 150 m above the measurement site.

Publication
Wever, N., Schmid, L., Heilig, A., Eisen, O., Fierz, C., and Lehning, M. Verification of the multi-layer SNOWPACK model with different water transport schemes. 2015. The Cryosphere. Volume 9. 2271-2293. http://dx.doi.org/10.5194/tc-9-2271-2015.

air-temperature

display_name: air-temperature

humidity

display_name: humidity

longwave-radiation

display_name: longwave-radiation

precipitation-amount

display_name: precipitation-amount

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

snow-ice-temperature

display_name: snow-ice-temperature

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

envidat

en: EnviDat: the Environmental Data Portal

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

en: WFJ_MOD: Meteorological and snowpack measurements from Weissfluhjoch, Davos, Switzerland


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

en: README

media_type: text/plain

en: README file gives an overview of the organization of the data

format: TXT

en: slow snow compression experiments

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

en:
We conducted consecutive loading-relaxation experiments at low strain rates to study the viscoplastic behavior of the intact ice matrix in snow. The experiments were conducted using a micro-compression stage within the X-ray tomography scanner in the SLF cold laboratory.

Next, to evaluate the experiments, a novel, implicit solution of a transient scalar model was developed to estimate the stress exponent and time scales in the effective creep relation (Glen's law). The result reveals that, for the first time, a transition in the exponent in Glen's law depends on geometrical grain size. A cross-over of stress exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is interpreted as a transition from grain boundary sliding to dislocation creep.

The dataset includes compression force data from 11 experiments and corresponding 3D image data from tomography scans.

ice

display_name: ice

microstructure

display_name: microstructure

slow-snow-compression-experiments

display_name: slow-snow-compression-experiments

snow-mechanics

display_name: snow-mechanics

x-ray-tomography

display_name: x-ray-tomography

educ

en: Education, culture and sport

en: A grain-size driven transition in the deformation mechanism in slow snow compression


en: shortwave-radiation radiation longwave-radiation weisfluhjoch davos

en: Processing doc

en: Description of processing code that was ported to R by P. Frei (2016).

en: Metadata

en: DATA ACCESS

en: ASRB radiation station at the Weissfluhjoch versuchsfeld.

en: Parameter file

en: Description of ASRB parameters.

mimetype: text/plain

en:
Incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch research site, Davos, CH. The experimental site at the Weissfluhjoch (WFJ, 46.83 N, 9.81 E) is located at an altitude of 2540 m in the Swiss Alps near Davos. During the winter months, almost all precipitation falls as snow at this altitude. As a consequence, a continuous seasonal snow cover builds up every winter, with a maximum snow height ranging from 153–366 cm over the period 1934–2012. The measurement site is located in an almost flat part of a southeast oriented slope.

 References
1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.
2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.

davos

display_name: davos

radiation

display_name: radiation

weisfluhjoch

display_name: weisfluhjoch

en: ASRB_WFJVF: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos


en: permos ritigraben permafrost borehole ground-temperature

en: Map of PERMOS borehole network

en: PERMOS DATA POLICY

en:
_ENVIDAT NOTE: Data currently unvailable and measures are being taken to recover and restore the data files._

Processed ground temperature measurements at the Ritigraben permafrost borehole (RIT_0102) in canton Valais, Switzerland. The borehole is located at 2690 m asl on a flat site. The surface material is coarse blocks and borehole depth is 30 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.

borehole

display_name: borehole

ground-temperature

display_name: ground-temperature

permafrost

display_name: permafrost

permos

display_name: permos

ritigraben

display_name: ritigraben

envidat

en: EnviDat: the Environmental Data Portal

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

en: RIT1: Processed permafrost borehole data (2690  m asl), Ritigraben, Switzerland


en: splash large-eddy-simulation saltation-parameterizations aerodynamic-entrainment snow-saltation drifting-snow

en: Code and input files to reproduce the simulations in Melo et al. (2024), as well as the post-processing scripts used in the analysis.

en:
This dataset includes the necessary files to reproduce the results presented in Melo et al. (2024):
- code version used in the simulations (also available at the institutional [GitLab repository](https://gitlabext.wsl.ch/atmospheric-models/les-lsm) - tag 'r2024')
- input files to run the simulations
- post-processing scripts (MATLAB and FORTRAN)
- selected raw output files
- post-processed results (MATLAB data files).
 	**Note:** Direct model outputs are not provided because of their size, but they can be shared upon request. They can be reproduced by running the code. The post-processed results allow you to reproduce all figures in Melo et al. (2024).

aerodynamic-entrainment

display_name: aerodynamic-entrainment

drifting-snow

display_name: drifting-snow

large-eddy-simulation

display_name: large-eddy-simulation

saltation-parameterizations

display_name: saltation-parameterizations

snow-saltation

display_name: snow-saltation

splash

display_name: splash

educ

en: Education, culture and sport

en: Understanding snow saltation parameterizations - Reproducibility


weather-station-klosters

maintainer: Maxime Hervo

title_for_slug: weather-station-klosters

en: temperature raclets klosters weather wind precipitation

en: Weather Station Klosters

en: All in one weather station measuring temperature (°C), humidity (%), wind (m s‾ ¹) and precipitation type. The time format is: number of days from January 0, 0000, UTC.

en: A weather station (Lufft WS600) measured meteorological parameters at Klosters (LON: 9.880413, LAT: 46.869019). Detailed information on the specifications can be found [here](https://www.lufft.com/products/compact-weather-sensors-293/ws600-umb-smart-weather-sensor-1832/productAction/outputAsPdf/).

klosters

display_name: klosters

precipitation

display_name: precipitation

raclets

display_name: raclets

temperature

display_name: temperature

weather

display_name: weather

wind

display_name: wind


maintainer: Yohann Chauvier

en: species-observations gbif flags occurrence-records taxonomy landsca accepted-name range-maps filtering large-scale ecoregions hard-limit r-package synonyms species-distribution conservation

en: GitHub URL

en: gbif.range R package version 1.1 (current)

en: wsl.gbif R package version 0.1 (deprecated)

en: Test version, not to be used.

en: gbif.range R package version 0.2 (deprecated)

en: Although species range may be obtained using expert maps or modeling methods, expert data is often species-limited and statistical models need more technical expertise as well as many species observations. When unavailable, such information may be extracted from the Global Biodiversity Information facility (GBIF), the largest public data repository inventorying georeferenced species observations worldwide. However, retrieving GBIF records at large scale may be tedious if users are unaware of specific tools and functions that need to be employed. Here we present *gbif.range*, an R library that contains automated methods to generate species range maps from scratch using in-house ecoregions shapefiles and an easy-to-use GBIF download wrapper. Finally, this library also offers a set of additional very useful parameters and functions for large GBIF datasets (generate doi, extract GBIF taxonomy, records filtering...). [gbif.range R project](https://github.com/8Ginette8/gbif.range)

accepted-name

display_name: accepted-name

conservation

display_name: conservation

ecoregions

display_name: ecoregions

filtering

display_name: filtering

flags

display_name: flags

gbif

display_name: gbif

hard-limit

display_name: hard-limit

landsca

display_name: landsca

large-scale

display_name: large-scale

occurrence-records

display_name: occurrence-records

r-package

display_name: r-package

range-maps

display_name: range-maps

species-distribution

display_name: species-distribution

species-observations

display_name: species-observations

synonyms

display_name: synonyms

taxonomy

display_name: taxonomy

en: gbif.range - An R package to generate species range maps based on ecoregions and a user-friendly GBIF wrapper


distributed-acoustic-sensing-brienz

title_for_slug: distributed-acoustic-sensing-brienz

en: rockfall early-warning seismic machine-learning natural-hazard landslide

en: Data access

en:
This dataset contains the Distributed Acoustic Sensing (DAS), radar detection data used for training and result analysis in the GRL paper titled `Automatic Monitoring of Rock-Slope Failures Using Distributed Acoustic Sensing and Semi-Supervised Learning`.
The DAS dataset (both waveform and cross-spectral density matrices), extracted features, labeled dataset, two trained models (feature extraction model and xgboost classification model), scripts to reproduce the whole training and classification processes, and a notebook to replicate the result analysis part are provided under the MIT license. To provide a reasonable data size, we chunked the raw data to a few hundred channels which we used in our project.
Abstract:
Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi-supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million cubic meters on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground-truth labeling. The proposed algorithm is capable of distinguishing between rock-slope failures and background noise, including road and train traffic, with a detection precision of over 95%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal-to-noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

early-warning

display_name: early-warning

landslide

display_name: landslide

machine-learning

display_name: machine-learning

natural-hazard

display_name: natural-hazard

rockfall

display_name: rockfall

seismic

display_name: seismic

envidat

en: EnviDat: the Environmental Data Portal

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

en: Distributed Acoustic Sensing Brienz


en: metamorphism snow 18o advection stable-water-isotope

en: Read-me

en: A short description of the data contained in the zipped-file. Recommended for reading before downloading the dataset.

en: Data files and read-me

en: raw data and micro-CT data available from contacts

en:
Stable water isotopes (δ18O) obtained from snow and ice samples of polar regions are used to reconstruct past climate variability, but heat and mass transport processes can affect the isotopic composition. Here we present an experimental study on the effect on the snow isotopic composition by airflow through a snow pack in controlled laboratory conditions. The influence of isothermal and controlled temperature gradient conditions on the δ18O content in the snow and interstitial water vapor is elucidated. The observed disequilibrium between snow and vapor isotopes led to exchange of isotopes between snow and vapor under non-equilibrium processes, significantly changing the δ18O content of the snow. The type of metamorphism of the snow had a significant influence on this process.
Ebner, P. P., Steen-Larsen, H. C., Stenni, B., Schneebeli, M., and Steinfeld, A.: Experimental observation of transient δ18O interaction between snow and advective airflow under various temperature gradient conditions, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-16, accepted, 2017.

advection

display_name: advection

metamorphism

display_name: metamorphism

snow

display_name: snow

stable-water-isotope

display_name: stable-water-isotope

educ

en: Education, culture and sport

en: Experiments on stable water isotopes,  snow metamorphism, and advection


en: terrain solar-irradiance light-availability forests radiation-transfer sky-view-fraction shortwave-radiation solar-energy lidar transmissivity

en: DataLink

en:
The SwissRad10 dataset contains light availability maps at 10 m and hourly resolution across an entire annual cycle with wall-to-wall coverage across the whole of Switzerland.
Areas below and within 25 m of forests include light availability calculated for both leaf-on and leaf-off canopy structure, and terrain only data is available across the full domain.
The variables sky-view fraction and direct-beam transmissivity are both included, allowing estimation of both direct and diffuse light at the ground surface.
The dataset was calculated using the detailed synthetic hemispheric image-based ray-tracing Canopy Radiation Model (CanRad) with the latest airborne lidar and terrain surface data from the swissSURFACE3D dataset.

forests

display_name: forests

lidar

display_name: lidar

light-availability

display_name: light-availability

radiation-transfer

display_name: radiation-transfer

shortwave-radiation

display_name: shortwave-radiation

sky-view-fraction

display_name: sky-view-fraction

solar-energy

display_name: solar-energy

solar-irradiance

display_name: solar-irradiance

terrain

display_name: terrain

transmissivity

display_name: transmissivity

en: SwissRad10 Hourly light availability maps at 10 m resolution over Switzerland


maintainer: Anita C. Risch

en: mineralization worldwide soil-chemistry soil grassland global nutrient-network soil-biology

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

bioclim.T.ann: Mean annual temperature (°C)

bioclim.T.var: Temperature variability (°C)

bioclim.T.q.wet: Temperature of the wettest quarter (°C)

bioclim.T.q.dry: Temperature of the driest quarter (°C)

bioclim.T.q.warm: Temperature of the warmest quarter (°C)

bioclim.P.ann: Mean annual preciptiation (mm)

bioclim.P.q.wet: Precipitation of the wettest quarter (mm)

ch.pH: pH

ch.WSK.vol: Water holding capacity (vol%)

ch.bulkdensity.gcm3: Bulk density (g cm-3)

ch.porespace: Pore space (vol%)

ch.sand.perc: Soil texture: sand (%)

ch.silt.perc: Soil texture: silt (%)

ch.clay.perc: Soil texture: clay (%)

ch.NO3start.mgg: NO3 at the beginning of the incubation (mgkg)

ch.Ntot.perc: Total soil N (%)

ch.Ctot.perc: Total soil C (%)

ch.Corg.perc: Soil organic C (%)

ch.netNminfield.mgkgd: Realised soil net N mineralisation (mg kg-1 d-1)

ch.netNminlab.mgkgd: Potential soil ne N mineralisation (mg kg-1 d-1)

archaea.cpg.log: Ammonia oxidising archaea (archaeal amoA gene copies g-1 soil)

bacteria.cpg.log: Ammonia oxidising bacteria (bacterial amoA gene copies g-1 soil)

micr.biom: Microbial biomass (ug g-1 soil)

ch.CtoN: C to N ratio

ch.netNnitriffield.mgkgd.sqrt: Realised soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniffield.mgkgd.sqrt: Realised soil net ammonification, transformed (mg kg-1 d-1)

ch.netNnitriflab.mgkgd.sqrt: Potential soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniflab.mgkgd.sqrt: Potential soil net ammonifiation, transformed (mg kg-1 d-1)

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

Paper Citation:

Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2

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

We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil

display_name: soil

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide


en: soil-respiration microbial-respiration microbial-biodiversity ecosystem-functions beta-diversity

en: Bacterial amplicon sequencing

en: Bacterial ASVs and taxonomic tables

en: Fungal amplicon sequencing

en: Fungal ASVs and taxonomic tables

en: Microbial functions and metadata

en: Extracellular enzyme activities, respiration and sites metadata

en: Structural and functional changes of microbial communities in ephemeral stream sediments and adjacent soils between was assessed in an artificial newly created catchment at 3 and 13 years after catchment construction. Bacterial and fungal communities, respiration and extracellular enzyme activities were assessed.

beta-diversity

display_name: beta-diversity

ecosystem-functions

display_name: ecosystem-functions

microbial-biodiversity

display_name: microbial-biodiversity

microbial-respiration

display_name: microbial-respiration

soil-respiration

display_name: soil-respiration

envidat

en: EnviDat: the Environmental Data Portal

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

en: Chicken Creek 10 years


en: snow erosion sediment-transport mars model

en: Datasets of Figure 2

en: Datasets of Figure 4b

en: Datasets of Figure 3a

en: Datasets of Figure 3b

en: Datasets of Figure 4a

en:
The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)).

 Manuscript Abstract:
Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.

erosion

display_name: erosion

mars

display_name: mars

model

display_name: model

sediment-transport

display_name: sediment-transport

snow

display_name: snow

educ

en: Education, culture and sport

en: Energy- and momentum-conserving model of splash entrainment in sand and snow saltation


en: source-code snow-modelling avalanche snowpack snowdrift

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

avalanche

display_name: avalanche

snow-modelling

display_name: snow-modelling

snowdrift

display_name: snowdrift

snowpack

display_name: snowpack

source-code

display_name: source-code

en: FSM2trans code