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

en: Observed and simulated snow profile data

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

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

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

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

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

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

failure-layer

display_name: failure-layer

rutschblock

display_name: rutschblock

snow-instability

display_name: snow-instability

snow-profiles

display_name: snow-profiles

snow-stratigraphy

display_name: snow-stratigraphy

snowpack

display_name: snowpack

snowpack-simulation

display_name: snowpack-simulation


$oid: OBSOLETE

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

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

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

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

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

format: XLS

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

format: CSV

media_type: text/csv

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

biodiversity

display_name: biodiversity

biogeography

display_name: biogeography

forest-management

display_name: forest-management

pairwise-stand-comparisons

display_name: pairwise-stand-comparisons

soil-impacts

display_name: soil-impacts


intratrait

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

en: intratrait

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

title_for_slug: intratrait

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

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

climate-change

display_name: climate-change

cold-adapted-plants

display_name: cold-adapted-plants

elevation-gradient

display_name: elevation-gradient

elevation-range

display_name: elevation-range

neighbour-interactions

display_name: neighbour-interactions

plant-traits

display_name: plant-traits


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

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

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

Documentation about the procedure to run COSMO-WRF

format: PDF

media_type: application/pdf

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

format: ZIP

media_type: application/zip

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

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

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

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

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

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

high-resolution

display_name: high-resolution

meteoswiss

display_name: meteoswiss

numerical-simulation

display_name: numerical-simulation


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

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

en: aws cryowrf python consistent antarctica

envidat

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

en: EnviDat: the Environmental Data Portal

envi

en: Environment

antarctica

display_name: antarctica

aws

display_name: aws

consistent

display_name: consistent

cryowrf

display_name: cryowrf

python

display_name: python


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

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

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

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

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

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

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

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

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

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

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

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

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

biodiversity

display_name: biodiversity

biomass

display_name: biomass

carbon

display_name: carbon

national-forest-inventory

display_name: national-forest-inventory

protection-forest

display_name: protection-forest

volume

display_name: volume


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

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

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

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

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

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

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

format: TXT

media_type: text/plain

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

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

microstructure

display_name: microstructure

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen


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

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

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

envidat

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

en: EnviDat: the Environmental Data Portal

envi

en: Environment

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

en:
Resource Title: SPSS-Dataset
    Size: 9307772 bytes
    Format: application/x-spss-sav
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/795a1d55-ec18-4153-9c93-e93109a67bd5/download/umfrage_mitlfi2_2.sav
    Description:

en:
Resource Title: Questionnaire Italian
    Size: 10955256 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/f4fe792f-05dc-4896-8b1e-541611d9f21c/download/umfrageerwachseneitalienisch.pdf
    Description:

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

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

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

adults-and-teenagers

display_name: adults-and-teenagers

nfi-photos

display_name: nfi-photos

online-questionnaire-survey

display_name: online-questionnaire-survey

perception-of-ecosystem-services

display_name: perception-of-ecosystem-services

visual-attractiveness

display_name: visual-attractiveness


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

en: Saltation of cohesive granular materials

en: wind sand snow cohesion sediment saltation

cohesion

display_name: cohesion

saltation

display_name: saltation

sand

display_name: sand

sediment

display_name: sediment

snow

display_name: snow

wind

display_name: wind


disdrometer-data-laret

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

en: Disdrometer Data Laret

en: visibility hydrometeors raclets disdrometer precipitation

title_for_slug: disdrometer-data-laret

disdrometer

display_name: disdrometer

hydrometeors

display_name: hydrometeors

precipitation

display_name: precipitation

raclets

display_name: raclets

visibility

display_name: visibility


disdrometer-data-gotschnagrat

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

en: Disdrometer Data Gotschnagrat

title_for_slug: disdrometer-data-gotschnagrat


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

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

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

envidat

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

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

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

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

format: markdown

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

alpine-regions

display_name: alpine-regions

climate-change-scenarios

display_name: climate-change-scenarios

delta-change

display_name: delta-change

imis

display_name: imis

meteoswiss

display_name: meteoswiss

python

display_name: python

quantile-mapping

display_name: quantile-mapping

r-script

display_name: r-script

switzerland

display_name: switzerland

temporal-downscaling

display_name: temporal-downscaling


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

en: Soil sealing Barcelona and Milan different territorial levels

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

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

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

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

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

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

barcelona

display_name: barcelona

copernicus

display_name: copernicus

imperviousness

display_name: imperviousness

milan

display_name: milan

soil-sealing

display_name: soil-sealing

urban-atlas

display_name: urban-atlas


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

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

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

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

en:
Resource Title:  AnIceFlux_v01_clim_mean_1982-2008.nc
    Size: 260945 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/b16a0901-3abe-4da0-b61e-cdb80c59ba63/resource/6a315e6d-4c92-4f85-b131-92fd87853b65/download/aniceflux_v01_clim_mean_1982-2008.nc
    Description: Data in NetCDF4 CF-1.6 format

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

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

antartic

display_name: antartic

climate-change

display_name: climate-change

cryospheric-science

display_name: cryospheric-science

forcing

display_name: forcing

freezing

display_name: freezing

freshwater-flux

display_name: freshwater-flux

mass-balance

display_name: mass-balance

melting

display_name: melting

physical-oceanography

display_name: physical-oceanography

polar

display_name: polar

salinity

display_name: salinity

salt

display_name: salt

satellite

display_name: satellite

sea-ice

display_name: sea-ice

sea-ice-divergence

display_name: sea-ice-divergence

sea-ice-volume

display_name: sea-ice-volume

southern-ocean

display_name: southern-ocean

transport

display_name: transport


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

en: Impulse response functions for nonlinear nonstationary and heterogeneous systems

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

envidat

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

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

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

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

arma-noise

display_name: arma-noise

de-mixing

display_name: de-mixing

deconvolution

display_name: deconvolution

greens-function

display_name: greens-function

impulse-response-function

display_name: impulse-response-function

irf

display_name: irf

irfnnhsr

display_name: irfnnhsr

nonlinear-deconvolution

display_name: nonlinear-deconvolution

system-identification

display_name: system-identification

time-series-analysis

display_name: time-series-analysis

transfer-function

display_name: transfer-function


modeling-snow-failure-with-dem

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

en: Modeling snow failure with DEM

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

envidat

en: EnviDat: the Environmental Data Portal

title_for_slug: modeling-snow-failure-with-dem

envi

en: Environment

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

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

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

discrete-element-modelling

display_name: discrete-element-modelling

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-micro-mechanics

display_name: snow-micro-mechanics


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

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

en: stable-water-isotopes groundwater streamflow alptal runoff

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

alptal

display_name: alptal

groundwater

display_name: groundwater

runoff

display_name: runoff

stable-water-isotopes

display_name: stable-water-isotopes

streamflow

display_name: streamflow


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

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

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

envidat

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

en: EnviDat: the Environmental Data Portal

envi

en: Environment

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

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

fagales

display_name: fagales

pinales

display_name: pinales

species-distribution-model

display_name: species-distribution-model

species-occurrence

display_name: species-occurrence

species-range-map

display_name: species-range-map

species-richness

display_name: species-richness


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

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

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

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

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

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

avalanche-activity

display_name: avalanche-activity

avalanche-forcasting

display_name: avalanche-forcasting

avalanche-size

display_name: avalanche-size

danger-level

display_name: danger-level


en:
Table of Content: 1. General context of the data set "lsUDPs" ; 2. Background and aims of the study using the data set lsUDPs; 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalisation
1. General context of the data set "lsUDPs"
The data set "lsUDPs" has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2020. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, strategic spatial plans) into quantitative land-change modelling approaches at the urban regional level. The first stage (2016-2017) of the CONCUR project focussed on 21 urban regions in Western Europe. The urban regions were selected through a multi-stage strategy for empirical research (see Hersperger, A. M., Grădinaru, S., Oliveira, E., Pagliarin, S., & Palka, G. (2019). Understanding strategic spatial planning to effectively guide development of urban regions. Cities, 94, 96–105. https://doi.org/10.1016/j.cities.2019.05.032 ).
2. Background and aims of the study using the data set lsUDPs
As part of the CONCUR project, a specific task was to examine the relationship between strategic spatial plans and the formulation and implementation (i.e. urban land change) of large-scale urban development projects in Western Europe.
Strategic urban projects are typically large-scale, prominent urban transformations implemented locally with the aim to stimulate urban growth, for instance in the form of urban renewals of deprived neighborhoods, waterfront renewals and transport infrastructures. While strategic urban projects are referred to in the literature with multiple terms, in the CONCOR project we call them large-scale urban development projects (lsUDPs).
Previous studies acknowledged both local and supra-local (or structural) factors impacting the context-specific implementation of lsUDPs. Local governance factors, such as institutional capacity, coordination among public and private actors and political leadership, intertwine with supra-local conditions, such as state re-scaling processes and devolution of state competencies in spatial planning, de-industrialisation and increasing social inequality. Hence, in implementing lsUDPs, multi-scalar factors act in combination.
Because the formulation and implementation of lsUDPs require multi-scalar coordination among coalitions of public and private actors over an extended period of time, they are generally linked to strategic spatial plans (SSPs). Strategic spatial plans convey collective visions and horizons of action negotiated among public and private actors at the local and/or regional level to steer future urban development, and can contain legally binding dispositions, but also indicative guidelines.
The key question remains as to what extent large-scale urban development projects and strategic spatial plans can be regarded as aligned. By alignment, or “concordance”, we mean that strategic projects are formulated and implemented as part of the strategic planning process (“high concordance”), or that the strategic role of projects is reconfirmed in (subsequent) strategic plans (“moderate concordance”). Lack of concordance is found when lsUDPs have been limitedly (or not at all) acknowledged in strategic spatial plans.
We assume that certain local and supra-local factors, characterising the development of the projects, foster (but not strictly “cause”) the degree of alignment between lsUPDs and SSPs. In this study, we empirically examine how, and to what extent, the concordance between 38 European large-scale urban development projects and strategic plans (outcome: CONCOR) has been enabled by five multi-scalar factors (or conditions): (i) the role of the national state (STATE), (ii) the role of (inter)national private actors (PRIVATE), (iii) the occurrence of supra-regional external events (EVENTS), (iv) the degree of transport connectivity (TRANSP), and (v) local resistance from civil society (RESIST). We adopted a (multi-data) case-based qualitative strategy for empirical research and applied the formalised procedure of within- and cross-case comparison offered by fuzzy-set Qualitative Comparative Analysis appropriate for the goal of this study. Based on set theory, QCA formally integrates contextual sensitivity to case specificities (within-case knowledge) with systematic comparative analysis (across-case knowledge).
The research question the data set has been created to reply to is the following: which conditions, and combinations of conditions, enable the concordance between large-scale urban development projects and strategic spatial plans?
The conditions (“independent variables”) considered are. STATE: the set of large-scale urban projects characterized by a high degree of state intervention and support in their formulation and implementation, PRIVATE: the set of large-scale urban projects characterized by a high degree of involvement of (inter)national private actors in their formulation and implementation, EVENTS: the set of large-scale strategic projects whose formulation and implementation have been strongly affected by unforeseen international events and/or global trends, TRANSP: the set of large-scale strategic projects with a high degree of road and/or transit connectivity, and RESIST: set of large-scale strategic projects whose realization has been characterized by resistances that have substantially delayed or modified the project implementation.
The outcome (“dependent variable”) under analysis is CONCOR: the set of large-scale urban projects having a high degree of concordance/alignment/integration with strategic spatial plans
3. The data set lsUDPs
      3.1 Selection of cases and data collection
To generate the current data set on large-scale urban development projects in European urban regions (data set "lsUDPs"), we identified 35 large-scale urban development projects in a sample of the 21 Western urban regions considered in the CONCUR project (see supra, Hersperger et al. 2019): Amsterdam, Barcelona, Copenhagen, Hamburg, Lyon, Manchester, Milan, Stockholm, Stuttgart. The criteria we followed to identify the 35 large-scale urban development projects are: geographical location, size (large-scale), site (located either in the city core or in the larger urban region) and urban function (e.g. housing, transportation infrastructures, service and knowledge economic functions). Employing these criteria facilitated the selection of diverse large-scale urban development projects while still ensuring sufficient comparability.
In 2016, we performed 47 in-depth interviews with experts in urban and regional planning and large-scale strategic projects and infrastructure (i.e. academics and practitioners) about the formulation, implementation and development (1990s–2010s) of each project in each of the 9 selected urban regions. On average, each interviewee answered questions on 3.1 large-scale urban development projects. Three cases were subdivided into two cases because a clear differentiation between specific implementation stages was identified by the interviewees (expansion of the Barcelona airport, cases “bcn_airport80-90” and “bcn_airport00-16”; realisation of Lyon Part-Dieu, cases “lyo_partdieu70-90” and “lyo_partdieu00-16”; MediaCityUK, cases “man_salfordquays80-00” and “man_mediacityuk00-16”). Therefore, from the initial 35 cases, the final number of analysed cases in the lsUDPs dataset is 38.
       3.2 The data set lsUDPs: Data management and operationalisation
Interviews were fully transcribed and analysed through MAXQDA (version 12.3, VERBI GmbH, Berlin, Germany), and intercoder agreement was evaluated on a sample of nine interviews. We also compiled “synthetic case descriptions” (SCD) for each case (totalling more than 160 SCDs) to spot potential inconsistencies among interviewees’ accounts and to facilitate completion of the “calibration table” for each case (see below). An online expert survey distributed to the interviewees (response rate 78%) helped systematise the information collected during the interviews. We also consulted both academic and gray literature on the case studies to check for possible ambiguity and inconsistencies in the interview data, and to solve discrepancies between our assigned set membership scores and questionnaire values. Site visits were also carried out to retrieve additional information on the selected cases.
For each case (i.e. each of the 38 selected large-scale urban development projects), we operationalised each condition (i.e. STATE, PRIVATE, EVENTS, TRANSP, RESIST) and the outcome (CONCOR) in terms of sets, for subsequent application of Qualitative Comparative Analysis. This process is called “calibration”; we used a number of indicators for each condition to qualitatively assess each large-scale project across the conditions. The case-based qualitative assessment was then transformed into fuzzy-set membership values. Fuzzy-set membership values range from 0 to 1, and should be conceived as “fundamentally interpretative tools” that “operationalize theoretical concepts in a way that enhances the dialogue between ideas and evidence” (Ragin 2000:162, in “Fuzzy-set Social Science”. Chicago: University Press). We employed a four-value fuzzy-set scale (0, 0.33, 0.67, 1) to “quantify” into set membership scores the individual histories of cases retrieved from interview data. Only the condition TRANSP was calibrated as a crisp-set (0, 1).
The translation of qualitative case-based information into numerical fuzzy-set membership values was iteratively performed by populating a calibration table following standard practices recently emerged in QCA when dealing with qualitative (interview) data.

en: lsUDPS Large-scale urban development projects in European urban regions

en: spatial-planning urban-transformations land-use-change europe strategic-spatial-planning large-scale-urban-development-projects land-use-transformations strategic-projects

en:
Resource Title: QCA R script large-scale urban development projects Europe
    Size: 7968 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/25ffb140-fb3f-43ac-9d24-16c92cac36aa/resource/33fcd866-c6e6-4d16-a1ff-87363adb819c/download/1905_qca_prj_implementation.r
    Description: For the set up of this R script, we used the “QCA” (Duşa, 2019) and “SetMethods” (Oana and Schneider, 2018) R packages.

The following further resources were also used:
Dusa, A. (2019). QCA with R: A comprehensive resource. Springer: Berlin.
Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences. A guide to qualitative comparative analysis. Cambridge: Cambridge University Press.
Thomann, E., Oana, I.-E., & Wittwer, S. (2018). Performing fuzzy- and crisp set QCA with R. A user-oriented beginner's guide. Retrieved from http://www.evathomann.com/links/qca-r-manual

en:
Resource Title: QCA data set large-scale urban development projects
    Size: 1617 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/25ffb140-fb3f-43ac-9d24-16c92cac36aa/resource/e8bce8b1-64cc-4831-b5cc-496fd607364a/download/qca_prj_implementation.txt
    Description: Please refer to  the sections: 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalization in the Metadata for further details on this resource.

europe

display_name: europe

land-use-change

display_name: land-use-change

land-use-transformations

display_name: land-use-transformations

large-scale-urban-development-projects

display_name: large-scale-urban-development-projects

spatial-planning

display_name: spatial-planning

strategic-projects

display_name: strategic-projects

strategic-spatial-planning

display_name: strategic-spatial-planning

urban-transformations

display_name: urban-transformations


en:
This is the source code to compute rainfall thresholds for debris flows or landslides following Hirschberg et al. (2021).
 How to install and run the example
Pyhton has to be installed to run the codes. To make sure it works correctly, it is easiest to install Anaconda and create an environment with the right packages from the yml-file. To this end, in a command-line interpreter, change the working directory to where you saved this project and run the following:
`$ conda env create -f environment.yml`
`$ conda activate thresholds` or `$ source activate thresholds`
To run an example:
`$ python run_example()`
It will save a dat-file and a figure as Fig. 7 in Hirschberg et al. (2021), where more information can be found.
 License
GNU General Public License v3.0

en: Source code for: Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

en: illgraben landslide debris-flow rainfall-thresholds python random-forest

debris-flow

display_name: debris-flow

illgraben

display_name: illgraben

landslide

display_name: landslide

python

display_name: python

rainfall-thresholds

display_name: rainfall-thresholds

random-forest

display_name: random-forest


aerosol-data-davos-wolfgang

en:
Aerosol properties were measured between February 8 and March 31 2019  at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (- 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.
Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 &956;m) and GT-526S (0.3 – 5 &956;m), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM&8321;, PM&8322;,&8325; and PM&8321;&8320; (assumptions: particle density: 1.65 g cm&713;&179;, refractive index: 1.5+i0).
Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 &956;m and ~20 &956;m by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cm&713;&179;).
Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cm&713;&179;).
Coriolis &956; and DRINCZ: A microbial air sampler (Coriolis &956;, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 &956;m were drawn with an air flow rate of up to 300 l min&713;&185; into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

en: Aerosol Data Davos Wolfgang

en: particles aerosol inp ice-nucleation raclets drincz

envidat

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

en: EnviDat: the Environmental Data Portal

title_for_slug: aerosol-data-davos-wolfgang

envi

en: Environment

en:
Resource Title: OPC GT-526S Dataset Davos Wolfgang
    Size: 14718206 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/03a2914e-d0c6-438a-b4c0-5f576db641da/resource/a0a29389-e885-4e4d-bc5f-a65913a0ba46/download/opc-gt526s_wolfgang.csv
    Description: Particle counts were recorded in six size channels (0.3, 0.5, 0.8, 1.5, 2.5 and 5 μm) in a resolution of 20 seconds (time in UTC) by a particle counter (GT-526S, Met One Instruments, Inc.).

en:
Resource Title: OPC-N3 Dataset Davos Wolfgang
    Size: 30756674 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/03a2914e-d0c6-438a-b4c0-5f576db641da/resource/2632179e-3c62-45f8-9454-c3d946ccf430/download/opc-n3_wolfgang.csv
    Description: Particle concentrations in a size range of 0.35 μm to 40 μm were measured with an OPC-N3 (Alphasense, Inc.) in a resolution of 20 seconds (time in UTC). PM₁, PM₂,₅ and PM₁₀ were directly derived.

en:
Resource Title: APS Dataset Davos Wolfgang
    Size: 100296188 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/03a2914e-d0c6-438a-b4c0-5f576db641da/resource/1b62d21e-674c-4fa4-884b-cce7f18801e6/download/aps_wolfgang.nc
    Description: The APS file contains aerosol data (total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) for aerodynamic diameters between 0.5 μm and ~20 μm as well as instrument characteristics, in a temporal resolution of 20 seconds, with time in UTC, measured at Davos Wolfgang.

en:
Resource Title: SMPS Dataset Davos Wolfgang
    Size: 32383567 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/03a2914e-d0c6-438a-b4c0-5f576db641da/resource/1135c794-6c1c-49cb-9189-4d0a67f15b0d/download/smps_wolfgang.nc
    Description: The SMPS file contains aerosol data (particle concentration, total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) in a size range between 12 and 460 nm (electrical mobility diameter) as well as instrument characteristics (sample temperature, pressure, relative humidity, mean free path and gas viscosity), in a temporal resolution of 2 minutes, with time in UTC, measured at Davos Wolfgang.

en:
Resource Title: DRINCZ Dataset Davos Wolfgang
    Size: 181636 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/03a2914e-d0c6-438a-b4c0-5f576db641da/resource/6c39beac-0605-4915-9f93-ef22ff5b29f4/download/raclets_inp_wop.nc
    Description: The DRINCZ file contains information on temperature (in °C) dependent frozen fractions, number of Ice Nuleating Particles (INP, per standard liter) and differential INP concentrations (per standard liter and Kelvin).

aerosol

display_name: aerosol

drincz

display_name: drincz

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

particles

display_name: particles

raclets

display_name: raclets


en:
The present dataset is part of the report titled Gradinaru S.R., Hersperger A.M., Schmid F. (2021). Deriving Planning Intentions from written planning documents. Report on CONCUR Project- From plans to land change: how strategic spatial planning contributes to the development of urban regions.
The data corresponds to the data collected as part of the DPI Method for deriving all PIs contained in a plan (open coding) as detailed in section 4 of the report. The method involved reading the plans to break down of information in meaningful discrete “incidents” or planning intentions. To identify the planning intentions, the starting points were represented by a) the structuring of the plans in chapters and sub chapters and b) the themes that the plans addressed. Thus, the collected information was not grouped according to pre-defined categories of planning intentions, but rather put together as a list of intentions as revealed by each plan. As a result, we provide, for each case study, a document (named [Urban region name] PI as defined in the plan) which contains:
	Date when the information was filled in.
	Name of the urban region and analysed strategic spatial plan .
	A list of all planning intentions contained in a plan, with each PI being addresses as follows:
	Name of PI as it appears in the plan
	Translated name of the PI (i.e. short name for easy understanding of the meaning)
	Explanation regarding the meaning of the PI
	Why the PI is considered a priority for the urban region
	Spatial information on the PI (text and cartographic representations).
In total, 14 documents are available, one for each case study. Documents contain up to 20 pages of information extracted from the plans together with explanations and notes taken during plan reading.

en: Planning intentions in strategic plans of European urban regions

en: strategic-plans development-priorities spatial-information urban-region europe

development-priorities

display_name: development-priorities

europe

display_name: europe

spatial-information

display_name: spatial-information

strategic-plans

display_name: strategic-plans

urban-region

display_name: urban-region


causal-effect-of-mfoz

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.

en: Causal effect of MFOZ

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

title_for_slug: causal-effect-of-mfoz

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