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: milan imperviousness soil-sealing urban-atlas barcelona copernicus

de:
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

de:
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

de:
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

de:
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

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


$oid: OBSOLETE

de: EnviDat

en:
Supplementary Data Sample Plot Inventory Sihlwald
The Sihlwald is one of the largest contiguous beech forests in the Swiss Plateau region. In the year 2000, timber harvesting was abandoned. Since 2007 the forest has been under strict protection as a natural forest reserve on an area of 1098 ha and since 2008 as a cantonal nature and landscape conservation area (SVO Sihlwald). Since 2010, it carries the national label ‘Nature discovery park’ (‘Naturerlebnispark’). As part of the national monitoring in nature forest reserves, a sampling inventory (calipering threshold of 7 cm) with 226 plots on an area of 917 ha was carried out in the Sihlwald in autumn and early winter 2017. The aim was to describe the state and development of the forest structure and make comparisons with earlier sampling inventories in the same area from 1981, 1989 and 2003.
This dataset contains supplementary tables for the publication by Brändli et al. (2020). The metadata file describes the structure of the tables.

en: natural-forest-dynamics sihlwald fagus-sylvatica forest-reserves forest-inventory

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

type: dataset

key: guid

value: Envidat Harvester

key: uri

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

de:
Resource Title: Standing dead trees 2003 - 2017
    Size: 7650 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/19f63643-114b-4e04-afc7-75d9cf73a7d9/resource/9d94affc-b8b6-4e94-8980-87df0df70749/download/standingdeadwood2003_2017.csv
    Description: Contains data on standing dead trees with a DBH >= 8 cm.

format: CSV

media_type: text/csv

de:
Resource Title: Standing living trees 2017
    Size: 92715 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/19f63643-114b-4e04-afc7-75d9cf73a7d9/resource/ffa9e767-aa7b-45d7-8cbd-3347932957c6/download/trees2017.csv
    Description: Contains data on standing, living trees with a DBH >= 7 cm.

de:
Resource Title: Standing dead trees 2017
    Size: 16479 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/19f63643-114b-4e04-afc7-75d9cf73a7d9/resource/2ea64c14-798e-4816-a80f-e88095720643/download/standingdeadwood2017.csv
    Description: Contains data on standing dead trees with a DBH >= 7 cm.

de:
Resource Title: Standing living trees 1981 - 2017
    Size: 417555 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/19f63643-114b-4e04-afc7-75d9cf73a7d9/resource/d3cc5592-e46f-4247-b36d-70214772ee68/download/trees1981_2017.csv
    Description: Contains data on standing, living trees with a DBH >= 8 cm.

de:
Resource Title: Data Description
    Size: 106663 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/19f63643-114b-4e04-afc7-75d9cf73a7d9/resource/fc55aacc-4927-4e94-a405-7e6a26a63388/download/supplementarymaterialsihlwald.pdf
    Description: Describes the available tables

format: PDF

media_type: application/pdf

de:
Resource Title: Regeneration 2017
    Size: 67773 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/19f63643-114b-4e04-afc7-75d9cf73a7d9/resource/27f21626-d1a3-4941-815d-019c88c8c618/download/regeneration2017.csv
    Description: Data on regeneration inventory 2017 (living trees between 10 cm height and DBH < 7 cm)

de:
Resource Title: Lying deadwood 2017
    Size: 1349 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/19f63643-114b-4e04-afc7-75d9cf73a7d9/resource/39f93467-552f-4f37-b59f-def4b99ac897/download/lyingdeadwood2017.csv
    Description: Contains data on lying deadwood assessed on 3 transects.

fagus-sylvatica

display_name: fagus-sylvatica

forest-inventory

display_name: forest-inventory

forest-reserves

display_name: forest-reserves

natural-forest-dynamics

display_name: natural-forest-dynamics

sihlwald

display_name: sihlwald


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: groundwater runoff alptal stable-water-isotopes streamflow

de:
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)

de:
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)

de:
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)

de:
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

de:
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)

de:
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)

de:
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)

de:
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)

de:
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)

de:
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)

de:
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)

de:
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

de:
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)

de:
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

de:
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

alptal

display_name: alptal

groundwater

display_name: groundwater

runoff

display_name: runoff

stable-water-isotopes

display_name: stable-water-isotopes

streamflow

display_name: streamflow


de: EnviDat

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

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

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

envidat

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.

de: EnviDat: das Umwelt-Datenportal

envi

de: Umwelt

de:
Resource Title: Fielddata_2020-02-14_Guilds.csv
    Size: 241361 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/b64e697c-bef8-455f-a2ca-96e269fdfd1c/resource/8b121238-5319-4a48-ad15-a0382e8e37f8/download/fielddata_2020-02-14_guilds.csv
    Description: Complete set of field data and LiDAR data including bat activity measurements

de:
Resource Title: Variable description_20220411.csv
    Size: 3254 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/b64e697c-bef8-455f-a2ca-96e269fdfd1c/resource/121b2a4d-d2a0-416e-bd23-3a744cb57b1b/download/variable-description_20220411.csv
    Description: Description of all variables given in the file 'Felddaten_2020-02-14_Guilde.csv'

de:
Resource Title: R_Script_Publication_20220405.R
    Size: 27971 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/b64e697c-bef8-455f-a2ca-96e269fdfd1c/resource/50bc0f40-a3ab-49d6-82bb-35ffe03d387f/download/r_script_publication_20220405.r
    Description: R scripts for all analyses

chiroptera

display_name: chiroptera

echolocation

display_name: echolocation

forest-management

display_name: forest-management

habitat-modelling

display_name: habitat-modelling

vegetation-structure

display_name: vegetation-structure


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

de:
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.

de:
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.

de:
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.

avalanche-activity

display_name: avalanche-activity

avalanche-forcasting

display_name: avalanche-forcasting

avalanche-size

display_name: avalanche-size

danger-level

display_name: danger-level

snow-avalanche

display_name: snow-avalanche


en:
Aerosol properties were measured between February 8 and March 31 2019  at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (- 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.
Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 &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 (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cm&713;&179;).
Coriolis &956; and LINDA: 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&8254;&185; into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

en: Aerosol Data Weissfluhjoch

en: ice-nucleation inp linda particles aerosol raclets

de:
Resource Title: OPC-N3 Dataset Weissfluhjoch
    Size: 54387041 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67ab6458-99fd-4f2e-b707-20c8dcf52109/resource/3932aae8-fa08-4dd7-bb1c-35cdfe3859bf/download/opc-n3_weissfluhjoch.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.

de:
Resource Title: APS Dataset Weissfluhjoch
    Size: 98624420 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/67ab6458-99fd-4f2e-b707-20c8dcf52109/resource/c7846a5d-4a0f-462b-bddc-594bfb6a578b/download/aps_weissfluhjoch.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 Weissfluhjoch.

de:
Resource Title: LINDA Dataset Weissfluhjoch
    Size: 191898 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/67ab6458-99fd-4f2e-b707-20c8dcf52109/resource/6451c23d-b1ec-4100-8f78-ba35611e540f/download/raclets_inp_wfj.nc
    Description: The LINDA file contains information on temperature (in °C) dependent frozen fractions, number of Ice Nuleating Particles (INP, per standard liter) and differential INP concentrations (per standard liter and Kelvin).

de:
Resource Title: SMPS Dataset Weissfluhjoch
    Size: 32316159 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/67ab6458-99fd-4f2e-b707-20c8dcf52109/resource/585a81d2-1d89-4453-b9b0-49176e48359d/download/smps_weissfluhjoch.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 Weissfluhjoch.

de:
Resource Title: OPC-GT526S Dataset Weissfluhjoch
    Size: 11552644 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67ab6458-99fd-4f2e-b707-20c8dcf52109/resource/6e0ab260-4ec2-4b08-b485-63bbed6b4433/download/opc-gt526s_weissfluhjoch.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.).

aerosol

display_name: aerosol

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

linda

display_name: linda

particles

display_name: particles

raclets

display_name: raclets


en:
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: ice-nucleation inp particles raclets aerosol drincz

de:
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.

de:
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.

de:
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.).

de:
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).

de:
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.

drincz

display_name: drincz


de: EnviDat

en:
Data set consists of monthly mean values for snow depth and days with snow on the ground intended for the use of break detection with ACMANT, Climatol and HOMER. List and coordinates of stations used as well as metadata and break detection results from all three methods is included.
 Columns
Monthly means for each hydrological year: Nov, Dec, Jan, Feb, Mar, Apr with May to Oct set to zero

en: Input data for break point detection of Swiss snow depth time series

en: snow-depth homogenisation break-detection snow-measurements climate

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.

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.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

envi

de: Umwelt

de:
Resource Title: HOMER dHS1
    Size: 113907 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9c1c977e-d0e0-4833-9644-87450d03822b/resource/a97422c5-c9b1-4e5d-a5b7-95eb3a324fe7/download/homer_input_dhs1.zip
    Description: Input files for HOMER

format: ZIP

media_type: application/zip

de:
Resource Title: Climatol dHS1
    Size: 51684 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9c1c977e-d0e0-4833-9644-87450d03822b/resource/bf7e327e-38dc-425e-9e7b-1f013f4059e5/download/climatol_dhs1.zip
    Description:

de:
Resource Title: ACMANT HSavg
    Size: 216404 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9c1c977e-d0e0-4833-9644-87450d03822b/resource/abef3eff-7768-4a87-9249-df76d137333f/download/acmant_input_hsavg.zip
    Description:

de:
Resource Title: HOMER HSavg
    Size: 190648 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9c1c977e-d0e0-4833-9644-87450d03822b/resource/78a07dc9-d524-4e62-827d-f86203a59b3c/download/homer_input_hsavg.zip
    Description: Input files for HOMER

de:
Resource Title: Station relocations
    Size: 117992 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c1c977e-d0e0-4833-9644-87450d03822b/resource/1449d076-2b75-4b49-a641-93d82349986f/download/relocations_various_sources.xlsx
    Description: Metadata compiled from various sources

format: XLS

de:
Resource Title: Climatol HSavg
    Size: 124338 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9c1c977e-d0e0-4833-9644-87450d03822b/resource/ff6ecc7b-3122-49a5-865a-7a015c04dc88/download/climatol_hsavg.zip
    Description:

de:
Resource Title: Breakpoints (R data file)
    Size: 14128 bytes
    Format: application/x-r-data
    URL: https://www.envidat.ch/dataset/9c1c977e-d0e0-4833-9644-87450d03822b/resource/d0a26c9a-6d41-45be-9002-8bd577536bf1/download/breaks.rda
    Description: Break points detected by all three methods and draft metadata table

de:
Resource Title: ACMANT dHS1
    Size: 137987 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9c1c977e-d0e0-4833-9644-87450d03822b/resource/000a2ddd-f5cf-42a4-a6de-bb76acbf3175/download/acmant_input_dhs1.zip
    Description:

de:
Resource Title: Station list
    Size: 9335 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/9c1c977e-d0e0-4833-9644-87450d03822b/resource/bf74e396-2a28-4b6d-87d7-fbf67bcb0722/download/000410stations_all.txt
    Description: List of stations used in this study. This file also serves as the station input file for HOMER

format: TXT

media_type: text/plain

break-detection

display_name: break-detection

climate

display_name: climate

homogenisation

display_name: homogenisation

snow-depth

display_name: snow-depth

snow-measurements

display_name: snow-measurements


en:
Study Aim
We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns.
 Data Description
Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables.
 Nuup Kangerlua (Godthåbsfjord)
_Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness
 Kangerluarsunnguaq (Kobbefjord)
Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height
 Methods
 Field survey in Nuup Kangerlua
We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022).
 Field survey in Kangerluarsunnguaq
We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022).
 Biotic variables
We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height.
 Climate variables
We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June – August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March – May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021).
 Topography and terrain wetness indicator variables
We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how ‘wet’ an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness.
 Computer code
Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim)
 Third-party data
Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).

en: Greenland shrubs and microclimate

en: accessible-shrub-forest climate arctic validation field-data sdm

envi

en: Environment

de:
Resource Title: Kangerluarsunnguaq
    Size: 93883 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/040d25dd-71c0-40e6-99a8-849427250f5d/resource/dc1bf94c-e651-4193-9a42-de3c9f3b11e9/download/kangerluarsunnguaq.csv
    Description: ## Collected August 2020 (PI: Nathalie Isabelle Chardon)

These data collected August 2020 (PI: Nathalie Isabelle Chardon). Please contact N. I. Chardon (nathalie.chardon_at_gmail.com) for individual quadrat (i.e. 1 m x 1 m) data, which are averaged at the 90 m spatial grid here.

## COLUMN ABBREVIATIONS

### Metadata for 90 m spatial grid

id: sampling zone and unique 90 m grid identifier

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

date: sampling day.month.year

### Species presence [1] or absence [0] at grid level

alcr: _Alnus crispa_

bena: _Betula nana_

dila: _Diapensia lapponica_

emni: _Empetrum nigrum_

hahy: _Harimanella hypoides_

lopr: _Loiseleuria procumbens_

oxpa: _Oxycoccus palustre_

phca: _Phyllodoce caerulea_

rhod: _Rhododendron_ sp.

saar: _Salix arctica_

sagl: _Salix glauca_

sahe: _Salix herbacea_

vaul: _Vaccinium uliginosum_

drin: _Dryas integrifolia_

thpr: _Thymus praecox_

### Habitat characteristics [% cover]

shrub: all shrub species

gram: graminoids

forb: forbs

bryo: bryophytes

lich: lichens

litt: litter

bare: bare ground

stone: stones (> 2 cm)

org: organic crust

### Shrub species height (maximum vegetative height) [mm] and cover (estimated in 1x1 m quadrat) [%]

alcr_c: _Alnus crispa_ cover

alcr_h: _Alnus crispa_ height

bena_c: _Betula nana_ cover

bena_h: _Betula nana_ height

dila_c: _Diapensia lapponica_ cover

dila_h: _Diapensia lapponica_ height

emni_c: _Empetrum nigrum_ cover

emni_h: _Empetrum nigrum_ height

hahy_c: _Harimanella hypoides_ cover

hahy_h: _Harimanella hypoides_ height

lopr_c: _Loiseleuria procumbens_ cover

lopr_h: _Loiseleuria procumbens_ height

oxpa_c: _Oxycoccus palustre_ cover

oxpa_h: _Oxycoccus palustre_ height

phca_c: _Phyllodoce caerulea_ cover

phca_height: _Phyllodoce caerulea_ height

rhod_c: _Rhododendron_ sp. cover

rhod_h: _Rhododendron_ sp. height

saar_c: _Salix arctica_ cover

saar_h: _Salix arctica_ height

sagl_c: _Salix glauca_ cover

sagl_h: _Salix glauca_ height

sahe_c: _Salix herbacea_ cover

sahe_h: _Salix herbacea_ height

vaul_c: _Vaccinium uliginosum_ cover

vaul_h: _Vaccinium uliginosum_ height

drin_c: _Dryas integrifolia_ cover

drin_h: _Dryas integrifolia_ height

### Community characteristics

rich_pa: shrub species richness at grid level

rich: shrub species richness averaged across 3 1x1 m sampling quadrats per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on _Betula nana_ (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on _Salix glauca_ (i.e. summed cover of non-_S. glauca_ species) [% cover]

ht_cwm: maximum shrub canopy height (i.e. community weighted mean by abundance of maximum shrub height) [mm]

### Topography and terrain wetness (calculated from)

tcws: Tasseled Cap Wetness component (Sentinal images)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

### Downscaled climate (time series 1984 – 2013)

insoljja: average June-August insolation [MJ cm-2 yr-1]

precipmam: cumulative March-May precipitation [mm]

tempcont: yearly maximum – minimum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

precipjja: cumulative June-August precipitation [mm]

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

de:
Resource Title: GitLab Repository
    Size: 235019467 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/040d25dd-71c0-40e6-99a8-849427250f5d/resource/9a2b483b-baf7-40ff-98a2-faf92ef7b7a2/download/gl_microclim.zip
    Description: Zip drive of GitLab repository contents (https://gitlab.com/nathaliechardon/gl_microclim).

de:
Resource Title: Nuup Kangerlua
    Size: 27166 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/040d25dd-71c0-40e6-99a8-849427250f5d/resource/49bbe7fa-5694-4aff-9754-1d66bdc0978e/download/nuupkangerlua.csv
    Description: ## Collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen)

These data collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen). Please contact J. Nabe-Nielsen (jnn_at_ecos.au.dk) to request access to data on all surveyed shrub species. Available here are only data used in Chardon et al. 2022.

## COLUMN ABBREVIATIONS

### Metadata for 90 m spatial grid

year: sampling year

alt: average altitude of grid as recorded during field surveys [m]

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

### Species presence [1] or absence [0] at grid level

pa_bena: _Betula nana_

pa_sagl: _Salix glauca_

### Shrub species cover (calculated from pin hits in 0.7 x 0.7 m frame, based on total of 25 hits) [%]

cov_bena: _Betula nana_ cover

cov_sagl: _Salix glauca_ cover

### Downscaled climate (time series 1984 – 2013)

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

tempcont: yearly maximum – minimum temperature [ºC]

precipmam: cumulative March-May precipitation [mm]

precipjja: cumulative June-August precipitation [mm]

insoljja: average June-August insolation [MJ cm-2 yr-1]

### Topography and terrain wetness (calculated from)

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

tcws: Tasseled Cap Wetness component (Sentinal images)

### Community characteristics

rich: shrub species richness averaged across pin-point frames per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on Betula nana (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on Salix glauca (i.e. summed cover of non-_S. glauca_ species) [% cover]

accessible-shrub-forest

display_name: accessible-shrub-forest

arctic

display_name: arctic

field-data

display_name: field-data

sdm

display_name: sdm

validation

display_name: validation


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

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

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

carbon

display_name: carbon

drought

display_name: drought

fauna-data

display_name: fauna-data

forest

display_name: forest

litter

display_name: litter

pfynwald

display_name: pfynwald

pinus

display_name: pinus

soil

display_name: soil


en: Data set consisting of snow climate indicators derived from parallel manual snow measurements in Switzerland.

en: climate-indicators snow-onset-and-disappearance-dates parallel-measurements snow-depth snow-height

de:
Resource Title: ReadMe
    Size: 783 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/9dcb9627-6bf4-435e-a9ec-22cb5645ce8e/resource/0b809d2e-f975-4093-97bd-44635664e729/download/readme.txt
    Description: File containing information about variables used in this data set

de:
Resource Title: snow climate indicators
    Size: 352530 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9dcb9627-6bf4-435e-a9ec-22cb5645ce8e/resource/2902ba17-736d-453e-8ac0-bd9e95cdf6b1/download/snow_climate_indicators.zip
    Description: Snow climate indicators derived from parallel manual snow measurements

de:
Resource Title: Sdur
    Size: 2652 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9dcb9627-6bf4-435e-a9ec-22cb5645ce8e/resource/f5873339-983b-4eda-8937-623bdc1ecab8/download/sdur.csv
    Description: Potential sunshine duration during MA and mean snow disappearance dates for a selection of station pairs.

climate-indicators

display_name: climate-indicators

parallel-measurements

display_name: parallel-measurements

snow-height

display_name: snow-height

snow-onset-and-disappearance-dates

display_name: snow-onset-and-disappearance-dates


de: EnviDat

en:
The present dataset is part of the published scientific paper Grădinaru, S. R., & Hersperger, A. M. (2019). Green infrastructure in strategic spatial plans: Evidence from European urban regions. Urban forestry & urban greening, 40, 17-28.
The goal of this research was to conduct a comparative analysis of the integration of green infrastructure concept in strategic spatial plans of European Urban regions. Specifically, the paper has the following objectivs: 1) which principles of GI planning are followed in strategic plans of urban regions? 2) can we identify different approaches to GI integration into strategic planning?. The study focues on a sample consisting of 14 case studies spanning 11 countries. We retrieved the strategic plans from the websites of the planning authorities. The list of the reviewed planning documents can be found in Appendix A of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper.
The planning documents were read in order to address the protocol items. The answer to the protocol items in each of the first two categories (items 1–11) was documented as text, while the answer for the third category, namely items addressing the planning principles (items 12–36), was coded according to Table 1 of the article.  As a result, we provide the folowing outputs:
•	GI_Dataset_1_Items_1-12.xlsx – available on request
o	Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure coresponding to items from 1 to 12 of the protocol. The data was discussed qualitativly in the research paper.
•	GI_Dataset_2_Items_12-36.csv – freely available
o	Results of the coding  on principles of GI planning followed in strategic plans of urban regions– this data was derived from the coding procedure coresponding to items from 12 to 36 of the protocol. The data served as input for the classifications performed through hierarchical cluster analysis. This data is a detailed version of Appendix C in the paper.

en: multifunctionality green-infrastructure planning-principles connectivity strategic-planning

envidat

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.

de: EnviDat: das Umwelt-Datenportal

de:
Resource Title: GI_Dataset_2_Items_12-36
    Size: 11895 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bc37e7de-560a-4df9-8ca1-0064a12533e0/resource/8332fe54-ab64-4666-a174-3590775bdd5e/download/dataset_2_items_12-36.csv
    Description: Results of the coding  on principles of GI planning followed in strategic plans of urban regions– this data was derived from the coding procedure coresponding to items from 12 to 36 of the protocol. The data served as input for the classifications performed through hierarchical cluster analysis. This data is a detailed version of Appendix C in the paper.

de:
Resource Title: GI_Dataset_1_Items_5-11
    Size: 11305 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/bc37e7de-560a-4df9-8ca1-0064a12533e0/resource/248318b8-4d2b-443f-a620-5bb649833402/download/dataset_1_items_5-11.csv
    Description: Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure corresponding to items from 5 to 11 of the protocol. The data was discussed qualitatively in the research paper.

connectivity

display_name: connectivity

green-infrastructure

display_name: green-infrastructure

multifunctionality

display_name: multifunctionality

planning-principles

display_name: planning-principles

strategic-planning

display_name: strategic-planning


en: The data set contains the danger descriptions (German) of the avalanche forecasts published at 5 pm between 27-Nov-2012 and 13-Feb-2020.

en: How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland?

en: switzerland danger-description consistency avalanche avalanche-bulletin avalanche-forecast avalanche-danger

envi

de: Umwelt

avalanche

display_name: avalanche

avalanche-bulletin

display_name: avalanche-bulletin

avalanche-danger

display_name: avalanche-danger

avalanche-forecast

display_name: avalanche-forecast

consistency

display_name: consistency

danger-description

display_name: danger-description

switzerland

display_name: switzerland


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

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

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

de:
Resource Title: day_Kueblis
    Size: 28335 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/df76c91e-b71e-4441-a905-31b32b8782fb/download/kub_day.csv
    Description: Daily data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

de:
Resource Title: Meteo_Kueblis
    Size: 674638 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/398c7a10-d1bf-4fd3-b7a9-8175843077d7/download/kub_meteo.csv
    Description: Snow and meteo data from automatic sensors from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

de:
Resource Title: GNSS_WFJ
    Size: 54679 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/f791ca79-0c9a-4055-9989-d5176e222935/download/wfj_gnss.csv
    Description: GNSS derived SWE, HS, and LWC for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

de:
Resource Title: GNSS_klosters
    Size: 19682 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/bc65a02c-f311-49c7-9007-a8df51a87610/download/klo_gnss.csv
    Description: GNSS derived SWE, LWC and HS from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-20

de:
Resource Title: day_Laret
    Size: 27183 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/3af4ec59-d2ff-425e-ba49-996cd5a85247/download/lar_day.csv
    Description: Daily data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

de:
Resource Title: Obs_Kueblis
    Size: 10744 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/ba8b38d2-fcc3-4345-b763-4b618e49836f/download/kub_obs.csv
    Description: Daily observer data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

de:
Resource Title: Webcam images
    Size: 236611230 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/292ddd39-edd8-4df4-8dc7-f79137c6494d/download/pictures_webcam.zip
    Description: Webcam images showing some typical differences in the snow conditions at the four sites (Küblis, Klosters, Laret and Weissfluhjoch) for two time periods (14-18.3.2019 and 27.1-11.2.2020). Both time periods included a precipitation event with partial rain-on–snow occurring at the lower locations.

de:
Resource Title: Prof_Laret
    Size: 3424 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/d18ef060-e61f-4196-82ad-859e3faa1c9b/download/lar_prof.csv
    Description: Profile data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

de:
Resource Title: Profiles_Klosters
    Size: 2530 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/dc102dd3-9d9d-4757-9263-9306c6474b5c/download/klo_prof.csv
    Description: Snow profiles data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

de:
Resource Title: GNSS_laret
    Size: 15587 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/0a95d946-3fd0-4d83-bfb9-4fdcb210a0d3/download/lar_gnss.csv
    Description: GNSS derived data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

de:
Resource Title: GNSS_kueblis
    Size: 17196 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/973a7cde-cf6e-484e-8da1-a2da1de4fc49/download/kub_gnss.csv
    Description: GNSS derived SWE, LWC and HS from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-20

de:
Resource Title: Profiles_WFJ
    Size: 2365 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/8a7318b9-571d-4da9-9686-2330bf0f373a/download/wfj_prof.csv
    Description: Snow profiles data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

de:
Resource Title: Manual profiles
    Size: 4065671 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/f7ac1de9-3b24-47fa-9f67-cd6560674a18/download/profiles.zip
    Description: Data of the traditional manual profiles for the locations Weissfluhjoch, Klosters and Küblis. For the location of Küblis there are no traditional manual profile data.  We provided the data as both PDF and .CAAML format (See https://niviz.org/  or http://caaml.org/Schemas/V5.0/Profiles/SnowProfileIACS/CAAMLv5_SnowProfileIACS.pdf). Moreover, we added LWC and density profiles when available (all locations).

de:
Resource Title: Meteo_WFJ
    Size: 2779069 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/be4ca553-12f8-4a2a-94bc-1672dfbac5b9/download/wfj_meteo.csv
    Description: Snow and meteo data from automatic sensors for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

de:
Resource Title: day_Klosters
    Size: 28691 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/40726e6f-f175-4033-8df7-526e42cdc266/download/klo_day.csv
    Description: Daily data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

de:
Resource Title: Profiles_Kueblis
    Size: 1151 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/e6d6f714-5f1a-4cc9-9ab1-3cf5417073a7/download/kub_prof.csv
    Description: Snow profiles data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

de:
Resource Title: Meteo_Klosters
    Size: 697178 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/0ba3714e-3caa-47d3-8b8c-c235fc57760e/download/klo_meteo.csv
    Description: Snow and meteo data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

de:
Resource Title: day_WFJ
    Size: 71919 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/01d5ba5c-4734-43b0-b1cd-68e97e0fd711/download/wfj_day.csv
    Description: Daily data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

de:
Resource Title: Obs_WFJ
    Size: 23691 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/4b796a10-3ab7-42c9-ab1f-3d6218692326/download/wfj_obs.csv
    Description: Daily observer data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

de:
Resource Title: Obs_Klosters
    Size: 13499 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/9bb74e8a-d19b-49ca-aecf-feed84c3e324/download/klo_obs.csv
    Description: Daily observer data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

de:
Resource Title: Meteo_LAR
    Size: 2994700 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/e582db3a-a0ba-4527-9c2f-ca961562c317/download/lar_meteo.csv
    Description: Snow and meteo data from automatic sensors for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

gnss

display_name: gnss

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

lwc

display_name: lwc

snow

display_name: snow


de: EnviDat

en:
Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016.
Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016.

en: Simulated avalanche problem types and seismic avalanche activity around Weissfluhjoch

en: snow-instability avalanche-danger snow-cover-modeling snow avalanche avalanche-activity crocus snowpack weissfluhjoch avalanche-problem-type

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.

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.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

envi

de: Umwelt

de:
Resource Title: 1999-2017 Avalanche problem types from SNOWPACK
    Size: 493 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/fd62a04d-b072-46d9-b860-0415656b3e38/download/wfj_1999-2017_snowpack_avalanche_problem_types.csv
    Description: Avalanche problem types were derived from snow cover simulations with the model SNOWPACK at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are shown in Figure 8 in Reuter et al. (2021).

de:
Resource Title: 2015-2016 Avalanche problem types from SNOWPACK
    Size: 2749 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/b76405da-ff97-4720-8a1e-323d6c4b6da9/download/2015-2016_snowpack_ava_prob_types.csv
    Description: Avalanche problem types were derived from a snow cover simulation with the model SNOWAPCK for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

de:
Resource Title: 1999-2017 Avalanche problem types from Crocus
    Size: 507 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/3b8f8b29-2be6-473e-8e33-0b598670531d/download/wfj_1999-2017_crocus_avalanche_problem_types.csv
    Description: Avalanche problem types were derived from snow cover simulations with the model Crocus at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are presented in Figure 8 in Reuter et al. (2021).

de:
Resource Title: 2015-2016 Avalanche problem types from Crocus
    Size: 2749 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/07bdeaa3-c5c4-4c04-bca2-66a8d84310ab/download/2015-2016_crocus_ava_prob_types.csv
    Description: Avalanche problem types were derived from a snow cover simulation with the model Crocus for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

de:
Resource Title: 2015-2016 Seismic avalanche acitivity catalogue
    Size: 322 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/aee18298-2eea-4876-ba1c-15cd7e69a031/download/2015_16_ava_seismic.csv
    Description: Avalanches were indentified in seismic data recorded by two seismic arrays, one deployed at Wannengrat (2,500 m) and another one deployed in the Dischma valley (2,000 m).
The data cover the period from 01.01.2016 - 30.04.2016 and are presented in Figure 7 in Reuter et al. (2021).

avalanche-activity

display_name: avalanche-activity

avalanche-problem-type

display_name: avalanche-problem-type

crocus

display_name: crocus

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

weissfluhjoch

display_name: weissfluhjoch


ect-and-rb-data-switzerland

en: The data set contains the data used in the publication "On snow stability interpretation of Extended Column Test results" by Techel et. al. (2020), published in Natural Hazards Earth System Sciences.

en: ECT and RB data Switzerland

en: stability-test snow-stability ect rutschblock slope-stability

title_for_slug: ect-and-rb-data-switzerland

envi

en: Environment

de:
Resource Title: data set
    Size: 67987 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/d22a5c70-e371-40ec-83b7-6ef5a2f298b1/resource/ebd6e2a3-0461-44eb-b8a3-1fd71b0761d1/download/data_envidat.csv
    Description: The data set contains the following information. For details, refer to the publication:
##Variables##
- unique identifier for the snow pit location (profileID)
- unique identifier for the ECT stability test (stabTestID)
- ECT stability classifications
-- orginal (ectStabOrig)
-- Winkler and Schweizer (ectStabWinkler)
-- new (ectStabNew)
- ECT results
-- lowest number of taps (ectScoreMin) for the decisive propagation
-- propagation
- slope stability information (slopeStability 0 = stable, 1 = unstable)
- RB stability class
- forecast avalanche danger level (forecastDangerLevel)

de:
Resource Title: data set (update)
    Size: 77825 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/d22a5c70-e371-40ec-83b7-6ef5a2f298b1/resource/e6cc7008-6537-4bb0-9924-d5e1af4b572b/download/data_envidat_update.csv
    Description: This is the same data set as uploaded originally, except for the following three additional columns, which are required to replicate the respective classification of ECT results: *depthCriteriaDecisive_ectStabOrig*, *depthCriteriaDecisive_ectStabWinkler*, *depthCriteriaDecisive_ectStabNew*. These columns have the following values:
- 1 if the depth criteria as defined in the paper was decisive for rating the ECT result with stability class 4
- 0 if the depth of the fracture plane was not decisive for the stability classification

In addition, the stability class has been recoded for:
- *ectStabOrig* from [1, 2] to [1, 4]
- *ectStabWinkler* from [1, 2, 3] --> [1, 2.5, 4]
to be fully in line with the stability classes used in the paper.

ect

display_name: ect

rutschblock

display_name: rutschblock

slope-stability

display_name: slope-stability

snow-stability

display_name: snow-stability

stability-test

display_name: stability-test


en:
Table of content: 1. Frequency of early concepts; 2. Frequency of additional concepts; 3. Use of any early concept; 4. Use of any additional concept, 5. Planning steps; 6. Protocol.
The present dataset is part of the published scientific paper entitled “Landscape ecological concepts in planning: review of recent developments” (Hersperger et al., 2021). The goal of this research was to review recent publications to assess the use of landscape ecological concepts in planning. Specifically, we address the following research questions: Q1. Landscape ecological concepts: What are they? How frequently are they mentioned in current research? Q2. How are landscape ecological concepts integrated in landscape planning?
We analysed all empirical and overview papers that have been published in four key academic journals in the field of landscape ecology and landscape planning in the years 2015–2019 (n = 1918). Four key journals in the field of landscape ecology were selected to conduct the analysis, respectively Landscape Ecology (LE), Landscape Online (LO), Current Landscape Ecology Reports (CLER), and Landscape and Urban Planning (LUP). The title, abstract and keywords of all papers were read in order to identify landscape ecological concepts. Then, all 1918 papers went through a keyword search to identify the use of early and additional concepts. We used the “pdfsearch” package in R programming language and searched for singular and plural forms and different variations of the concepts (see Supplementary material 1, Table A). As a result, we provided four outputs:
  1. Frequency of early concepts. This data provides the total number of times each article used each early concept (Q1). This data was used to produce the Figure 2a at the original publication.
  2. Frequency of additional concepts. This data provides the total number of times each article used each additional concept (Q1). This data was used to produce the Figure 2b at the original publication.
  3. Use of any early concept. This data provides the total number of times each article used any early concept (Q1).
This data was used to produce the Figure 3a at the original publication.
  4. Use of any additional concept. This data provides the total number of times each article used any additional concept (Q1). This data was used to produce the Figure 3b at the original publication.
To address the second question (Q2), the title, abstract and keywords of the papers included in our sample (n=1918 articles) were screened to identify papers that might show how landscape ecological concepts are integrated into planning. We selected 52 empirical papers (see Supplementary material – 4 Integration of landscape ecological concepts into planning), and we provided two outputs:
  5. Planning steps.  This data provides the number of times landscape ecological concepts were addressed in each
planning steps in 52 empirical papers analysed in detail (Q2). This data was used to produce the Figure 4 at the
original publication.
  6. Protocol for assessing the integration of landscape ecological concepts into planning. To systematically collect the
data, we used this protocol which addressed the following questions: (a) which type of planning is addressed by the
paper? (b) to which planning level does the paper refer to? (c) which concepts are integrated in any of the planning
steps described above?

en: The usage of landscape ecological concepts in the planning literature

en: planning socio-ecological-system landscape-services resilience literature-review landscape-perception green-infrastructure multifunctionality pdfsearch ecosystem-services sense-of-place decision-making landscape-planning landscape-ecology

de:
Resource Title: 6. Protocol
    Size: 91483 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/8675307f-37dc-4062-b990-3202634a5dbc/download/6_protocol.xlsx
    Description: Protocol for assessing the integration of landscape ecological concepts into planning. To systematically collect the data, we used this protocol which addressed the following questions: (a) which type of planning is addressed by the paper? (b) to which planning level does the paper refer to? (c) which concepts are integrated in any of the planning steps described above?

de:
Resource Title: 2. Frequency of additional concepts.
    Size: 1187545 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/7ae0ec68-0675-4d2b-a5d2-51d08e153f99/download/2-frequency_of_additional_concept.csv
    Description: This data provides the total number of times each article used each additional concept (Q1). This data was used to produce the Figure 2b at the original publication.

de:
Resource Title: 4. Use of any additional concept.
    Size: 146949 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/070af529-0b2d-4ead-b0d8-86aaf15afd04/download/4.-use_of_any_additional_concept.csv
    Description: This data provides the total number of times each article used any additional concept (Q1). This data was used to produce the Figure 3b at the original publication.

de:
Resource Title: 5. Planning steps.
    Size: 3898 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/f456f712-f67c-40d0-9f6a-7e18f2746ce4/download/5.-planning_steps.csv
    Description: This data provides the number of times landscape ecological concepts were addressed in each planning steps in 52 empirical papers analysed in detail (Q2). This data was used to produce the Figure 4 at the original publication.

de:
Resource Title: 3. Use of any early concept.
    Size: 157296 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/21b7a6f8-9f37-4ddb-b8fa-b17f25428996/download/3.-use_of_any_early_concept.csv
    Description: This data provides the total number of times each article used any early concept (Q1). This data was used to produce the Figure 3a at the original publication.

de:
Resource Title: 1. Frequency of early concepts.
    Size: 1002570 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/3b4f848b-5dc5-4f24-96b3-09778c0a8655/download/1.-frequency_of_early_concepts.csv
    Description:  This data provides the total number of times each article used each early concept (Q1). This data was used to produce the Figure 2a at the original publication.

decision-making

display_name: decision-making

ecosystem-services

display_name: ecosystem-services

green-infrastructure

display_name: green-infrastructure

landscape-ecology

display_name: landscape-ecology

landscape-perception

display_name: landscape-perception

landscape-planning

display_name: landscape-planning

landscape-services

display_name: landscape-services

literature-review

display_name: literature-review

multifunctionality

display_name: multifunctionality

pdfsearch

display_name: pdfsearch

planning

display_name: planning

resilience

display_name: resilience

sense-of-place

display_name: sense-of-place

socio-ecological-system

display_name: socio-ecological-system