maintainer: Tobias Wechsler

en: hydropower ecosystem-protection integral-water-management gis decision-making

format: SHP

en: Indicators

en: The indicators capture supporting ecosystem functions.

en:
 A spatial dataset and tool to simultaneously assess hydropower potential and ecological potential of the Swiss river network (Version 2016)

 Introduction
The steadily growing demand for energy and the simultaneous pursuit of decarbonisation are increasing interest in the expansion of renewable energies worldwide. In Switzerland, various funding projects have been launched to promote technologies in the field of renewable energies and their application as quickly as possible. With the introduction of a funding instrument in 2009, the number of projects submitted to produce renewable energies increased rapidly. The applications for small hydropower plants (≤ 10 MW) were correspondingly numerous. However, the assessment of the environmental impact and its comparison with hydropower importance is still not standardized.
To provide a basis for decision-making, a methodology was developed to determine the overall hydropower potential of a region. A detailed assessment of each river reach, and the systematic and holistic assessment of small hydropower projects at a regional scale are combined here. The assessment of a river reach is conducted at the river space (i.e., the river and adjacent areas) and at the surrounding landscape level. The HYDROpot_integral methodology was developed as part of Carol Hemund's dissertation (2012) at the University of Bern. It allows the evaluation of river reaches holistically, regarding ecological, social, economic and cultural criteria. As a second part of the overall project, the theoretical hydropower (or hydraulic) potential was calculated for the entire river network, which complemnets the spatial assessment. In particular, it is possible to classify river reaches into those that are more suitable for hydropower production (=”use”) and those that are more suitable for protection.

 Material and method
The HYDROpot_Integral method was developed and tested on the basis of cantonal and national data (Hirschi et al. 2013). The method relies on 73 geodata sets. This holistic assessment is the key element of the entire assessment procedure. Its aim is to quantify the importance of the ecosystem functions in terms of services. The river network (GWN07) is divided into reaches of about 450m and for each reach two study units are defined. The river space (RS) records the ecosystem functions of the water body and the nearby riparian area. The length of the RS is 315 m on average in Switzerland and a maximum of 450 m, whereas the width is based on the FOEN definition (BWG 2001: 18f) and varies between 7-107 m. The surrounding landscape (SLS) is the second survey unit that records the ecosystem functions of the surrounding area over a range of 21 m to 321 m. The SLS is calculated over three times the RS width. The length of the SLS is identical to the length of the RS. The ecosystem functions are divided into three types: regulating (service A), cultural (service B) and provisioning (service C) functions. Accordingly, the assessment of the functions is divided into three parts and three values are assigned to each river reach. The more functions there are and the greater their performance, the higher these values are and the more important the corresponding functions are. Hence, these values quantify the importance of the ecosystem functions and the ecological, cultural and economic ecosystem services of each river reach. The concatenation of ecosystem services results in a value (ABC) that can occur in 125 different versions due to the chosen five-level value scale; i.e. each digit of the three-digit number sequence can be assigned a value between 1 and 5. Each of the 125 combinations, and thus each river reach, has its own characteristics determined by the assessments of the three function types. To record the suitability, the combinations are ranked according to their ecological, cultural and economic ecosystem services. These rules mean that the combination that is most suitable for hydropower production at minimum cost in terms of ecological and cultural ecosystem services and has a high economic potential is ranked first; rank 125 indicates the highest ecological and cultural ecosystem services and the lowest economic services and is therefore most suitable for protection. A river reach that is excluded from hydropower use due to legislation, a so-called priority reach, is given rank 126 from the outset and specially marked. A more detailed description of the methods can be found in Hirschi et al. 2013 [Link]. The dataset presented here presents the latest state of the HYDROpot_integral methodology applied at the national level. Only national data that is easily accessible was used in the preparation of the dataset. The cantonal data, such as renaturation and revitalization, would have to be requested by each canton individually and was excluded here. The nationwide value synthesis was made with R.
A list of data sources can be found here [link to text file]
A list of all parameters can be downloaded here [link to PDF and text files]

 Dataset description
Data is presented as a single shapefile. It contains the river network and all assessment results obtained with HYDROpot_Integral.

 Changes in the methodology compared to the original method (Hirschi et. al 2013)

 * RS_A11 Ecomorphology: recorded for the whole of Switzerland and zero values equated with NA;
individual cantons such as Zug and St. Gallen have no mapped values according to the modular
concept of the federal government, Valais and Graubünden only the main valleys, Ticino and Fribourg not
completely (BAFU 2009).
 * RS_A14 Renaturation and revitalization data: not centrally available at the time of data collection.
centrally available, therefore values in GR were equated with NA.
 * RS_A15 Dilution ratio at wastewater treatment plants (WWTPs) for discharges: Zero values
equal to NA.
 * RS_A20 Water flow: use WASTA (2013) with hydroelectric power plants (> 300 kW) under
Federal control and dams serving hydroelectricity (Dam, as of 2013).
 * RS_C05 Synoptic hazard maps: are cantonally managed at the time of data collection,
Values in GR are marked with a 5 so that the systematics in the decision tree is not affected.
is affected.
 * Water quality (RS_A15, RS_A16, RS_A17, RS_A18, RS_A19): for the evaluation of the function type.
A Nature, it is important whether the median of the five values is less than or equal to 3 in total. This
evaluation is based on the decision tree for evaluating GR (Hirschi et al. 2013:22).
Therefore, an evaluation of the station data is made where critical and possible river segments
with poor quality (median less than 3) exist. Only two longer and one short
sections in Switzerland receive a lower median than 3 for water quality.
 * SLS_B06 Visibility: For 99 percent of the river segments (30,733 of 31,062) in the canton of
Bern (2015 reduced version), the landscape area is considered to be visible. Due to this high number
of sections, a large number of viewpoints in the layer of Swisstopo and
the computation time and computability in ArcGIS, the landscape area is classified as generally viewable
(equal to 1).
16 Method
Additional indicators were added (see Appendix B.2):
 * SLS_A21 Dissection
 * SLS_A22 Forest areas
 * SLS_B03 Hiking trails
 * SLS_B10 Residential and vacation homes
 * SLS_B11 Tourist infrastructure
 * SLS_C01 Landfill
 * SLS_C03 Infrastructure
 * SLS_C05 Industry
 * SLS_C06 Agricultural land
Not to be added, although present to some extent:
 * SLS_B06 Cultural assets of national importance: here, too, the calculability of the visibility analysis is
for the whole of Switzerland is limited
 * SLS_A15 Legally binding protection and land use planning: the individual river sections are not
clearly designated, i.e. no geodata exist
The following data are also not supplemented, as they are cantonal data:
 * SLS_A10 Cantonal nature reserves
 * SLS_A16 Forest reserves
 * SLS_A17 Cantonal inventories and contractually protected areas

decision-making

display_name: decision-making

ecosystem-protection

display_name: ecosystem-protection

gis

display_name: gis

hydropower

display_name: hydropower

integral-water-management

display_name: integral-water-management

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


en: snow alps photovoltaic winter-electricity bifaciality

state: active

type: dataset

media_type: application/zip

en:
Title: Monitoring data set at A2 (utility-scale power-plant Muttsee), months 10.2022-09.2023
Provider: AXPO - IWB Industrielle Werke Basel
Last edition: 17.01.2024
Editor: Y.Frischholz, Laboratory of Cryospheric Sciences, EPFL
File name structure:
The power-plant is made of 23 string boxes (GAK) and inverters (WR) named after their given number. One file per device is provided in the data/ subfolder, following the same naming convention (e.g. for the string box number 1, the file name is: “A2_GAK01.csv” and for the corresponding inverter, the file name is “A2_WR01.csv”). Refer to the publication for further details on the setup of the power plant.
File format:
All files are provided in the CSV format.
Column headings:
	String boxes and inverters (GAK & WR):
	⁃	device_name (string): name of the device (same as file name)
	⁃	timestamp (datetime64): time stamp in format “YYYY-MM-DD HH:mm:ss”, time-zone: UTC
	String boxes (GAK):
	⁃	I_ (double): input current from the substring number (W)
	⁃	I_SUM (double): output current of string box (A)
	⁃	U_DC (double): output voltage of string box (V)
	⁃	P_DC_ (double): input DC power from the substring number (W)
	⁃	P_DC (double): output DC power of string box (W)
	⁃	$S$_T#_$T$ (double): temperature from the sensor connected to the string box (empty if not existing), placed on the “front” or “back” side (S) of table T. Two types of temperature measurements (#) are available: 1=standard cell temperature (°C) , 2=module temperature on the rear side (°C) .
	⁃	$S$_SRAD_T (double): poa irradiation (W/m**2) from the sensor connected to the string box (empty if not existing), placed on the “front” or “back” side (S) of table T.
		Inverters (WR):
	⁃	I_DC (double): input DC current(A)
	⁃	U_DC(double): input DC voltage(V)
	⁃	P_DC (double): input DC power (W)
	⁃	P_AC (double): output AC power (W)
Preprocessing:
	1. Outliers:
	⁃	Quantiles Q1 and Q99 are computed for each variable
	⁃	Values below Q1 and above Q99 are considered outliers
	⁃	Outliers are replaced by NaNs.

	2. Missing data:
	⁃	Data holes of less or equal to 3 hours are interpolated linearly for all variables
	⁃	Data holes of more than 3 hours are replaced using the monthly nan-mean of the considered hour if more than 50% of the qualifying data points are available. For instance, if the data point 2022-12-12 12:12:00, is missing for a given variable and is part of a data hole larger than 3 hours, then it is replaced by the mean of all data points with hour = 12 of the month of December 2022, if 50% of all the qualifying data points are not NaN. In case more than 50% of the qualifying points are missing, the data point remains as missing (NaN).
	3. Wiring losses:
	⁃	To be consistent with A1, wiring losses are corrected at A2. The wiring loss between the string boxes and the inverters is removed by multiplying the value of input P_DC at the inverter level (P_DC,inv) by the mean ratio P_DC,inv / P_DC,arr.
	⁃	The computed mean wiring loss is 2.3\%. It is computed from 2023-02-01 to 2023-09-30, after the long data hole in the data sets of the string boxes.

Mean uptime:
	Raw:
	⁃	WR: 98.2%
	⁃	GAK*: 38.5%
	Processed:
	⁃	WR: 99.6%
	⁃	GAK*: 56.4%

	*The uptime at the string box level (GAK) is low because PMUs at this level shut down during steady and low signal leading to unavailable data at night. In addition, the 23 days downtime of 	January 2023 could not be interpolated nor replaced.

format: ZIP

en:
Title: Monitoring data set at A1 (test-site of Totalp), years 2018-2022
Provider: ZHAW - EKZ
Edition: Y.Frischholz, Laboratory of Cryospheric Sciences, EPFL
Last edition: 17.01.2024
File name structure:
The test site contains 6 segments named from A to F. The six files provided in the data/ subfolder follow the same naming convention (e.g. for segment A, the file name is: “A1_A.csv”). Refer to the publication for further details on the setups of the segments.
File format:
All files are provided in the CSV format.
Column headings:
	•	timestamp (datetime64): time stamp in format “YYYY-MM-DD HH:mm:ss”, time-zone: UTC
	•	poa_front (double): front plane-of-array irradiation (W/m**2)
	•	poa_back (double): rear plane-of-array irradiation (W/m**2)
	•	mod_temp (double): module’s temperature (°C)
	•	P_AC (double): output AC power (W)
Preprocessing:
	Outliers:
	⁃	Quantiles Q1 and Q99 are computed for each variable
	⁃	Values below Q1 and above Q99 are considered outliers
	⁃	Outliers are replaced by NaNs.
	Missing data:
	⁃	Data holes of less than 3 hours are interpolated linearly for all variables
	⁃	Data holes of more than 3 hours are replaced using the monthly nan-mean of the considered hour if more than 50% of the qualifying data points are available. For instance, if the data point 2022-12-12 12:12:00, is missing for a given variable and is part of a data hole larger than 3 hours, then it is replaced by the mean of all data points with hour = 12 of the month of December 2022, if 50% of all the qualifying data points are not NaN. In case more than 50% of the qualifying points are missing, the data point remains as missing (NaN).
	Wiring losses :
	⁃	As described in [1] , the test-site setup caused abnormally large wiring losses, mainly due to the distance to optimisers and to the low installed power per DC-wire.
	⁃	The wiring losses comprised between 10% and 15%, are averaged hourly and added to the measured DC power.

	Misc.:
	⁃	P_AC is computed for each segment based on the measured DC power and the average inverter’s efficiency (96.1%).
	⁃	As described in [1], the peak power of the installed module, used to compute the final yield is slightly different from the values announced by the manufacturer.
Mean uptime:
	Raw:
	⁃	95.2%
	Processed:
	⁃	100%
References:
[1] 	D. Anderegg, S. Strebel, and J. Rohrer, “Alpine Photovoltaik Versuchsanlage Davos Totalp : Erkenntnisse aus 5 Jahren Betrieb,” de, Sep. 	2023, Accepted: 2023-09-29T09:13:16Z Publisher: ZHAW Zürcher Hochschule für Angewandte Wissenschaften.DOI: 10.21256/	zhaw-2524. [Online]. Available: https://digitalcollection.zhaw.ch/handle/11475/28797 (visited on 12/15/2023).

en:
On this repository you can find the monitoring data of the 3 PV power-plants analysed in the article _"Confirmation of the power gain for solar photovoltaic systems in alpine areas"_ (doi: 10.3389/fenrg.2024.1372680) . Each folder contains the following two elements:
1. **README.txt** : describes the content of the folder. Especially it describes the plant's monitoring system's architecture and relates it to the various files included in the data sub-folder. It also provides a detailed description of all processing steps leading to the data sets as available in the data sub-folder and used in the pre-cited article.
3. **data/** : sub-folder containing the file(s) used in the referenced article.

alps

display_name: alps

bifaciality

display_name: bifaciality

photovoltaic

display_name: photovoltaic

snow

display_name: snow

winter-electricity

display_name: winter-electricity

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Monitoring data sets of alpine photovoltaic power-plants


maintainer: EnviDat Support

en: snow-water-equivalent climate-change-impact uncertainty rain-on-snow cryosphere natural-climate-variability

en: climate realizations

format: SERVICE

en: The dataset contains for three variables (snow water equivalent, surface water input and liquid precipitation) 50 realizations of current and future climate periods for two time horizons (mid end end of century), two emission senarions (RCP 4.5 and 8.5) and 10 climate model chains (all EUR11 chains within CH2018). To quantify natural climate variability for projections of snow conditions and resulting rain-on-snow (ROS) flood events, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The data was extracted in 2021 from original model output.

climate-change-impact

display_name: climate-change-impact

cryosphere

display_name: cryosphere

natural-climate-variability

display_name: natural-climate-variability

rain-on-snow

display_name: rain-on-snow

snow-water-equivalent

display_name: snow-water-equivalent

uncertainty

display_name: uncertainty

en: Multiple realizations of daily snow water equivalent, surface water input and liquid precipitation projections for mid- and late-century


en: avalanche-accidents avalanche-fatalities number-of-caught-persons number-of-dead-persons avalanche-accident-statistics

en: Fatal avalanche accidents in Switzerland since 1995-1996

media_type: text/csv

format: CSV

mimetype: text/csv

en:
Attention: this data is not updated after 2022 anymore.
This data collection contains information concerning all accidents by snow avalanches causing at least one fatality in Switzerland. The data set commences on 01/10/1995. After the completion of a hydrological year, the new data is added.
The following information is provided:
 * avalanche identifier
 * date of the accident
 * accuracy of the date in range of days before and after
 * canton
 * name of the locality
 * start zone of the avalanche
  * coordinates (Swiss coordinate system, approximately in middle of start zone)
  * accuracy of the coordinates in meters
 * elevation (in meteres above sea level, app. in middle of start zone)
 * slope aspect (main orientation of start zone)
 * slope inclination (in degree, steepest point within start zone)
 * number of dead persons
 * number of caught persons
 * number of fully buried persons
 * forecasted avalanche danger level
 * activity/location of the accident party at the time of the incident

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-accidents

display_name: avalanche-accidents

avalanche-fatalities

display_name: avalanche-fatalities

number-of-caught-persons

display_name: number-of-caught-persons

number-of-dead-persons

display_name: number-of-dead-persons


en: roads geospatial buffer gis density jupyter

en: Plots

en: Random plot locations

mimetype: application/zip

en: Road Density Calculation (Python Notebook) online

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

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

buffer

display_name: buffer

density

display_name: density

geospatial

display_name: geospatial

gis

display_name: gis

jupyter

display_name: jupyter

roads

display_name: roads

en: Dataset for OGRS 2018 publication


en: climate-change-impact bryophyte madeira-island functional-taxonomic-groups climate-change moss endemic-species lichen functional-taxonomic-group liverwort bryophyte-life-strategy lichen-growth-form endemic lichen-growthform biodiversity photobiont disturbance elevational-gradient

en: MadCrypto – Bryophyte and macrolichen diversity in laurel forests of Madeira

en:
This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).

The dataset was used for the paper
Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science.

format: XLS

en:
This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).

The dataset was used for the paper
Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science 30: 1122–1133.

The excel file contains 5 sheets:
1) Plot information
2) Bryophyte data with species specific information, separated per substrate
3) Macrolichen data with species specific information, separated per substrate
4) Bryophyte data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 2 and 4 might therefore differ slightly.
5) Macrolichen data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 3 and 5 might therefore differ slightly.

biodiversity

display_name: biodiversity

bryophyte

display_name: bryophyte

bryophyte-life-strategy

display_name: bryophyte-life-strategy

climate-change

display_name: climate-change

climate-change-impact

display_name: climate-change-impact

disturbance

display_name: disturbance

elevational-gradient

display_name: elevational-gradient

endemic

display_name: endemic

endemic-species

display_name: endemic-species

functional-taxonomic-group

display_name: functional-taxonomic-group

functional-taxonomic-groups

display_name: functional-taxonomic-groups

lichen

display_name: lichen

lichen-growth-form

display_name: lichen-growth-form

lichen-growthform

display_name: lichen-growthform

liverwort

display_name: liverwort

madeira-island

display_name: madeira-island

moss

display_name: moss

photobiont

display_name: photobiont

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


en: snow-depth model-validation-data snow-water-equivalent model-calibration-data snow-density

en: swe2hs calibration and validation data

en:
 Contents of this archive

 README.md

 A readme file containing a description of the other files.

 automatic_stations_validation_data.csv

 Daily data from the automatic weather stations used for validation.

 Data sources and providers are listed in the SWE2HS paper, Table 1.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * site_id: code for the measurement site
  - HS_interpolated: boolean flag for interpolated values
  - SWE_interpolated: : boolean flag for interpolated values

 coordinates_swe2hs_paper_stations.csv

 Coordinates of all measurement sites used in the SWE2HS paper.

 Columns:

  * site_id: code for the measurement site
  * location_name: location name of the measurement site
  * lon_[wgs84]: longitude in WGS84
  * lat_[wgs84]: latitude in WGS84
  * elevation_[m]: atitude of the measurement site in [m]
  * dataset: dataset in which the station is included

 manual_station_calibration_data.csv

 Calibration data from the Swiss manual observer stations. Long station records
 trmmed to 15 random water-years.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * SWE_from_profile_[m]: biweekly measurement from the SWE in the measurement
   pit. Corrected with the bulk density in order to match the snow depth from
   the measurement stake. Please refer to the paper for further details.
  * site_id: code for the measurement site

 manual_stations_validation_data.csv

 Validation data from the Swiss manual observer stations. Remaining water-years
 from the long sttaion record not used for the calibration data.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * SWE_from_profile_[m]: biweekly measurement from the SWE in the measurement
   pit. Corrected with the bulk density in order to match the snow depth from
   the measurement stake. Please refer to the paper for further details.
  * site_id: code for the measurement site

 manual_stations_used_deltasnow_parameters.csv

 Site specific DeltaSNOW model parameters used for the calculation of the daily
 SWE series in 'manual_stations_calibration_data.csv' and
 'manual_stations_validation_data.csv'.

 Parameters were optimized by reducing RMSE to the biweekly snow pit SWE measurements.
 Please refer to the paper for further details.

 A python implementation of the DeltaSNOW model was used for the calculation:
 pydeltasnow (https://pypi.org/project/pydeltasnow/)

 For a detailed description of the DeltaSNOW model parameters please see the
 respective publication:

 Winkler, M., Schellander, H., & Gruber, S. (2021). Snow water equivalents
 exclusively from snow depths and their temporal changes: the Δ snow model.
 Hydrology and Earth System Sciences, 25(3), 1165-1187.

 Columns:

  * site_id: code for the measurement site
  * rho_max: Maximum density of an individual snow layer produced by the DeltaSNOW model in [kg/m3]
  * rho_null: Fresh snow density for a newly created layer [kg/m3
  * c_ov: Overburden factor due to fresh snow [-]
  * k_ov: Defines the impact of the individual layer density on the compaction due to overburden [-]
  * k: Exponent of the exponential-law compaction [m3/kg]
  * tau: Uncertainty bound [m]
  * eta_null: Effective compactive viscosity of snow for “zero-density” [Pa s]

en:
The data in this repository was used for the calibration and validation of the
SWE2HS model in the following publication:
Aschauer, J., Michel, A., Jonas, T., & Marty, C. (2023). An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development Discussions, 1-19. https://doi.org/10.5194/gmd-2022-258
Contains daily snow water equivalent and snow depth timeseries from stations in the European Alps.

model-calibration-data

display_name: model-calibration-data

model-validation-data

display_name: model-validation-data

snow-density

display_name: snow-density

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

en: SWE2HS model calibration and validation data


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

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

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

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

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

biodiversity

display_name: biodiversity

climate-change

display_name: climate-change

ecology

display_name: ecology

forest

display_name: forest

microclimate

display_name: microclimate

radiation-transfer

display_name: radiation-transfer

statistical-interpolation

display_name: statistical-interpolation

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


maintainer: Michael Lehning

en: snow snowpack winter-tourism snow-conservation snow-farming terrestrial-laser-scanning

en: Martell .smet

en: Meteodata used as input for SNOWAPCK simulations, orginating from Station Hintermartell.

en: Fluela .sno

en: Input snow profile (.sno file) used for SNOWPACK simulations of the Flüela heap

en: Martell .sno

en: Input snow profile (.sno file) used for SNOWPACK simulations of the Martell heap

en: HS Fluela spring 2015

media_type: text/plain

en:
Snow depth/ height of snow heap in spring 2015 obtained by terrrestrial laserscanning
cell size: 10 cm
spatial reference: CH1903

format: TXT

mimetype: text/plain

en: HS Martell autumn 2015

en:
Snow depth/ height of snow heap in autumn 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: European Datum 1950 UTM
Nodata: -9999

en: HS Fluela autumn 2015

en:
Snow depth/ height of snow heap in autumn 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: CH1903
Nodata: -9999

en: Fluela .smet

en: Meteodata used as input for SNOWAPCK simulations, orginating from Station Windtunnel and DAV.

en: HS Martell spring 2015

en:
Snow depth/ height of snow heap in spring 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: European Datum 1950 UTM
Nodata: -9999

en:
Two data sets obtained for snow farming projects (Fluela, Davos, CH and Martell, IT) in 2015.

The data set contains for each site:

* 10 cm GIS raster of snow depth calculated from terrestrial laserscanning surveys (TLS) in the end of winter season (April/May)

* 10 cm GIS raster of snow depth calculated from TLS in the end of summer season (October)

Input files for SNOWPACK model:

* .sno: snow profile at the end of winter

* .smet: meteorological data measured by weather stations in the area

For more details see Grünewald, T., Lehning, M., and Wolfsperger, F.: Snow farming: Conserving snow over the summer season, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-93, in review, 2017.

snow

display_name: snow

snow-conservation

display_name: snow-conservation

snow-farming

display_name: snow-farming

snowpack

display_name: snowpack

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

winter-tourism

display_name: winter-tourism

en: Snowfarming data set Davos and Martell 2015


snow-drift-station-micro-rain-radar

title_for_slug: snow-drift-station-micro-rain-radar

en: mountain precipitation snow rain radar raclets

en: Parameter MRR

media_type: application/pdf

en: The PDF contains additional information on parameters which were set for the certain time periods (time in UTC).

format: PDF

mimetype: application/pdf

en: MRR data Period 2

en: The dataset contains measurements between March 5 and March 8, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: MRR data of period 3

en: The dataset contains measurements between March 8 and March 13, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: MRR data of period 4

en: The dataset contains measurements between March 13 and March 22, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: MMR Data Period 1

en: The dataset contains measurements between March 1 and March 5, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: The instrument (MRR, Metek) was mounted at Gotschnagrat (LON: 46.859 LAT: 9.849) at a height of 1 m above snow surface (at the beginning of the campaign) with an orientation of 22° with respect to North and a horizontal viewing direction. The sampling time was either 5 s or 10 s, depending on the settings at the specific period. The MRR produces standard outputs like radar reflectivity, doppler velocity, etc., and additional information can be found [here](https://metek.de/de/product/mrr-2/).

mountain

display_name: mountain

precipitation

display_name: precipitation

raclets

display_name: raclets

radar

display_name: radar

rain

display_name: rain

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Snow Drift Station - Micro Rain Radar


maintainer: Amelie Fees

en: avalanche time-lapse-photography avalanche-activity glide-snow-avalanche snowpack-simulation

en: Glide-snow avalanche activity

en: Table that contains all glide-snow avalanche events that were detected in time-lapse photographs (season 2008/09 to 2021/22). The columns are explained in the README.txt

en: SNOWPACK simulations

en: SNOWPACK simulations (.pro and .smet) covering season 2008/09 to 2021/22 at 10 virtual locations on Dorfberg. The locations correspond to the most common glide-snow avalanche paths.

en: README

en: README.txt explaining the dataset and columns of tables in more detail.

en: Glide distances for opening glide-cracks

en: This folder contains all glide-cracks where glide-distances were extracted from time-lapse photographs. Glide-distances were calculated in x-direction (width) and y-direction (downslope, length) by georeferencing the images.

en:
This dataset includes the processed data of the glide-snow avalanche activity and dynamics on Dorfberg (Davos, Switzerland) covering seasons 2008/09 to 2021/22. This dataset was described in the research article:

Fees, A., van Herwijnen A., Altenbach, M., Lombardo, M., Schweizer, J.: Glide-snow avalanche characteristics at different time-scales extracted from time-lapse photography, Annals of Glaciology, 91

We extracted the dynamics of opening glide-cracks and the glide-snow avalanche activity from time-lapse photographs. Glide-snow avalanches were separated into surface and interface events using the liquid water content which was simulated with SNOWPACK at 10 virtual stations on Dorfberg.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

glide-snow-avalanche

display_name: glide-snow-avalanche

snowpack-simulation

display_name: snowpack-simulation

time-lapse-photography

display_name: time-lapse-photography

en: Glide-snow avalanche activity on Dorfberg, Davos, Switzerland


en: tree-volume section-wise-diameter allometry stemwood branchwood growing-stock

en: dataset with section-wise stem diameter and branchwood volume measurements. For column definitions see Column_definitions.pdf.

en: Updated dataset of outdated_Stem&Branchchwood_2024-02-29.txt, including corrections to the assignment of tree species to NFI main species and location information for two additional sites.

en: definitions of data in columns contained in the dataset Stem&Branchchwood_2024-02-29.txt

en: Revised version of the initial submission StemBranchwood_Didionetal_initialsubmission.pdf after review.

en: manuscript submitted for publication.

en: additional background information on this work

en:
The data presented here were prepared for publication in form of a data paper. The dataset presents an update of the information in Didion M, Herold A, Vulovic Z, Nitzsche J, Stillhard J (2022) Datasets for deriving functions for the stem- and branchwood volume in the Swiss National Forest Inventory. EnviDat. doi:https://www.doi.org/10.16904/envidat.358. The new dataset is based on an alternative, more accurate and comprehensive dataset, that was identified as the source of the dataset in Didion et al. 2022. It includes additional data, including newly digitized information.
The dataset includes section-wise stem measurements on 40’349 felled individual trees and empirically derived coarse (diameter >= 7 cm) and fine branch volume of 27’297 and 18’980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range. The dataset has undergone quality controls and due to its origin from 768 plots of the Experimental Forest Management project long-term consistency is assured.

allometry

display_name: allometry

branchwood

display_name: branchwood

growing-stock

display_name: growing-stock

section-wise-diameter

display_name: section-wise-diameter

stemwood

display_name: stemwood

tree-volume

display_name: tree-volume

en: A dataset of 40000 trees with section-wise measured stem diameter and length and volume of branches from across Switzerland


en: force time-series remote-sensing sentinel-2 raster-processing switzerland

media_type: image/tiff

format: TIFF

en:
We processed Sentinel-2 image time series from 2017 to 2023 for Switzerland with the Software FORCE (Frantz 2019) on the basis of [Sentinel-2 images](https://envidat.ch/#/metadata/sentinel-2-imagery-of-switzerland). The respective parameter files can be found here: [Github](https://github.com/TLKoch/Sentinel-2_CH). All the image time series consist of several TB and therefore access will be granted upon request.
The available bands (in spatial reference system EPSG 3035) are the following:
Red, Green, Blue, NIR, Red-Edge-1, Red-Edge-2, Red-Edge-3, SWIR-1, SWIR-2
The available indices (in spatial reference system EPSG 3035) are the following:
CCI, CIRE, NDWI/NDMI, NDVI, EVI
Further indices might be provided upon request.

 Processing
On the basis of processed Sentinel-2 images for the 14 Sentinel-2 tiles covering Switzerland (T31TGN, T32TLT, T32TMT, T32UMU, T32TNT, T32TPT, T31TGM, T32TLS, T32TMS, T32TNS, T32TPS, T32TLR, T32TMR, T32TNR), we processed the image time series further with FORCE v. 3.7.8-12.
We generated interpolated Sentinel-2 time series with a 5-day interval, corresponding to the theoretical revisit time of the Sentinel-2 satellites. It's important to note that the 5-day time series consist of interpolated and smoothed composites, not the original images. We used the radial basis convolutional filtering (RBF) available in the FORCE time series analysis (TSA) submodule (Schwieder et al. 2016). The RBF is similar to a spatial moving window average approach over time (Schwieder et al. 2016). We applied kernel width values of 10, 20, 30, and 50 days. We spectrally adjusted all the images to match Sentinel-2A, and we removed curve outliers and pixels that failed the quality checks for clouds and their shadows, snow, saturation, and limited illumination. The processed image time series are available in tiles of 30 by 30 km.

 Example images
Uploaded is an example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The time series spans the month of July from 2018.

force

display_name: force

raster-processing

display_name: raster-processing

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

time-series

display_name: time-series

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Sentinel-2 time series of Switzerland


maintainer: Dominique Weber

en: landsat mowing land-use grassland management use-intensity remote-sensing sentinel-2

en: Readme

en: Metadata and description of the maps and webcam-based reference data.

en: Grassland-use intensity map 2021

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2021. See readme file for more details.

format: GeoTIFF

en: Webcam-based reference data

en: Webcam-based reference data from 2020 and 2021 (see readme).

en: Grassland-use intensity map 2018

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2018. See readme file for more details.

en: Grassland-use intensity map 2019

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2019. See readme file for more details.

en: Preview time of 1st grassland management event 2020

media_type: image/png

en: Spatial distribution of the timing (day of year; DOY) of the first management event for Switzerland and for three zoomed-in regions (a, b, c). The three zoomed-in regions show different grassland-use intensities, which are reflected in the timing of the first event of the year (Weber et al., 2023; IN REVIEW)

format: PNG

en: Preview number of grassland management events 2020

en: Spatial distribution of the number of management events for Switzerland and for three zoomed-in regions (a, b, c). The three zoomed-in regions show different grassland-use intensities, which are reflected in the distribution of management frequency (Weber et al., 2023; IN REVIEW)

en: Grassland-use intensity map 2020

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2020. See readme file for more details.

en:
A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018–2021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework.

The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m × 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons.

We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see [Weber et al., 2023](https://doi.org/10.1002/rse2.372)).

The webcam-based reference data used for verification was subsequently added on 14.02.2024.

grassland

display_name: grassland

land-use

display_name: land-use

landsat

display_name: landsat

management

display_name: management

mowing

display_name: mowing

use-intensity

display_name: use-intensity

en: Grassland-use intensity maps for Switzerland


dischmex-meteorological-measurements

maintainer: Rebecca Mott

title_for_slug: dischmex-meteorological-measurements

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

en: Meteorological measurement 2014

en: data

en: Meteorological measurements 2016

en: Meteorological measurements 2014

en: Overview sensors

en: Meteorological measurement 2015

en: Location of the meteorological stations

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

dischma

display_name: dischma

dischmex

display_name: dischmex

meteo-station

display_name: meteo-station

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: DISCHMEX - Meteorological measurements


maintainer: Marcia Phillips

en: permafrost permos temperature borehole gemsstock

en: Map of PERMOS borehole network

en: PERMOS DATA POLICY

en: DATASET & PLOTS

en: Data visualiser and download service provided by PERMOS.

en:
Processed ground temperature measurements at the Gemsstock permafrost borehole in canton Uri, Switzerland. The borehole is located at 2940 m asl on a steep (50°) North-West slope (315°). The surface material is bedrock and borehole depth is 40 m. Thermistors used YSI 44008. Year of drilling 2006. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.

Publications

1. A. Haberkorn, M. Phillips, R. Kenner, H. Rhyner, M. Bavay, S.P. Galos, M. Hoelzle. Thermal regime of rock and its relation to snow cover in steep Alpine rock walls: Gemsstock, central Swiss Alps. 2015. Geografiska Annaler: Series A, Physical Geography. Volume 97. Issue 3. 579–597. http://dx.doi.org/10.1111/geoa.12101. 10.1111/geoa.12101.

2. R. Kenner, M. Phillips, C. Danioth, C. Denier, P. Thee, A. Zgraggen. Investigation of rock and ice loss in a recently deglaciated mountain rock wall using terrestrial laser scanning: Gemsstock, Swiss Alps. 2011. Cold Regions Science and Technology. Volume 67. Issue 3. 157–164. http://dx.doi.org/10.1016/j.coldregions.2011.04.006. 10.1016/j.coldregions.2011.04.006.

borehole

display_name: borehole

gemsstock

display_name: gemsstock

permafrost

display_name: permafrost

permos

display_name: permos

temperature

display_name: temperature

en: Processed permafrost borehole data (2940 m asl), Gemsstock, Switzerland


en: habitat-heterogeneity habitat-availability colonisation dead-wood biodiversity sloss saproxylic-beetles forest-management conservation

en: Community matrix (all beetles)

en: Abundances of all beetle species for each branch bundle

en: Variable description

en: Detailed description of the forest and environmental variables

en: Community matrix (saproxylic beetles)

en: Abundances of saproxylic beetle species for each branch bundle

en: Forest and environmental variables

en: Data on the branch bundle, forest and environmental variables

en:
The data was generated during a landscape experiment performed on 69 plots in Sihlwald forest between 2016 and 2019. Branch bundles of different sizes consisting of beech branches were exposed in the forest for one year to be colonized by saproxylic beetles and later reared for one year in emergence traps. All beetles emerging from the branches were collected. Overall 244 branch bundles are included and we found 66 beetles species (23511 individuals) of which 43 were classified as saproxylic (20873 individuals).
 Species data (2 tables)
Two community matrices (including abundance): one for saproxylic beetles and one for all beetles. Beetles were classfied as saproxylic following an extended list of the list reported in Schmidl & Bussler (2004).
 Model data (1 table)
*Response variables*
Species richness and abundance of (saproxylic) beetles.
*Branch bundles*
Branch bundles consisted of 1, 3, 6 or 12 branches and each bundle size was installed at each of the 69 plots. The bundles consisted of standardized freshly cut beech branches (Fagus sylvatica) with a length of 80 cm and a diameter between 3 and 6 cm. Total surface and volume of each bundle is reported.
*Tree on which the bundle was installed*
Information on the tree to which the bundles were attached (tree species, dbh, distance and azimut from the plot center).
*Temperature and light availability*
Temperature (plot level) and light availability (bundle level) were calculated.
*Deadwood availability in the landscape*
Based on a map of lying dead wood, dead wood amount and isolation were calculated for concentric circles with a radius ranging from 20 to 200 m (in 10 m steps) around each branch bundle.
 Variable descriptions (1 table)

biodiversity

display_name: biodiversity

colonisation

display_name: colonisation

conservation

display_name: conservation

dead-wood

display_name: dead-wood

forest-management

display_name: forest-management

habitat-availability

display_name: habitat-availability

habitat-heterogeneity

display_name: habitat-heterogeneity

saproxylic-beetles

display_name: saproxylic-beetles

sloss

display_name: sloss

en: Beetle communities reared from beech branches and associated forest and environmental variables


irkis-soil-moisture-measurements-davos

maintainer: Nander Wever

title_for_slug: irkis-soil-moisture-measurements-davos

en: soil-moisture meteorological-measurements meteo-station davos dischma-catchment

en: IRKIS soil moisture data (zip file)

en: Compressed archive (zip) containing soil moisture measurements, as well as meteorological measurements and interpolated meteorological time series.

en:
Meteorological and soil moisture measurements from soil moisture stations installed from October 2010 - October 2013 in the area surrounding Davos, in particular in the Dischma catchment.

There are in total 7 stations: 1202, 1203, 1204, 1205, 222, 333 and SLF2. For each of the stations, there is a:

* vwc_[stn].smet: containing the soil moisture measurements

* station_[stn].smet: in-situ measured meteorlogical parameters. Note, the quality of these measurements for stations 1202, 1203, 1204 and 1205 is very low, with data gaps. Use this data with care. For stations 222, 333 and SLF2, data quality is high and only the default cautiousness should be applied.

* interpolatedmeteo_[stn].smet contains per stations a dataset derived by interpolating from several stations in the Davos area to the stations location. This dataset was generated from the output of the Alpine3D model, of which simulations are presented in the Wever et al. (2017) manuscript.

At the soil moisture measurement sites, Decagon 10HS sensors were installed, at 10, 30, 50, 80 and 120 cm depth. Per depth 2 sensors were installed, labelled A and B in the datafiles. Note that at stations 1203, 1204 and 1205, sensors were only installed at 10, 30 and 50 cm depth.

The files follow the SMET format: https://models.slf.ch/docserver/meteoio/SMET_specifications.pdf and metadata for the stations can be found in the header of the smet files.

Please cite the Wever et al. (2017) reference when using this data in publications.

For a more detailed description, please refer to: Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053-4071, https://doi.org/10.5194/hess-21-4053-2017, 2017.

davos

display_name: davos

dischma-catchment

display_name: dischma-catchment

meteo-station

display_name: meteo-station

soil-moisture

display_name: soil-moisture

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: IRKIS Soil moisture measurements Davos


en: growing-degree-days growing-season-length relative-humidity soil-water-balance solar-radiation precipitation temperature

en: CHELSA-BIOCLIM+ Technical Documentation

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

en: CHELSA-BIOCLIM+

en: Global extented set of bioclimatic variables at different temporal (e.g. monthly, annual, long term normals) resolution in Geotiff format.

en: A multitude of physical and biological processes on which ecosystems and human societies depend are governed by climatic conditions. Understanding how these processes are altered by climate change is central to mitigation efforts. Based on mechanistically downscaled climate data, we developed a set of climate-related variables at yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We created gridded data for near-surface relative humidity (hurs), cloud area fraction (clt), near-surface wind speed (sfcWind), vapour pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), climate moisture index (cmi), and site water balance (swb), at a monthly temporal and 30 arcsec spatial resolution globally starting 1980 until 2018. At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models. Time-series variables showed high accuracy when validated against observations from meteorological stations. Climatological normals were also highly correlated to observations although some variables showed notable biases, e.g., snow cover days (scd). Together, the data sets presented here allow improving our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world’s ecosystems and associated services to societies.

growing-degree-days

display_name: growing-degree-days

growing-season-length

display_name: growing-season-length

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

soil-water-balance

display_name: soil-water-balance

solar-radiation

display_name: solar-radiation

en: CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution


en: snow measurements pore-water transport soil water

en: Descriptions of the nomenclature, Hydrus files, and videos.

en: Description of the videos

en: The Hydrus files used for the inverse fitting of the hydraulic conductivity.

en: Videos of the capillary rise experiments

en: This dataset consists of data related to capillary rise experiments performed with neutron radiography. There are 4 videos of capillary rise experiments as well as the files used to perform the inverse fitting with Hydrus. The videos show the upward flow of water in glass columns filled with sand and snow or sand, gravel, and snow. The videos show the 2D evolution of the unitless optical density with time. The Hydrus files were used to fit the parameter values of the Mualem-van Genuchten model. The experiments were performed at the Paul Scherrer Institute (PSI) in Villigen, Switzerland.

measurements

display_name: measurements

pore-water

display_name: pore-water

snow

display_name: snow

soil

display_name: soil

transport

display_name: transport

water

display_name: water

en: Capillary rise rise experiments in snow using neutron radiography


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

en: Codebook to SPSS-file

en: Questionnaire French

en: Questionnaire German

en: Dataset in csv-format

en: SPSS-Dataset

en: Questionnaire Italian

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

adults-and-teenagers

display_name: adults-and-teenagers

nfi-photos

display_name: nfi-photos

online-questionnaire-survey

display_name: online-questionnaire-survey

perception-of-ecosystem-services

display_name: perception-of-ecosystem-services

visual-attractiveness

display_name: visual-attractiveness

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


en: forest-dynamics example-applications forest species-migration forest-management land-use bioclimatic-variables spatial-modelling treemig r-package climate-change

en:
1) Simulation environment TMEnvironmentExampleApplications for the simulation of the case study applications. It contains a) simulation settings (folder C), b) species parameters (folder P), c) folders S, R and P for the state files and results and plots to be produced, and the binaries of the TreeMig model.
2) Input data for Switzerland in TMInputData (Climate, soil, land cover, DEM)
3) Several R scripts (folder Scripts) to run and plot the simulations directly from R without having to use the graphical user interface.
For learning how to use TreeMig we recommend the starter kit https://www.doi.org/10.16904/envidat.533.

en: R-script to install the TreeMig framework and start the graphical user interface

en: Where the environmental and land use input data come from and how they were processed

en: A condensed instruction for running the presented simulations.

en:
The [TreeMig framework](https://treemig.wsl.ch/en/) allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions.
Here, case study examples in Switzerland are given that simulate the dynamics and spatial spread of competing tree species in a region in Switzerland, the invasion of a hypothetical invasive tree species, and the control of this species via a management model coupled to TreeMig. The datasets consist of the installation- and GUI starting-script, example environmental input data for Switzerland, the simulation environment, and further R-scripts for running the simulations and plotting the simulation results directly from R and for coupling TreeMig to the forest management model.

bioclimatic-variables

display_name: bioclimatic-variables

climate-change

display_name: climate-change

example-applications

display_name: example-applications

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-management

display_name: forest-management

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-migration

display_name: species-migration

treemig

display_name: treemig

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

en: Case study applications demonstrating the use and potential of the TreeMig framework v1


maintainer: Andrin Caviezel

en: stonenodedata rockfall-runout natural-hazards induced-rockfall dem deposition-points rockfall sensor-stream stonenode rockfall-experiments

en: Induced Rockfall Data, Tschamut, GR

en:
 Induced Rockfall Experimental Data, Tschamut, GR, Switzerland

The archive contains site specific geographical data as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally availabe are the StoneNode v1.0 sensor streams for the equipped rocks.

The _OverviewFigures_ file serves as quick representation of the included data. It also contains the overview table for the rock masses in order to set the dimensions accordingly in RAMMS::ROCKFALL.

The archive contains the following folders:
<\ul>
* DEM: ArcGIS compatible DEM Tschamut_DEM_2m (grid resolution of 2 m) as well as the .asc grid used in RAMMS::ROCKFALL simulations
* _DepositionPoints_: containing two folders _RF Tschamut Frozen_ and _RF Tschamut WET_ containing each a shapefile with the complete deposition data of each series as well as deposition shapfiles for each rock shape.
* _DigitizedRocks_: Digitized point clouds of the used rocks as used for RAMMS::ROCKFALL simulations (.pts files) (dimensions have to be checked).
* _Orthofoto_: Orthophoto of the experimental site obtained via UAV with 5 cm resolution.

en: StoneNode v1.0 Data for equipped experimental Runs.

en: Corresponding StoneNode v1.0 data streams belonging to the deposition points.

en:
 Dataset of an experimental campaign of induced rockfall in Tschamut, Grisons, Switzerland.

The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally available are all the StoneNode data streams for rocks equipped with a sensor. The data set consists of

* Deposition points from two series (wet (27/10/2016) and frozen (08/12/2016) ground)
* Digital Elevation Model (grid resolution 2 m) obtained via UAV
* Orthophoto (5 cm resolution) obtained via UAV
* Digitized rock point clouds (.pts input files for RAMMS::ROCKFALL)
* StoneNode v1.0 raw data stream for equipped rocks.

Further information is found in

* A. Caviezel et al., _Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments_, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/
* P. Niklaus et al., _StoneNode: A low-power sensor device for induced rockfall experiments_, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/

dem

display_name: dem

deposition-points

display_name: deposition-points

induced-rockfall

display_name: induced-rockfall

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

sensor-stream

display_name: sensor-stream

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata

en: Induced Rockfall Dataset (Small Rock Experimental Campaign), Tschamut, Grisons, Switzerland


maintainer: Matthias Haeni

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

en: Site photo of the forest station (CIB)

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

mimetype: image/jpeg

en: Data plot of the field station (CIF)

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

en: Forest station data (CIB)

en: Data file.

en: Site aerial photo (Chironico)

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

en: Data plot of the forest station (CIB)

en: Site photo of the field station (CIF)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Chironico in Switzerland where one station is located within a natural coniferous forest stand (CIB) with Norway spruce (_Picea abies_; 160-180 yrs) and European silver fir (_Abies alba_; 140-160 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CIF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Chironico is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Chironico, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 2000 onwards


en: time-series nonstationarity hydrology rainfall-runoff-modelling nonlinear-deconvolution streamflow ecohydrology precipitation

en: Demonstration scripts.zip

en: Zip-compressed folder containing demonstration scripts, source data, and output files for the ERRA results shown in "Introduction to ERRA" and in J.W. Kirchner, "Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using Ensemble Rainfall-Runoff Analysis (ERRA): proof of concept", Hydrology and Earth System Sciences, 2024.

en: An introduction to ERRA.pdf

en: "An introduction to ERRA" presents a series of vignettes that introduce some of the capabilities of the Ensemble Rainfall-Runoff Analysis (ERRA) script, and that show how to invoke them. This introduction also includes documentation for ERRA function calls, largely reproducing the documentation embedded in the ERRA script itself.

en: Zip-compressed folder containing two versions of the ERRA script. ERRA_v1.0.R uses Rcpp to speed up several key calculations, but requires a C++ compiler. ERRA_v1.0_no_Rcpp.R is a bit slower but does not require a C++ compiler or Rcpp. Public use of these scripts is permitted under GNU General Public License 3 (GPL3); for details see <https://www.gnu.org/licenses/>

en:
ERRA is a data-driven, nonparametric, model-independent method for quantifying rainfall-runoff relationships across a spectrum of time lags, in systems that may be nonlinear, nonstationary, and spatially heterogeneous.
Researchers using ERRA in published work should cite J.W. Kirchner, "Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using Ensemble Rainfall-Runoff Analysis (ERRA): proof of concept", Hydrology and Earth System Sciences, 2024 (for ERRA itself) and J.W. Kirchner, "Impulse response functions for nonlinear, nonstationary, and heterogeneous systems, estimated by deconvolution and de-mixing of noisy time series", Sensors, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022 (for the underlying mathematics).
This data set includes two versions of the ERRA script written in the open-source programming language R, a detailed user's guide, and sample scripts and source data for all of the results in Kirchner (2024).
These scripts are made publicly available under GNU General Public License 3; for details see https://www.gnu.org/licenses/. The data and documentation are made available under Creative Commons Attribution Share-Alike CC-BY-SA.
ETH Zurich, WSL, and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose. Users are solely responsible for determining the suitability and reliability of this software for their own purposes.

ecohydrology

display_name: ecohydrology

hydrology

display_name: hydrology

nonlinear-deconvolution

display_name: nonlinear-deconvolution

nonstationarity

display_name: nonstationarity

rainfall-runoff-modelling

display_name: rainfall-runoff-modelling

streamflow

display_name: streamflow

time-series

display_name: time-series

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

en: ERRA -- an R script for Ensemble Rainfall-Runoff Analysis


en: numerical-simulation wrf grayzone complex-terrain cosmo-wrf

en:
This dataset contains simulation data for *"Influence of Air Flow Features on Alpine Wind Energy Potential" (Kristianti et al., 2024).*
•	The WRF_dia2702 and WRF_dia1103 folders contain the namelist.wps, namelist.input, and output files from the simulation in the Diablerets region on 27/02/2021 and 11/03/2021, respectively.
•	The WRF_luk2310 and WRF_luk0912 folders contain the namelist.wps, namelist.input, and output files from the simulation in the Lukmanier region on 23/10/2020 and 09/12/2020, respectively.
•	The postprocessing file contains Python codes used to create figures in Kristianti et al. (2024).
**Notes:**
•	Topography input is provided by Gerber et al. (2021).
•	Simulations are run using the COSMO-WRF model developed by Gerber et al. (2018).
•	Visualization by Python code uses wrf-python provided by Ladwig et al. (2017).
*This dataset was produced by the EDGE consortium sponsored by the Swiss Federal Office of Energy's SWEET programme, Swiss National Science Foundation (SNSF): Grant 179130, and the Swiss National Supercomputing Centre (CSCS) projects s938, s1115 and s1242.*

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

grayzone

display_name: grayzone

numerical-simulation

display_name: numerical-simulation

wrf

display_name: wrf

en: COSMO-WRF Dataset for Swiss Alps Simulations in Gray-Zone Resolution


en: climate-change forest-fire mountains climate climate-indicators europe indicator-values meteorological-data fire european-alps

en: Data access

en: This dataset contains the Fire Weather Index for Hydrological Bavaria from 1980 - 2099 as stated in the paper "Climate change impacts on regional fire weather in heterogeneous landscapes of Central Europe" published in Natural Hazards and Earth System Sciens (NHESS) 2023. The dataset contains daily Fire Weather Index values for all 50 members (subfolders of the dataset) of the CRCM5-LE (11 km spatial resolution) from 1980 to 2099 over the domain of Hydrological Bavaria. Please cite this dataset as the publication: Miller, J., Böhnisch, A., Ludwig, R., & Brunner, M. I. (2023). Climate change impacts on regional fire weather in heterogeneous landscapes of Central Europe. Natural Hazards and Earth System Sciences Discussions, 1-25. doi: 10.5194/nhess-2023-51.

climate

display_name: climate

climate-change

display_name: climate-change

climate-indicators

display_name: climate-indicators

europe

display_name: europe

european-alps

display_name: european-alps

fire

display_name: fire

forest-fire

display_name: forest-fire

indicator-values

display_name: indicator-values

meteorological-data

display_name: meteorological-data

mountains

display_name: mountains

en: Fire Weather Index for Hydrological Bavaria from 1980-2099 derived from the 50 member CRCM5-LE


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

en: Manually observed snow profiles

en: Manually observed snow profiles as csv-files containing depth [cm], thickness [cm], density [kg m-3], hand hardness, first grain type, second grain type, first grain size [mm], and second grain size [mm] of each layer.

en: Extracted data

en: Extracted data contain validation and results published in Richter et al. (2019).

en: Stability tests

en: Stability tests including csv-files with CT, ECT and PST experiments for each year and each field site.

en: SNOWPACK simulations

en: This dataset contains all SNOWPACK simulations, including input files, setup and output files.

en: README

en: README file explaining data structure and content

en:
To validate the critical crack length as implemented in the snow cover model SNOWPACK, PST experiments were conducted for three winter seasons (2015-2017) at two field site above Davos, Switzerland. This dataset contains manually observed snow profiles and stability tests. Furthermore, corresponding SNOWPACK simulations are included.
These data were analyzed and results were published in Richter et al. (2019).

Please refer to the Readme file for further details on the data.

These data are the basis of the following publication:
Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019.

avalanche

display_name: avalanche

avalanche-forecast

display_name: avalanche-forecast

snow

display_name: snow

snow-stability-evaluation

display_name: snow-stability-evaluation

snowpack

display_name: snowpack

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Validating and improving the critical crack length in SNOWPACK


en: hydrologic-and-hydrodynamic-modelling switzerland perialpine-lakes lake-level-regulation climate-change

en: Lake outflow scenarios CH2018

en: Lake outflow scenarios for Lake Walen, Lake Zurich, Lake Brienz and Lake Thun.

en: Lake water level scenarios CH2018

en: Lake water level scenarios for Lake Walen, Lake Zurich, Lake Brienz and Lake Thun.

en: Lake characteristics

en: Lake characteristics: stage-area relation for Lake Walen, Lake Zurich, Lake Brienz and Lake Thun, stage-discharge relations for the unregulated Lake Walen and semi-regulated Lake Brienz and, lake level management rules for the two regulated lakes Zurich and Thun.

en:
The dataset "Lake_climate_change_scenarios_CH2018" provides simulation-based climate change impact scenarios for perialpine lakes in Switzerland. These transient future scenarios were produced by combining the hydrologic model PREVAH with the hydrodynamic model MIKE11 to simulate daily lake water level (Lake_water_level_scenarios_CH2018.xls) and outflow scenarios (Lake_outflow_scenarios_CH2018.xls) from 1981 to 2099, using the Swiss Climate Change Scenarios CH2018. The future scenarios contain a total of 39 model members for three Representative Concentration Pathways, RCP2.6 (concerted mitigation efforts), RCP4.5 (limited climate mitigation) and RCP8.5 (no climate mitigation measures). These scenarios result from the study titled "Lower summer lake levels in regulated perialpine lakes, caused by climate change," authored by Wechsler et al. in 2023.

The dataset emphasizes four specific Swiss lakes, each subject to different degrees of lake level management: an unregulated lake (Lake Walen), a semi-regulated lake (Lake Brienz), and two regulated lakes (Lake Zurich and Lake Thun).

In addition, the file (Lake_characteristics.xlsx) includes data used in the modeling process, encompassing the stage-area relation for the four lakes, stage-discharge relations for the unregulated and semi-regulated lakes, and lake level management rules for the two regulated lakes.

hydrologic-and-hydrodynamic-modelling

display_name: hydrologic-and-hydrodynamic-modelling

lake-level-regulation

display_name: lake-level-regulation

perialpine-lakes

display_name: perialpine-lakes

switzerland

display_name: switzerland

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

en: Lake climate change scenarios CH2018


greenland-climate-network-gc-net-data

title_for_slug: greenland-climate-network-gc-net-data

en: automatic-weather-station air-temperature net-radiation climate aws greenland

en: Access the latest L1 data

en:
The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial version of L1 data has been archived.

en: Archived GC-Net Data (deprecated)

en:
The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial versions of L0 and L1 data have been archived.

en: GC Net Chart Parameters

media_type: application/json

en: Stations parameters from static bucket

format: JSON

en: 22-Petermann ELA

en: GC Net Config for Stations

en: Stations Config from the static bucket

en:
In Memory of Dr. Konrad (Koni) Steffen

Update October 2022: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).
Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS. Future data versions will be available at: [https://doi.org/10.22008/FK2/VVXGUT](https://doi.org/10.22008/FK2/VVXGUT)
 Background
Starting with a single station in 1991, the Greenland Climate Network (commonly known as GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen, and spanning the Greenland Ice Sheet (GrIS). This first station was "Swiss Camp" or the "ETH-CU" camp (GC-Net station 01) which was used as a field science and education site by Koni for years. The GC-Net was expanded with multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data (see "C-file" below) were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado.

 Overview
Provided in this dataset are the 16 longest running stations in the network, which are spread over a significant area of the GrIS and the majority of the unique climatic zones. From the South Dome high point in the South, to the Western Jakobshavn ablation region in the west, to the Petermann glacier in the North across east of the Northeast Greenland Ice Stream to the east, GC-Net is the longest running climatological record of Greenland.

 The standard GC-Net station consists of:
* Air temperature measurements at 2 heights above the surface
* Temperature and humidity measurements at 2 heights above the surface
* Wind speed and direction measured at 2 heights above the surface
* Sonic distance sounder measurements for 2 snow height and distance of instruments to surface
* Incoming shortwave radiation measurement
* Reflected shortwave radiation measurement
* Net broadband radiation (long- and short-wave) measurement
* Air pressure measurement
Data have often been repatriated in near-real time using one of either the GOES geostationary satellite or the ARGOS polar orbiting satellite transmission system. The stations were visited typically every 1-2 years for maintenance and service, and to download full uncorrupted data directly from the dataloggers. GC-Net stations were visited by Twin Otter equipped with snow skids to land directly on the open-ice at the AWS locations, or by helicopter near the west coast. The AWSs operate on solar and battery power and occasionally lost power during the dark and cold winter months, particularly when the batteries were aging.

 Dataset
This dataset consists of 2 main data levels; Level 0 and Level 1.
Level 0 is the raw data from the dataloggers, historical processing codes, satellite transmissions, and Koni’s personal data archive. Level 0 data (.zip) directories contain subdirectories:
* “C file” - contains the historical processed datafile for each station.
* “Campbell logger files” - contains the raw csv datafiles from the stations’ Campbell Scientific dataloggers since the CR1000 era (~2007-2008 for most stations).
* “Photos” - contains photographs of the station when available marked by year.
Level 1 is the appended, calibrated, cleaned, and quality flagged data. The full processing scheme is open-source and publicly available on the following GitHub repository (please also check GitHub for the latest L1 data):
[GC-Net L1 data on GitHub](https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing "GC-Net-level-1-data-processing")
Level 1 data is provided in the newly described csv-compatible [NEAD format](https://www.envidat.ch/#/metadata/nead "NEAD format").

 Additional Details
Dataset description publication will be forthcoming.
The Geological Survey of Denmark and Greenland (GEUS) has been imperative in the reprocessing and continuity mission of GC-Net. Multiple GC-Net stations have been replaced with updated and upgraded AWS hardware at the same coordinates by GEUS. This effort will ensure continuity of the GC-Net dataset into the future.

air-temperature

display_name: air-temperature

automatic-weather-station

display_name: automatic-weather-station

aws

display_name: aws

climate

display_name: climate

greenland

display_name: greenland

net-radiation

display_name: net-radiation

en: Greenland Climate Network (GC-Net) Data


lwf-tea-bag-sites

maintainer: Marcus Schaub

title_for_slug: lwf-tea-bag-sites

en: decomposition tea-bag roiboos modeling litter green-tea verification carbon forest

en: Site Information

en: Descriptive information on sites

en:
Decomposition of plant litter is a key process for the transfer of carbon and nutrients in ecosystems. Carbon contained in the decaying biomass is released to the atmosphere as respired CO2, and may contribute to global warming. Litterbag studies have been used to improve our knowledge of the drivers of litter decomposition, but they lack comparability because litter quality is plant species-specific. The use of commercial tea bags as a standard substrate was suggested in order to harmonize studies, where green tea and rooibos represent more labile and more recalcitrant C compounds as surrogates of local litter.
The tea bag approach was implemented on eight sites of the Swiss long-term Forest Ecosystem Research (LWF) network (https://www.wsl.ch/LWF). This allowed us to take advantage from the existing infrastructure and data from a previous litterbag study with local litter. In Beatenberg and Schaenis, additional elevation transects were established (1200-1800 m and 540-1150 m, respectively) to examine particularly the effect of temperature on decomposition. In Pfynwald (https://www.wsl.ch/de/ueber-die-wsl/versuchsanlagen-und-labors/flaechen-im-wald/pfynwald.html) and Salgesch, infrastructure of running projects was used to examine the effect of drought and understory removal, respectively. In Novaggio, tea bags were incubated in summer and winter to study the effect of seasonality particularly precipitation. Tea bags are collected after 3, 12, 24, and 36 months; for the two time-shifted experiments additionally after 6 and 9 months.
The study has two primary objectives. Firstly, it contributes to TeaComposition initiative (http://teacomposition.org/) which aims at investigating long-term litter decomposition and its key drivers at present as well as under different future climate scenarios using a common protocol and standard litter (tea) across nine terrestrial biomes. Secondly, the data are used to further develop decomposition models such as Yasso (http://en.ilmatieteenlaitos.fi/yasso) which is used by several countries, including Switzerland to estimate the annual carbon fluxes in dead wood, litter, and soil for reporting in National Greenhouse Gas Inventories under the United Nations Framework Convention on Climate Change and the Kyoto Protocol.

carbon

display_name: carbon

decomposition

display_name: decomposition

forest

display_name: forest

green-tea

display_name: green-tea

litter

display_name: litter

modeling

display_name: modeling

roiboos

display_name: roiboos

tea-bag

display_name: tea-bag

verification

display_name: verification

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: LWF-Tea bag sites


maintainer: Merin Reji Chacko

en: environmental-dna spatial-ecology biomonitoring dna-metabarcoding biodiversity-assessment

en: Analysis and Figures

en: This zipped file includes R scripts and the datasets required to run the analyses. A README describes the columns in each dataset.

en: Genomic data

en: Fastq files, primer descriptions and the final output dataset.

en: Field Metadata

en: Information concerning the locations and times of sampling, as well as the identity of the samplers and relevant comments.

en: This dataset contains the results of a five-day field excursion which the extent to which eDNA sampling can capture the diversity of a region with highly heterogeneous habitat patches across a wide elevation gradient through multiple hydrological catchments of the Swiss Alps. Using peristaltic pumps, we filtered 60 L of water at five sites per catchment for a total volume of 1 800 L. Using an eDNA metabarcoding approach focusing on vertebrates and plants, we detected 86 vertebrate taxa spanning 41 families and 263 plant taxa spanning 79 families across ten catchments. This dataset includes two sets of data. The first (Genomic data) includes all the necessary data for the bioinformatic pipeline, whereas the second (Analysis Figures) contains tidied data and scripts for the reproduction of all figures/analyses in the article describing this study.

biodiversity-assessment

display_name: biodiversity-assessment

biomonitoring

display_name: biomonitoring

dna-metabarcoding

display_name: dna-metabarcoding

environmental-dna

display_name: environmental-dna

spatial-ecology

display_name: spatial-ecology

envidat

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

en: Vertebrate and plant taxa recovered from 10 catchments in Vaud using an eDNA-metabarcoding approach


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

en: Site aerial photo (Vordemwald)

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

en: Data plot of the field station (VOF)

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

en: Site photo of the forest station (VOB)

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

en: Data plot of the forest station (VOB)

en: Site photo of the field station (VOF)

en: Site photo of the field stationfrom the side.

en: Field station data (VOF)

en: Data file.

en: Forest station data (VOB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Vordemwald in Switzerland where one station is located within a natural mixed forest stand (VOB) with European silver fir (_Abies alba_; 110 yrs) and oak trees (_Quercus sp._; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Vordemwald is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


en: biomass-dynamics soil-nutrients climate-change treeline-ecosystems plant-community plant-traits

en: Data on soils, tree biomass, plant nutrients, rock mineralogy, climate of treeline ecosystems in Khibiny mountains, Kola Penninsula, Russia

en: Data on soil nutrients and charcteristics, tree and understory biomass, foliage nutrients, mineralogy, soil temperature and moisture along an elevation gradient from boreal forest to mountain tundra in teh Khibiny mountains (Kola Penninsula, Russia). Samples were taken along two gradients at 7 elevation level either under tree canopy (tree) and in open areas (open).

biomass-dynamics

display_name: biomass-dynamics

plant-community

display_name: plant-community

plant-traits

display_name: plant-traits

soil-nutrients

display_name: soil-nutrients

treeline-ecosystems

display_name: treeline-ecosystems

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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