en: This dataset includes the diversity of soil prokaryotes, fungi and micro-eukaryotes in unmanaged forest reserves with managed forests across nine locations within Switzerland. Forests have been exploited for resources for centuries, which has caused disturbances, altered community structure, and reduced diversity at different scales. Strict forest reserves, where no forest management is practiced, have been implemented to promote biodiversity, by allowing old trees with microhabitats and high deadwood amounts. However, the effectiveness of forest reserves on soil microbial diversity and functional traits are little known at a national scale in Swiss forests. The associated metadata includes local environmental conditions, such as soil pH, organic C, N, deadwood volume, MAT and MAP, and forest type (coniferous vs deciduous).

en: Management practices shape soil fungal diversity and functional traits in Swiss forests

en: prokaryotes forest-management microeukaryotes fungi soil-biodiversity

en:
Resource Title: Prokaryotes
    Size: 2738041651 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.780%2FForestMan_SoilBD_Prokaryotes%20%2816S%29%2F
    Description:

en:
Resource Title: Environmental data
    Size: 13926 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/04e9459c-7024-46c3-9c33-a50a33da55b2/resource/c27f01d0-217e-4b50-88c0-70421ec0334f/download/forest-management-soilbd-environmental-data-envidat.csv
    Description: Environmental Data such as soil abiotic properties

en:
Resource Title: Microeukaryotes
    Size: 2512555868 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.780%2FForestMan_SoilBD_Microeukaryotes%20%2818S%29%2F
    Description:

en:
Resource Title: Fungi
    Size: 2931315180 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.780%2FForestMan_SoilBD_Fungi%20%28ITS%29%2F
    Description:

forest-management

display_name: forest-management

fungi

display_name: fungi

microeukaryotes

display_name: microeukaryotes

prokaryotes

display_name: prokaryotes

soil-biodiversity

display_name: soil-biodiversity


$oid: OBSOLETE

en: This dataset comprises qualitative data for the development of the five Swiss Shared Socioeconomic Pathways (SSP-CH) up to the year 2100. First, it comprises projections of 22 socioeconomic factors, each represented by four to five factor-specific trajectories that form the basis of the qualitative scenario storylines. Second, it includes an input table providing concise qualitative characterizations of 63 socioeconomic factors across the five SSP-CH scenarios.

en: Shared Socioeconomic Pathways for Switzerland (SSP-CH): qualitative data for scenario development

en: ssp-storylines scenario-development qualitative-scenarios national-ssps socioeconomic-scenarios

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

en:
Resource Title: Input-Tabelle_final_d.xlsx
    Size: 198502 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/3e5310e5-4038-4782-b3c9-b64100709e32/resource/3458c684-6a22-437e-99cf-20c9f494d521/download/input-tabelle_final_d.xlsx
    Description: SSP-CH Input table: qualitative description of 63 socioeconomic factors in the 5 SSP-CH (german). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Programme. The following individuals participated in the compilation of this data: Gubler, L., Tschumi, P., Pütz, M., Seidl, I.

format: XLS

en:
Resource Title: Input-table_final_e.xlsx
    Size: 45599 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/3e5310e5-4038-4782-b3c9-b64100709e32/resource/9eef7ee5-8a6d-47c2-9952-447cd39018e4/download/input-table_final_e.xlsx
    Description: SSP-CH Input table: qualitative description of 63 socioeconomic factors in the 5 SSP-CH (english). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Programme. The following individuals participated in the compilation of this data: Gubler, L., Tschumi, P., Pütz, M., Seidl, I.

en:
Resource Title: SSP-CH_Faktorprojektionen_d.pdf
    Size: 3030385 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/3e5310e5-4038-4782-b3c9-b64100709e32/resource/11d2b1ab-8a77-407e-9732-db22a853e03f/download/faktorprojektionen_beschreibung_d.pdf
    Description: SSP-CH factor projections of 20 socioeconomic factors (german). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Programme. The following individuals participated in the compilation of this data: Gubler, L., Tschumi, P., Pütz, M., Seidl, I.

format: PDF

media_type: application/pdf

national-ssps

display_name: national-ssps

qualitative-scenarios

display_name: qualitative-scenarios

scenario-development

display_name: scenario-development

socioeconomic-scenarios

display_name: socioeconomic-scenarios

ssp-storylines

display_name: ssp-storylines


en:
This dataset contains the results of greenhouse gas emission modeling for the five Swiss Shared Socioeconomic Pathways (SSP-CH) up to the year 2100. Six sector-specific reduced-complexity models were developed specifically for this analysis, enabling the modelling of the SSP-CH storylines. Accordingly, the dataset comprises the outputs of all six models, generated both in the absence of and under the implementation of additional climate policy packages, referred to as Shared Policy Assumptions (SPAs; see related publications).
- Transport model (energy consumption by energy source and transport type/fleet mix)
- Housing model (heating demand and GHG emissions per building category)
- Electricity model (electricity consumption, electricity generation by energy source, installed capacity, district heating)
- Industry model (energy consumption by energy source, subdivided into raw materials/building materials, consumer goods, and processed goods)
- Agriculture model (feed digestion in livestock, manure storage, and nitrogen fertilization)
- Export/Import model (product quantities in import and export, broken down into consumer goods, raw materials/building materials, and processed goods)
- Synthesis model

en: Results of greenhouse gas modelling of the Swiss Shared Socioeconomic Pathways (SSP-CH)

en: socioeconomic-scenarios emissions-scenarios qualitative-scenarios greenhousegas-modelling national-ssp

en:
Resource Title: SSP-CH_GHG_transport_model.xlsx
    Size: 9070182 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/be82872b-5fd7-4498-a4b9-2141dd5eb00f/download/ssp-ch_ghg_transport_model.xlsx
    Description: SSP-CH GHG modelling results of Transport model (energy consumption by energy source and transport type/fleet mix). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_industry_model.xlsx
    Size: 465818 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/3e81b975-33bf-4327-bf18-3ae32f36b21d/download/ssp-ch_ghg_industry_model.xlsx
    Description: SSP-CH GHG modelling results of Industry model (energy consumption by energy source, subdivided into raw materials/building materials, consumer goods, and processed goods). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_Synthesis_model.xlsx
    Size: 386540 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/a1e54990-11c5-43cf-b922-d839925b4078/download/ssp-ch_ghg_synthesis_model.xlsx
    Description: SSP-CH GHG modelling results of Synthesis model. This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_agriculture_model.xlsx
    Size: 624865 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/b8d73680-8159-46ac-98f6-9d97131f5c2e/download/ssp-ch_ghg_agriculture_model.xlsx
    Description: SSP-CH GHG modelling results of Agriculture model (feed digestion in livestock, manure storage, and nitrogen fertilization). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_imp_exp_model.xlsx
    Size: 11240735 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/c80f618c-fd87-4c43-920e-77c8081ccd5a/download/ssp-ch_ghg_imp_exp_model.xlsx
    Description: SSP-CH GHG modelling resluts of Export/Import model (product quantities in import and export, broken down into consumer goods, raw materials/building materials, and processed goods). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_housing_model.xlsx
    Size: 3051356 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/913c1ae0-418b-4f31-8b51-9ef1e364b3fd/download/ssp-ch_ghg_housing_model.xlsx
    Description: SSP-CH GHG modelling results of Housing model (heating demand and GHG emissions per building category). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_transport_model.xlsx
    Size: 9070182 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/c147c40f-3431-4847-a954-5a80b6a38cf1/download/ssp-ch_ghg_transport_model.xlsx
    Description: This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_electricity_model.xlsx
    Size: 1195377 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/b543e472-b1c0-488c-8a71-ff09be0d140d/download/ssp-ch_ghg_electricity_model.xlsx
    Description: SSP-CH GHG modelling results of Electricity model (electricity consumption, electricity generation by energy source, installed capacity, district heating). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

emissions-scenarios

display_name: emissions-scenarios

greenhousegas-modelling

display_name: greenhousegas-modelling

national-ssp

display_name: national-ssp


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

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

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

envidat

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

en:
Resource Title: TreeMig_Tutorial_v2024_12.pdf
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/5188a2e1-470c-4ed7-83c6-26dc72bd18ab/download/treemig_tutorial_v2024_12.pdf
    Description: Tutorial for using the TreeMig framework; updated

en:
Resource Title: Description input data
    Size: 263110 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/3db8ca57-2838-4b26-82a9-9770b75b7096/download/readme_descriptioninputdata.pdf
    Description: Details where the input data come from and how they have been processed

en:
Resource Title: TreeMig Tutorial
    Size: 2046576 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/8e6ac324-ab2b-40bc-a12f-70960c8f4bd1/download/treemig_tutorial.pdf
    Description: Tutorial for using the TreeMig framework

en:
Resource Title: TreeMig Simulation Environment and Input Data
    Size: 1612103870 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/5eef16d1-80af-442b-9da2-f64b4fa94671/download/tmenvandinputdata.zip
    Description: The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data.  For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders.  For the first example simulation of the tutorial it contains the
   - the TreeMig executables for MacOS, Linux and Windows
   - simulation control files that define the setup and input files of the simulation (in folder C)
   - species parameters (in folder P)
   - bioclimatic data  (in folder E)
   - folder R for results
   - folder Plots for plots of the simulation results.
   - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

format: ZIP

media_type: application/zip

en:
Resource Title: TreeMig_Tutorial_v2024_12_04.pdf
    Size: 6400462 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/d4c15251-917c-4102-b172-256fecd39161/download/treemig_tutorial_v2024_12_04.pdf
    Description:

en:
Resource Title: TreeMig_Tutorial_2025-05-02.pdf
    Size: 2107648 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/a1dec65a-30ba-4688-90ac-50df5db73d4c/download/treemig_tutorial_2025-05-02.pdf
    Description: Tutorial for using the TreeMig framework; updated

en:
Resource Title: TreeMig Simulation Environment and Input Data
    Size: 1602386904 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/c2ae78fd-418a-4cdb-a511-0914eb46224f/download/tmenvandinputdata.zip
    Description: The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data.  For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders.  For the first example simulation of the tutorial it contains the
   - the TreeMig executables for MacOS, Linux and Windows
   - simulation control files that define the setup and input files of the simulation (in folder C)
   - species parameters (in folder P)
   - bioclimatic data  (in folder E)
   - folder R for results
   - folder Plots for plots of the simulation results.
   - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

bioclimatic-variables

display_name: bioclimatic-variables

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-model

display_name: forest-model

gui

display_name: gui

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-interaction

display_name: species-interaction

species-migration

display_name: species-migration

treemig

display_name: treemig


en:
Spatio-temporal vegetation models are essential for simulating dynamics and shifts
in species distributions over large areas, particularly under global change. One such
model is the dynamic forest landscape model TreeMig. We have developed a frame-
work that makes the model more user-friendly, improves its functionality, and expands
its possibilities. The framework consists of the improved TreeMig model, the R pack-
age ‘TreeMig-R’ for pre-processing input data, model execution and visualization and a
graphical user interface (GUI) for ease of use. The code of the TreeMig Package and the GUI is stored here.

en: Code of TreeMig Package version 1

en: forest-model landscape gui dynamic-modelling r-package

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

dynamic-modelling

display_name: dynamic-modelling

landscape

display_name: landscape


en:
We used 1463 observations of early leaf discoloration presence or absence in Switzerland's European beech (*Fagus sylvatica L.*) forests between 2018 and 2023 and interpolated Sentinel-2 vegetation index time series from Koch et al. (2024) to train a support vector classifier of early leaf discoloration. We applied this classifier to the European beech dominated forest area of Switzerland over the same time period to estimate a probability of early leaf discoloration between 0 and 1 (high probability of early leaf discoloration). The maps intentionally exclude legacy effects of drought like crown dieback or mortality which occurred in prior years.
Early leaf discoloration maps are available at the end of every summer month (June, July, August, and September) 2017 to 2023. Additional maps are available every 5-days during the summer months of 2018. Probability of early leaf discoloration at each date is provided as a GeoTIFF file with a scaled value range of 0 to 10,000 such that a probability of 0.9 is represented by a value of 9000 (a high probability of early leaf discoloration). An optimized probability threshold of 0.4 (4000) is recommended for classification into discolored and not discolored pixels. More details on the methods and results can be found in the publication of Bloom et al. (2026).
***These maps represent an interpreted remote sensing product that approximates ground observations of early leaf discoloration across Switzerland's European beech dominated forests.***
Given ongoing efforts to operationalize an early discoloration pipeline, all data is currently available upon reasonable request. An example of the 2018 5-day time series in the Hardwald (Canton Basel-Land) is available for immediate download.

en: Early leaf discoloration maps for Switzerland's European beech forests

en: drought early-warning switzerland sentinel-2 european-beech early-defoliation forest drought-2018

drought

display_name: drought

early-defoliation

display_name: early-defoliation

early-warning

display_name: early-warning

european-beech

display_name: european-beech

switzerland

display_name: switzerland


en:
The TreeMig framework 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.

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

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

en:
Resource Title: InstructionsForRunningTheSimulations.pdf
    Size: 127302 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/32c74611-c61e-48be-998b-6722bbb697c8/download/instructionsforrunningthesimulations.pdf
    Description: A condensed instruction for running the presented simulations.

en:
Resource Title: ExampleApplicationsTreeMig.zip
    Size: 1686005000 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/0f59997f-3e90-4b25-9e5b-37b641d9769b/download/exampleapplicationstreemig.zip
    Description: 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:
Resource Title: ExampleApplicationsTreeMig.zip
    Size: 1676301558 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/609e85c8-98d0-4043-be91-48bd32c162ea/download/exampleapplicationstreemig.zip
    Description: 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:
Resource Title: readme_descriptioninputdata.pdf
    Size: 263110 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/5abbe67b-ab73-413b-bff8-5cccf5347b1c/download/readme_descriptioninputdata.pdf
    Description:

en:
Resource Title: installAndRunTreeMigWrapperAndGUI_std.R
    Size: 553 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/916c8a50-1d57-4e28-97c8-f50b8a9f7bf7/download/installandruntreemigwrapperandgui_std.r
    Description: R-script to install the TreeMig framework and start the graphical user interface

en:
Resource Title: InstructionsForRunningTheSimulations.pdf
    Size: 68995 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/830a45db-7985-42f6-8864-4961fe5ef5a2/download/instructionsforrunningthesimulations.pdf
    Description: A condensed instruction for running the presented simulations.

bioclimatic-variables

display_name: bioclimatic-variables

climate-change

display_name: climate-change

example-applications

display_name: example-applications

forest

display_name: forest

forest-management

display_name: forest-management


en:
Regional disturbance predisposition maps for windthrow, bark beetle and snow breakage. The maps are created using spatially explicit data of classified forest structure and site factor parameters. The classified input parameters were weighted according to their effect on windthrow, bark beetle and snow breakage predisposition using an expert-based model. The predisposition raster maps (10 x 10 m) represent the sum of expert-weighted effects on the predisposition and are provided as raw values (sum of the effects on predisposition) and as classified layers.
Classified predisposition maps are encoded as follows:
1: low predisposition (-50% quantile based on raw predisposition values)
2: increased predisposition (50–75% quantile)
3: high predisposition (-75–95% quantile)
4: extreme predisposition (-95% quantile)
Also included are the derived forest structure maps for development stage, dominant stand height, number of stems and canopy cover used for the predisposition mapping, as well as the R scripts and example data to re-create these parameters. Forest structure parameters are based on a data set of individual trees detected using a high-resolution vegetation height model. Further included are ArcPy scripts and example data to re-create the predisposition maps. Running the ArcPy scripts requires a valid ArcGis license including the extensions "SpatialAnalyst", "ImageAnalyst" and "3D".
The example data provided here is assembled from different sources; please check the related publication Bührle et al. (in review) for data sources and further information about the processing, the expert-based model, the forest structure derivation and the predisposition mapping.
Also consider reviewing the related datasets and publications Bast et al. (2025) for more information about canopy layering derivation and individual tree detection (ITD).

en: Regional bark beetle windthrow and snow breakage disturbance predisposition maps

en: development-stage tree-detection itd windthrow predisposition-mapping canopy-cover forest-structure als stand-height stem-number snow-breakage bark-beetle predisposition

envidat

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

en:
Resource Title: 01_predispostion_maps.zip
    Size: 19881001 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/59df8818-2e56-4794-9152-3652c20fe6fc/download/01_predispostion_maps.zip
    Description: Classified and raw value predisposition maps for bark beetle, windthrow and snow breakage

en:
Resource Title: 04_forest_structure_maps.zip
    Size: 34068234 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/cd162993-bc30-46c1-816b-0722c3d83a50/download/04_forest_structure_map.zip
    Description: Maps of development stage, number of stems, dominant stand height and canopy coverage based on single trees detected from a vegetation height model.

en:
Resource Title: 02_python_scripts_predisposition_mapping.zip
    Size: 438323 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/e165fc12-ca86-423f-807a-d7cf1c8535a5/download/02_python_scripts_predisposition_mapping.zip
    Description: ArcPy-Python scripts and demo data to create the bark beetle, windthrow and snow breakage predisposition maps.

en:
Resource Title: 03_rcode_forest_structure.zip
    Size: 103693681 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/42199442-7c55-48ed-a3a7-04aaf9967da0/download/03_rcode_forest_structure.zip
    Description: R-codes and demo data to derive the forest structure parameters: development stage, number of stems, dominant stand height, and canopy coverage based on single trees detected from a vegetation height model. These parameters are necessary input parameters for predisposition mapping.

als

display_name: als

bark-beetle

display_name: bark-beetle

canopy-cover

display_name: canopy-cover

development-stage

display_name: development-stage

forest-structure

display_name: forest-structure

itd

display_name: itd

predisposition

display_name: predisposition

predisposition-mapping

display_name: predisposition-mapping

snow-breakage

display_name: snow-breakage

stand-height

display_name: stand-height

stem-number

display_name: stem-number

tree-detection

display_name: tree-detection

windthrow

display_name: windthrow


en:
Phenology, growth, NSC and Amax data used for the following paper:
Early-season warming advances autumn senescence in temperate tree saplings in association with early growth dynamics  published in Plant Cell & Environment 2026.

en: Data for the paper: Early-season warming advances autumn senescence in temperate tree saplings in association with early growth dynamics

en: nsc tree-phenology growth warming-experiment leaf-senescence

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: ReadMe_Data information.docx
    Size: 17668 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/90ccc75b-bc5e-4968-92a2-38d3452341a8/resource/28146b13-81fd-4625-bc7a-6f55ee39796d/download/readme_data-information.docx
    Description:

en:
Resource Title: Data.zip
    Size: 42598 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/90ccc75b-bc5e-4968-92a2-38d3452341a8/resource/6a336540-7ba5-471e-b452-db20bc540b6a/download/data.zip
    Description: Data used in the paper published in Plant Cell & Environment Vitasse et al. 2026

growth

display_name: growth

leaf-senescence

display_name: leaf-senescence

nsc

display_name: nsc

tree-phenology

display_name: tree-phenology

warming-experiment

display_name: warming-experiment


en:
Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.
Data are provided on request to contact person against bilateral agreement.

en: Ecological properties of urban ecosystems. Biodiversity dataset of Zurich

en: arthropods diversity sdms snails insects spiders species-abundance-distributions urbanization urban-ecology species-richness-models birds

en:
Resource Title: Dataset_3_Species_richness_hyper.txt
    Size: 1373635 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/0ef3b508-b8e7-4404-a7b9-edc51c7f2375/download/dataset_3_species_richness_hyper.txt
    Description: contains the raw data to perform the species richness models of the hypercommon species

format: TXT

media_type: text/plain

en:
Resource Title: Dataset_5_Species_richness_all.txt
    Size: 2453668 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/f3abc8d4-c92e-4a57-aea4-39ba8c93158d/download/dataset_5_species_richness_all.txt
    Description: contains the raw data to perform the species richness models of all species

en:
Resource Title: Dataset_1_SAD_SOD.txt
    Size: 73482 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/a7a96a85-dc46-4b5f-bcf4-4d91485e3d03/download/dataset_1_sad_sod.txt
    Description: abundance and occurrence data of the 1446 species studied, used to investigate species-abundance distributions and species-occurrence distributions

en:
Resource Title: Dataset_2_SDM_data.txt
    Size: 65682801 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/0e2e7fb0-88ba-4aa4-92ef-079191b4a668/download/dataset_2_sdm_data.txt
    Description: contains the raw data to perform species distribution models, only for common species

en:
Resource Title: Dataset_4_Species_richness_rare.txt
    Size: 2275410 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/3bbbc78a-92c3-4832-b51b-61d65346460e/download/dataset_4_species_richness_rare.txt
    Description: contains the raw data to perform the species richness models of the rare species

arthropods

display_name: arthropods

birds

display_name: birds

diversity

display_name: diversity

insects

display_name: insects

sdms

display_name: sdms

snails

display_name: snails

species-abundance-distributions

display_name: species-abundance-distributions

species-richness-models

display_name: species-richness-models

spiders

display_name: spiders

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization


en: This dataset contains R scripts and pre-processed files to reconstruct the analyses made in the technical note submitted to Ecology Letters as a response to Li & He (2026, https://doi.org/10.1111/ele.70326). In short, I show that the claims made by LH26 (variation in tree growth has increased due to global warming, and that the power exponent between growth variance and mean is b=1.4) are questionable. I show that the reported variance increase in ring widths is an artefact caused by division-based detrending. The reported exponent of 1.4 reflects systematic underestimation, not biological reality of a steep variance-mean scaling. The ecological interpretations built on these foundations cannot be considered reliable.

en: No increased tree growth variability due to global warming

en: estimation-methods climate-change tree-rings variability global-warming

envidat

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

en:
Resource Title: Diagnostic plots.zip
    Size: 279812506 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b1e75fa6-f232-45af-a524-d2088ff2757b/resource/e51b22cb-ff0f-40cb-b302-bfc24e2a3a48/download/diagnostic-plots.zip
    Description: Diagnostic plots presented in SM1.

Outlier_plots_negexp contains the outlier screening where the arithmetic mean at the population levels creates much larger values than the robust mean.

varmax_diagnostic_pages contains the outlier screening for running 5-year variance >5.

en:
Resource Title: preprocessed data.zip
    Size: 3199750636 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b1e75fa6-f232-45af-a524-d2088ff2757b/resource/3ada643d-214e-4573-a4d6-3c49736f5e3f/download/preprocessed-data.zip
    Description: pre-processed data produced in the accompanying R scripts.

en:
Resource Title: R scripts.zip
    Size: 24412 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b1e75fa6-f232-45af-a524-d2088ff2757b/resource/15fb0bd5-ad52-4cf3-ad52-3573063232d2/download/r-scripts.zip
    Description: R scripts to reconstruct analyses made in technical note.

mean_variance_ITRDB_SM1: SM1, calculation of running variances, calculation of outliers, diagnostic plots
power_estimate_clean_simulation: Simulation for SM5
fair_power_comparison_persample: power estimation per 50-year period per sample, SM3
fair_power_comparison: population power estimation per 50-year period, SM3
powertest3.1: Simulation to estimate power given the shape of the ITRDB raw ring-width data, SM4
different power exponent estimations: SM4

climate-change

display_name: climate-change

estimation-methods

display_name: estimation-methods

global-warming

display_name: global-warming

tree-rings

display_name: tree-rings

variability

display_name: variability


en:
Jikradia olitoria Say is a Nearctic leafhopper that was recently identified in several European countries. Given its role as the vector of phytoplasmas associated with Grapevine Yellows diseases (GYD) in its native range, its presence in European vineyard agroecosystems could represent an important phytosanitary risk.
Systematic monitoring conducted over two consecutive years (i.e., 2023 and 2024) using chromotropic traps in seven sites allowed to confirm its widespread presence in the southern Alps, both inside vineyards as well as in the nearby forest edge compartment. Moreover, a subset of the collected specimens was analyzed to test potential infection by phytoplasmas associated with Flavescence dorée.

en: First detailed record of Jikradia olitoria (Hemiptera: Cicadellidae) in the Swiss southern Alps and evaluation of its current potential as a vector of 16SrV-phytoplasmas

en: flavescence-doree nearctic-leafhopper grapevine-yellows epidemiology neobiota

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: Table2 - Jikradia olitoria hatching events
    Size: 15278 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/7bf9a0ee-2111-4144-8f11-f4f35784b74c/download/table2.docx
    Description: Jikradia olitoria hatching events divided by year and wood type. For the hatching experiment, wood was collected from the Giornico site due to the large J. olitoria adult population observed in 2023.

en:
Resource Title: Supplementary_table3 - Hatching events
    Size: 15852 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/588d922d-1acd-4eab-9693-c76d4cf7d9a6/download/supplementary_table3.xlsx
    Description: Hatching events in 2024 and 2025.

en:
Resource Title: Supplementary_table2 - Trap captures 2024
    Size: 165550 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/bba02f83-22d2-48d3-8d60-39e03be1cfb6/download/supplementary_table2.xlsx
    Description: Trap captures in 2024.

en:
Resource Title: Supplementary_table1 - Trap captures 2023
    Size: 144783 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/8c89de75-a018-44c1-b913-346b1dbc5356/download/supplementary_table1.xlsx
    Description: Trap captures in 2023.

en:
Resource Title: Supplementary Figure 1
    Size: 13233029 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/0b456ac4-2091-4836-9b36-065b5fa39f55/download/figure_s1.tiff
    Description: Jikradia olitoria captures on yellow sticky traps placed inside the vineyard canopy in the years 2023 and 2024.

format: TIFF

media_type: image/tiff

en:
Resource Title: Table 3 - Insect vectors of 'Candidatus Phytoplasma vitis' associated to grapevine’s Flavescence dorée in Europe
    Size: 21135 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/7b134d20-6f86-41d1-bca1-3610fdf02b07/download/table-3.docx
    Description: Insect vectors of 'Candidatus Phytoplasma vitis' associated to grapevine’s Flavescence dorée in Europe. Insect species categorized according to their vector competence, i.e., main vector: primary epidemic grapevine-to-grapevine vector; alternative vector: FD phytoplasma acquisition and transmission confirmed in laboratory and supported by data collected in the field and usually involving other plant species; putative vector: only FDp and/or FDp-like genotypes acquisition confirmed, no indication of ability to transmit FDp to receipient plant. Associated plant species involved in alternative FDp cycle do not represent the complete and known range of host plant species for the listed insect species. Only major and epidemic map-type genotypes are listed. For further details on map-type gene and grouping (i.e., FD1, FD2, and FD3) and vectotype related gene VmpA, refer to Malembic-Maher et al. (2020).

en:
Resource Title: Table1 - Jikradia olitoria total captures
    Size: 21647 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/05806ade-fd08-4bfa-b2bb-a0991dec3377/download/table1.docx
    Description: Jikradia olitoria total captures divided by plot and trap type, mean captures per trap and ratio between the two sampling years. YST/plot = yellow sticky traps installed in each plot, L = landscape YST, V = vineyard YST, N YST = overall number of processed YST throughout the experiment.

en:
Resource Title: Supplementary Figure 2
    Size: 13233029 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/41f7fb05-1fac-4ef1-adc6-7362059d972d/download/figure_s2.tiff
    Description: Jikradia olitoria captures on yellow sticky traps placed in the landscape around the vineyards in the years 2023 and 2024.

epidemiology

display_name: epidemiology

flavescence-doree

display_name: flavescence-doree

grapevine-yellows

display_name: grapevine-yellows

nearctic-leafhopper

display_name: nearctic-leafhopper

neobiota

display_name: neobiota


en: This repository contains plant community data collected at 180 sites across five types of urban green spaces (allotments, gardens, green areas in residential estates, parks, and ruderal sites) in three Swiss cities (Lugano, Geneva, Zurich). There are 12 sites per UGS type and city (i.e., 60 sites per city). Plant communities were sampled in 100 m2 plots, and coverage was recorded using the extended Braun-Blanquet scale. The data is part of the SNF Synergia project PAPPUS.

en: Data on the plant composition in 5 urban green space types and 3 Swiss Cities (project PAPPUS)

en: urban-greenspaces origin-status urban-plants ubran-biodiversity human-preferences

envidat

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

en:
Resource Title: Non-native plant community
    Size: 183641 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb645df-e8d4-4887-93c4-d91a24afdea5/resource/b0270e86-5580-4b99-8f94-a903178db451/download/community_matrix_nonnative_f.csv
    Description: The dataset contains the non-native plant communities  sampled in the 180 study sites. Rows are sites and columns are plant species. Cells contain the numerical values of the extended Braun-Blanquet coverage classes (e.g., https://doi.org/10.1658/1100-9233(2007)18[413:HRITMO]2.0.CO;2). Native species are considered as those whose natural distribution ranges are outside continental Europe, following (https://doi.org/10.1016/j.dib.2021.107243). They include invasive species and exotics.

Sites IDs are composed by City_UGStype + Values orthogonal gradient + replicate number:

    City: 3 levels, GE = Geneva, ZH= Zurich, LG = Lugano
    UGStype: 5 levels, Al = Allotments, Ga = private gardens, Es = green areas in residential estates, Pa = parks, Ru = ruderal sites.
    Orthogonal gradient: Landscape urbanization class + Local structural diversity class. 4 combinations: HH, HL, LH, LL. H =high, L = Low
    Replicate, numeric. 1-3

format: CSV

media_type: text/csv

en:
Resource Title: Full plant community
    Size: 449744 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb645df-e8d4-4887-93c4-d91a24afdea5/resource/70681117-86df-43b9-abf9-870d027eb8af/download/community_matrix.csv
    Description: The dataset contains the full plant communities (including both native and non-native species) sampled in the 180 study sites. Rows are sites and columns are plant species. Cells contain the numerical values of the extended Braun-Blanquet coverage classes (e.g., https://doi.org/10.1658/1100-9233(2007)18[413:HRITMO]2.0.CO;2).

Sites IDs are composed by City_UGStype + Values orthogonal gradient + replicate number:
- City: 3 levels, GE = Geneva, ZH= Zurich, LG = Lugano
- UGStype: 5 levels, Al = Allotments, Ga = private gardens, Es = green areas in residential estates, Pa = parks, Ru = ruderal sites.
- Orthogonal gradient: Landscape urbanization class + Local structural diversity class.  4 combinations: HH, HL, LH, LL.  H =high, L = Low
- Replicate, numeric. 1-3

en:
Resource Title: Native plant community
    Size: 262862 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb645df-e8d4-4887-93c4-d91a24afdea5/resource/ca98f7fd-5e48-4ffc-b5cd-b8a9b6fd4b15/download/community_matrix_native_f.csv
    Description: The dataset contains the native plant communities  sampled in the 180 study sites. Rows are sites and columns are plant species. Cells contain the numerical values of the extended Braun-Blanquet coverage classes (e.g., https://doi.org/10.1658/1100-9233(2007)18[413:HRITMO]2.0.CO;2). Native species are considered as thosw whose natural distribution ranges are within continental Europe, following (https://doi.org/10.1016/j.dib.2021.107243)

Sites IDs are composed by City_UGStype + Values orthogonal gradient + replicate number:

    City: 3 levels, GE = Geneva, ZH= Zurich, LG = Lugano
    UGStype: 5 levels, Al = Allotments, Ga = private gardens, Es = green areas in residential estates, Pa = parks, Ru = ruderal sites.
    Orthogonal gradient: Landscape urbanization class + Local structural diversity class. 4 combinations: HH, HL, LH, LL. H =high, L = Low
    Replicate, numeric. 1-3

human-preferences

display_name: human-preferences

origin-status

display_name: origin-status

ubran-biodiversity

display_name: ubran-biodiversity

urban-greenspaces

display_name: urban-greenspaces

urban-plants

display_name: urban-plants


en:
This dataset supports the analysis presented in "From point clouds to forest management: Quantifying the sensitivity of a decision support framework to initialization data using close-range remote sensing" (https://doi.org/10.1016/j.ecoinf.2026.103896). The repository covers two parts of the analysis: (1) data preparation from LiDAR-based forest inventories, and (2) sensitivity analysis of decision support system (DSS) outputs. The intermediate forest simulation (ForClim v4.1) and multi-criteria decision analysis (MCDA) were carried out using an external pipeline described in Blattert et al. (2024), using the R package rforclim (v1.01.001) by Hiltner & Marano (2025). The corresponding model outputs are included here as inputs to the sensitivity analysis, making all analyses in this repository fully reproducible without requiring access to the simulation pipeline. The ForClim model (v4.1) (Marano et al., 2025) is openly available from the ETH Zurich Institute of Terrestrial Ecosystems (https://ites-fe.ethz.ch/openaccess/products/forclim). Climate data were sourced from the publicly available Swiss CH2018 dataset (CH2018 Project Team, 2018). Soil data were sourced from Baltensweiler et al. (2024). For a detailed description of the repository structure, scripts, and data files, see the README file included in this dataset.
 References
Blattert C, Mutterer S, Thrippleton T, Diaci J, Fidej G, Bont LG, Schweier J (2024). Managing European Alpine forests with close-to-nature forestry to improve climate change mitigation and multifunctionality. Ecological Indicators, 165, 112154. https://doi.org/10.1016/j.ecolind.2024.112154
Baltensweiler A, Walthert L, Hanewinkel M, Zimmermann S, Nussbaum M (2024). Soil property maps for the Swiss forest. EnviDat. https://doi.org/10.16904/envidat.484
CH2018 Project Team (2018). CH2018 - Climate Scenarios for Switzerland. National Centre for Climate Services, Zurich. https://www.nccs.admin.ch/nccs/en/home/climate-change-and-impacts/swiss-climate-change-scenarios/ch2018---climate-scenarios-for-switzerland.html
Marano, G., Hiltner, U., Meusburger, K., Hands, T., & Bugmann, H. (2025). Predicting drought-induced tree mortality in Swiss beech forests hinges upon predisposing and inciting factors (p. 2025.12.15.694299). bioRxiv. https://doi.org/10.64898/2025.12.15.694299
Hiltner U, Marano G (2025). rforclim: Collection of useful functions to parameterize and run the forest model ForClim. R package version 1.01.001. Available on request from the ETH Zurich Institute of Terrestrial Ecosystems.

en: decision-support forest-model sensitivity-analysis forest-inventory lidar ecosystem-services uncertainty

envidat

en: EnviDat: the Environmental Data Portal

en:
Resource Title: dss_sensitivity_analysis_envidat.zip
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    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.784%2F
    Description:

en:
Resource Title: README.html
    Size: 23631 bytes
    Format: text/html
    URL: https://www.envidat.ch/dataset/6b3838ed-8433-492d-8424-2a19ae134b0a/resource/b331ae91-9d28-4262-9edd-9efeb9f2fc12/download/readme.html
    Description:

format: HTML

media_type: text/html

en:
Resource Title: README.md
    Size: 4149 bytes
    Format: text/markdown
    URL: https://www.envidat.ch/dataset/6b3838ed-8433-492d-8424-2a19ae134b0a/resource/eaa7ed36-52f8-47fc-827c-a54a71292a4a/download/readme.md
    Description:

format: markdown

decision-support

display_name: decision-support

ecosystem-services

display_name: ecosystem-services

forest-inventory

display_name: forest-inventory

forest-model

display_name: forest-model

lidar

display_name: lidar

sensitivity-analysis

display_name: sensitivity-analysis

uncertainty

display_name: uncertainty


en:
Rising temperatures are thawing mountain permafrost, putting infrastructure like buildings, cable cars, and tunnels at risk. While warming air is the main cause, rainfall at high altitudes also plays an important role.
We built a small, portable system to test how active cooling powered by solar panels, can help keep the ground frozen. Experiments showed that rain can sometimes deepen thawing, especially in winter, but in summer under the impact of liquid precipitation it can also briefly refreeze parts of the ground or lead to ground movement.
Outdoor tests over six months showed that a solar-powered cooling system can successfully maintain a stable frozen layer. The results suggest that such systems could help protect infrastructure in mountain regions, though managing rainfall effects is crucial.
The results are described in the publication: Preserving frozen ground with active cooling under seasonal forcing - an experimental study.

en: solar-energy prototype precipitation climate-change permafrost swiss-alps thermal-stabilisation

envi

en: Environment

climate-change

display_name: climate-change

permafrost

display_name: permafrost

precipitation

display_name: precipitation

prototype

display_name: prototype

solar-energy

display_name: solar-energy

swiss-alps

display_name: swiss-alps

thermal-stabilisation

display_name: thermal-stabilisation


en: This dataset contains the responses of an online expert survey on municipal land-use planning regulations governing buildings and open and green spaces across 30 European countries. Using purposive sampling, 73 planning experts – practitioners and academics – rated the priority they assign to thirteen groups of regulations (seven for buildings, six for open and green spaces) and assessed to which extent does each regulation group pursues the objectives of efficiency equity. The survey was administered online between August and December 2023. The dataset is the quantitative companion to the expert-interview dataset on discretion in land-use planning (doi:10.16904/envidat.785).

en: European survey of planning experts on land-use planning regulations

en: efficiency development-priorities building-regulation green-spaces equity

en:
Resource Title: regulations_survey_questionnaire.pdf
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/3676fed8-bc78-46c4-96c7-cea0b89c5630/resource/3c99f535-70e6-4586-ae43-84fcd3ad934e/download/regulations_survey_questionnaire.pdf
    Description: The survey questionnaire as administered. Only the questions whose answers appear in the published data file are included.

en:
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    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/3676fed8-bc78-46c4-96c7-cea0b89c5630/resource/6a0473de-2866-406a-b233-ece90429c965/download/regulations_survey_data.xlsx
    Description: The survey data in Excel format. 73 respondents from 30 European countries, 45 columns: respondent background, priority ratings of regulation groups and their efficiency and equity ratings. Identical content to the csv file.

en:
Resource Title: Data_Documentation.pdf
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/3676fed8-bc78-46c4-96c7-cea0b89c5630/resource/e75c2dc0-7329-4fcb-9b9a-820b496370d0/download/data_documentation.pdf
    Description: Full documentation of the dataset. Describes the survey design, sampling, the 45 variables, regulation group definitions, license and recommended citation. Read this first.

en:
Resource Title: regulations_survey_data.csv
    Size: 14643 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3676fed8-bc78-46c4-96c7-cea0b89c5630/resource/95cb0a11-6693-4e5b-b1aa-9065b0912c36/download/regulations_survey_data.csv
    Description: The survey data in plain CSV format. 73 respondents from 30 European countries, 45 columns. Identical content to the xlsx file.

building-regulation

display_name: building-regulation

development-priorities

display_name: development-priorities

efficiency

display_name: efficiency

equity

display_name: equity

green-spaces

display_name: green-spaces


en: This dataset comprises the interview guides and the corresponding verbatim transcripts of 16 in-depth, semi-structured expert interviews examining how discretion is understood and exercised in land-use planning, across both plan-making and development control. The three jurisdictions — England (5 interviews), Ireland (5) and Switzerland (6) — were selected for their contrasting approaches to pursuing efficiency and equity through planning regulation, as identified in the companion survey of planning experts (doi:10.16904/envidat.735). The interviews were conducted with planning practitioners and academics across two rounds, structured by a six-question first guide and an eight-question second guide. All interviews were digitally recorded, transcribed verbatim, and anonymised prior to deposit.

en: Interviews with experts in Switzerland, England and Ireland on planners' discretion and the pursuit of efficiency and equity objectives

en: land-use-regulations land-use-planning planning-culture development-control plan-making

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:
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    Description: Anonymised transcript of expert interview CHE2 (Switzerland, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
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    Description: Anonymised transcript of expert interview ENG5 (England, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

en:
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    Description: Anonymised transcript of expert interview CHE5 (Switzerland, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

en:
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    Description: Anonymised transcript of expert interview ENG2 (England, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
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    Description: Anonymised transcript of expert interview CHE1 (Switzerland, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
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en:
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    Description: Anonymised transcript of expert interview CHE3 (Switzerland, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
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    Description: Anonymised transcript of expert interview IRL2 (Ireland, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
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    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/6e8bea59-8dc5-4348-8b63-0267942ab8ae/download/interview_guide.pdf
    Description: The semi-structured interview guides for both rounds. Round 1 used questions Q1-6. Round 2 used questions Q1-8. Question numbers match those referenced in the transcripts.

en:
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    Format: text/csv
    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/3e865c5f-fc90-4d82-ad8c-1226ecd5bac0/download/participant_index.csv
    Description: Index of the 16 interviews. One row per participant with anonymised code, country, interview round, question set used and the matching transcript file name.

en:
Resource Title: Transcript_ENG1.docx
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en:
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    URL: https://www.envidat.ch/dataset/cc23353a-5255-4b9c-a73d-aed9e19ec87a/resource/40211321-bf12-41e4-9593-34f37ee833fa/download/data_documentation.pdf
    Description: Full documentation of the dataset. Describes the study design, participant recruitment, the two interview rounds, the anonymisation procedure, known limitations, license and recommended citation. Read this first.

en:
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    Description: Anonymised transcript of expert interview CHE6 (Switzerland, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

en:
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    Description: Anonymised transcript of expert interview IRL5 (Ireland, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

en:
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en:
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    Description: Anonymised transcript of expert interview IRL3 (Ireland, round 1, questions Q1-6). Names, organisations and self-identifying places are redacted.

en:
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en:
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en:
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    Description: Anonymised transcript of expert interview CHE4 (Switzerland, round 2, questions Q1-8). Names, organisations and self-identifying places are redacted.

development-control

display_name: development-control

land-use-planning

display_name: land-use-planning

land-use-regulations

display_name: land-use-regulations

plan-making

display_name: plan-making

planning-culture

display_name: planning-culture


en:
*Currently, this dataset is intended solely for article review purposes and may not be used for any other purposes.*
This dataset consists of a series of full snow profiles collected at the field site of Weissfluhjoch, Davos, Switzerland over the two snow seasons of 2022-2023 and 2023-2024. This series of snow profiles was collected with a high vertical and temporal resolution, with a specific focus on the melting seasons, during which up to 3 snow profiles per week were sampled. This dataset is part of the measurement campaign carried out within the SnowTinel project, whose aim is to explore the Sentinel-1 SAR backscattering response to melting Alpine snowpacks. Therefore, this dataset contains manual measurements of the main properties of snow which are responsible for scattering, i.e. temperature, density, specific surface area (SSA), liquid water content (LWC), surface roughness and snow water equivalent (SWE).
The dataset is composed of a total of 85 snow profiles, 38 carried out in 2022-2023 and 47 in 2023-2024.
  - Profiles of snow **temperature** were sampled at a vertical resolution of 10 cm (2022-2023) and 5 cm (2023-2024) using HI98501 Checktemp from Hanna Instruments;
   - Profiles of snow **density** were sampled at a vertical resolution of 3 cm using a box density cutter and a digital scale;
   - Profiles of snow **specific surface area** were sampled at a vertical resolution of 4 cm using the InfraSnow sensor from FPGA;
   - Profiles of snow **liquid water content** were sampled at a vertical resolution of 2 cm using the Denothmeter (2022-2023) and the New Capacitive Sensor from FPGA (2023-2024). In conditions of ripe snow, dielectric measurements were backed up by melting calorimetry measurements following a recently revised field protocol;
   - Snow **surface roughness** is expressed by the Root Mean Square of the Heights (RMSH) and the correlation length (CL), both computed from a digitized snow transect obtained by a digital photograph of a panel vertically inserted into the snow.
- **Snow water equivalent** was sampled in sections from the surface to the bottom of the snowpack with a cylinder cutter.

Additionally, **runoff** was automatically measured in the close proximity to the measurement field at a sub-hourly resolution by a lysimeter. The instrument was unfortunately discovered to be clogged at the start of the runoff in 2023 and only repaired in late May 2023. Therefore, the time series is not explanatory for the runoff start in 2023. The instrument was inspected timely and assessed as fully functional for the following snow season. The manually measured values of **SWE** are complemented by an automatically recorded time series at sub-hourly time intervals with a SSG1000 snow scale manufactured by Sommer Messtechnik, Austria.

 ---

- References:
  - HI98501 Checktemp: [https://www.hannainstruments.co.uk/modules/teapotknowledgehub/uploads/ist98501_06_18-60d496ca2cd31.pdf](https://www.hannainstruments.co.uk/modules/teapotknowledgehub/uploads/ist98501_06_18-60d496ca2cd31.pdf)
  - InfraSnow sensor: Wolfsperger, Fabian & Ziegler, Silvio & Schneebeli, Martin & Löwe, Henning. (2022). Evaluation of the InfraSnow: a handheld device to measure snow specific surface (SSA). 10.13140/RG.2.2.31566.95047; [https://snow-sen.com/infrasnow-ssa-sensor-2/](https://snow-sen.com/infrasnow-ssa-sensor-2/)
  - Denothmeter: Denoth, A. “An Electronic Device for Long-Term Snow Wetness Recording.” Annals of Glaciology 19 (1994): 104–6. [https://doi.org/10.3189/S0260305500011058](https://doi.org/10.3189/S0260305500011058)
  - New Capacitive Sensor: Wolfsperger, Fabian & Geisser, Michel & Ziegler, Silvio & Löwe, Henning. (2023). A NEW HANDHELD CAPACITIVE SENSOR TO MEASURE SNOW DENSITY AND LIQUID WATER CONTENT; [https://snow-sen.com/slf-snowpro-40/](https://snow-sen.com/slf-snowpro-40/)
  - Melting calorimetry: Barella, R., Bavay, M., Carletti, F., Ciapponi, N., Premier, V., and Marin, C.: Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow, The Cryosphere, 18, 5323–5345, [https://doi.org/10.5194/tc-18-5323-2024](https://doi.org/10.5194/tc-18-5323-2024), 2024.
  - Surface roughness: B. Riccardo, C. Marin, M. Callegari, M. Gianinetto, T. Moranduzzo and C. Notarnicola, "A Low-Cost Portable Automatic System for Snow Surface Roughness Measurements Based on Digital Photography," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 5562-5565, doi: [https://doi.org/10.1109/IGARSS47720.2021.9553989](https://doi.org/10.1109/IGARSS47720.2021.9553989).
  - SSG: [https://www.sommer.at/de/produkte/schnee-eis/schneewaage-ssg-2](https://www.sommer.at/de/produkte/schnee-eis/schneewaage-ssg-2)

en: SnowTinel high-temporal-resolution ground truth dataset for SAR remote sensing of snow

en: snow-ssa snow-lwc sentinel-1 surface-roughness radar snow-profiles snow-density snow-temperature wet-snow snow-melt snow-roughness satellite

envidat

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

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
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format: OCTET

en:
Resource Title: snowtinel_surface_roughness_2023_2024.csv
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    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/95e1f75e-f4a1-448b-b8a1-418b3872e2a4/download/snowtinel_surface_roughness_2023_2024.csv
    Description: Manually measured time series of surface roughness (2023-2024).

en:
Resource Title: snowtinel_profiles_2022_2023.csv
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    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/43461178-b0b2-4c9b-895e-8398b986f552/download/snowtinel_profiles_2022_2023.csv
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en:
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    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/40dcf569-8e0b-4496-a556-79259095bf34/download/snowtinel_swe_2023_2024.csv
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en:
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    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/6c8939cc-2947-43c4-b0d5-9750e3bd00ab/download/snowtinel_profiles_2023_2024.csv
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en:
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en:
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en:
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    Size: 3369865 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/84b800f0-f5df-48ca-9474-005e952118de/download/lysimeter_data_2022_2024.smet
    Description: Runoff time series automatically recorded from the lysimeter (2022-2024).

radar

display_name: radar

satellite

display_name: satellite

snow-density

display_name: snow-density

snow-lwc

display_name: snow-lwc

snow-melt

display_name: snow-melt

snow-profiles

display_name: snow-profiles

snow-roughness

display_name: snow-roughness

snow-ssa

display_name: snow-ssa

snow-temperature

display_name: snow-temperature

surface-roughness

display_name: surface-roughness

wet-snow

display_name: wet-snow


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.

en: SWE2HS model calibration and validation data

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

model-calibration-data

display_name: model-calibration-data

model-validation-data

display_name: model-validation-data

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent


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.

en: Monitoring data sets of alpine photovoltaic power-plants

en: bifaciality alps snow winter-electricity photovoltaic

en:
Resource Title: A1.zip
    Size: 5999205 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/40024d58-c912-499e-8f4f-8a9b9189955a/resource/1a7597fd-203a-427f-851a-009673c29374/download/a1.zip
    Description: 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:
Resource Title: A2.zip
    Size: 27177550 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/40024d58-c912-499e-8f4f-8a9b9189955a/resource/e68bb1d0-915a-49a5-a108-c607356b1889/download/a2.zip
    Description: 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.

alps

display_name: alps

bifaciality

display_name: bifaciality

photovoltaic

display_name: photovoltaic

snow

display_name: snow

winter-electricity

display_name: winter-electricity


en:
This collection of datasets consists of various measurements taken during the year 2022 in Pfynwald. It combines 2 electrical resistivity transects which were monitored in May and July, before and after the irrigation season. The transects transverse all treatments (irrigation, control, irrigation stop). Each transect was repeated twice during the day for a period of 3 days to a week.
In addition there are raw and post-processed drone images (and resulting PRI maps), which were used to compare the below ground responses (from resistivity) to the above ground (crown) stress.
Here, the raw data is stored and a link to a git project is provided where python code is stored to reproduce all the results the published manuscript: "Does optimality partitioning theory fail for belowground traits?  Insights from geophysical imaging of a drought-release experiment in a dry Scots Pine forest", New Phytologist, Shakas et al., 2024. The in-depth explanations from each processing step are found in the code (git project).

en: Pfynwald Geoelectric Experiment 2022

en: ert pinus-sylvestris drought irrigation drone-data

envidat

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

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: 2022_NP.zip
    Size: 511138780 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/1e4afcde-3acf-4b78-ab2f-0376aab525be/resource/167b27d0-27c2-48d3-b16b-83f4eb2bf740/download/2022_np.zip
    Description: This zip file contains all raw and processed data for the electrical resistivity and drone campaigns that took place in Pfynwald, 2022

en:
Resource Title: SWPC_data_Pfynwald_NP2024_data.csv
    Size: 153809 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1e4afcde-3acf-4b78-ab2f-0376aab525be/resource/e0f3f914-7db4-4536-a47f-ee8b878b5ad1/download/swpc_data_pfynwald_np2024_data.csv
    Description: This .csv file contains the soil water potential data for all treatments (irrigation, irrigation-stop and control) and for the whole year of 2022. The data is given as a mean value and with an upper and lower uncertainty bound, given from the manufacturer. The depth of the sensor is given in cm, and the measured values are in units of kPa.

drone-data

display_name: drone-data

drought

display_name: drought

ert

display_name: ert

irrigation

display_name: irrigation

pinus-sylvestris

display_name: pinus-sylvestris


en:
Monitoring terrestrial mammals through eDNA in rivers of northern Namibia
In 2022, we collected 20 water samples from 10 stations two rivers of Namibia. Samples were taken from the side of the river. At each station, we performed two filtration replicates using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 30 minutes, during which approximately 30 liters of water were filtered through each capsule. After filtration, the water inside the capsules was removed, and the capsules were filled with 50 ml of conservation buffer for preservation at room temperature. We followed strict contamination control protocols throughout both the fieldwork and laboratory processes, adhering to the guidelines of Valentini et al. (2016). We used eDNA primers targeting bacteria, bivalves, fish, vertebrates (V05) and all Eukaryotes (Euka03). To prevent contamination, each sample was processed using disposable gloves and single-use filtration equipment. The MiSeq Reagent Kit v3 (2x75 bp) (Illumina, San Diego, CA, USA) was used for paired-end sequencing at a theoretical sequencing depth of 200,000 reads per sample.
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

en: Environmental DNA Freshwater Namibia Namibia 2022

en: bivalves edna metabarcoding eukaryotes namibia

bivalves

display_name: bivalves

edna

display_name: edna

eukaryotes

display_name: eukaryotes

metabarcoding

display_name: metabarcoding

namibia

display_name: namibia