maintainer: Albrecht von Boetticher

en: barrier-impact-simulation interfoam murgang debris-flow finite-volumes natural-hazard shallow-landslide dberisintermixing hangmuren

en: These are the installation instructions for debrisInterMixing4

en: debrisInterMixing for OF-Version 6

en:
Please install OpenFOAM 7 on a UBUNTU Linux system following the instructions given in readme_debrisintermixing-7.pdf
After a successful OpenFOAM installation, copy the applications-Folder from putMyContent-.zip into the OpenFOAM-User directory and follow the instructions in readme_debrisintermixing-7.pdf to install debrisInterMixing 7.

If you use UBUNTU 24-04 you might have troubles getting openmpi, I attached a modifiesd instruction installtionOF7Ubuntu20.04.5LTS.txt for a workaround.

en:
This version of debrisInterMixing is based on OpenFOAM Version 4.x and was tested on Ubuntu 18.04.1 LTS.
Please follow the instructions given in ReadMe_debrisInterMixing4.pdf for installation.

en: Installation Instructions for debrisInterMixing-6

en: These are the installation instructions for debrisInterMixing6

en:
Here debrisInterMixing until version 7 is provided for download. Further versions as well as installation instructions and tutorials and discussion forums (mostly in german) can be acessed at murgang.org (choose "als Gast anmelden" and then scroll to the bottom). The first OpenFoam-compatible Version 2.3.x are available as supplement to v. Boetticher, A., Turowski, J. M., McArdell,W. B., Rickenmann, D., Hürlimann, M., Scheidl, C., and Kirchner, J. W.: DebrisInterMixing-2.3: A Finite Volume solver for three dimensional debris flow simulations based on two calibration parameters. Part two: model validation with experiments. Geoscientific Model Development, 10, 11: 3963-3978. doi: 10.5194/gmd-10-3963-2017.
DebrisInterMixing is a Volume-of-Fluid based Finite Volume code that accounts for shear-thinning sensitive shares of fine sediment suspension together with pressure-sensitive components of the gravel grains within debris flow mixtures. All model properties can be derived from a material sample except for a grid-sensitive calibration parameter.
For more information, please contact albrecht.vonboetticher@wasserbau.ch.

For a recent summary on applications see the DFHM8 contribution at https://www.e3s-conferences.org/articles/e3sconf/abs/2023/52/e3sconf_dfhm82023_02024/e3sconf_dfhm82023_02024.html - DOI: https://doi.org/10.1051/e3sconf/202341502024

UPDATE: DebrisInterMixing for OpenFOAM-7 is available, please contact albrecht.vonboetticher@wasserbau.ch. DebrisInterMixing with OpenFOAM-10 is ready but not yet validated.

barrier-impact-simulation

display_name: barrier-impact-simulation

dberisintermixing

display_name: dberisintermixing

debris-flow

display_name: debris-flow

finite-volumes

display_name: finite-volumes

hangmuren

display_name: hangmuren

interfoam

display_name: interfoam

murgang

display_name: murgang

natural-hazard

display_name: natural-hazard

shallow-landslide

display_name: shallow-landslide

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: Three-dimensional debris flow simulation tool debrisInterMixing


maintainer: Michael Lehning

en: alpine3d snow snow-hydrology alpine-precipitation snow-distribution snow-cover alpine3d-simulations

state: active

type: dataset

en: ADS datasets and simulation inputs

media_type: application/zip

en: The file contains the ADS datasets of snow depth and input data used for simulations.

format: ZIP

mimetype: application/zip

en:
Dataset (Model input, snow distribution and validation) for the precipitation scaling paper, which should be cited along with the data set citation.

This data is useful for distributed hydrological modelling or other tasks that involve the study of snow distribution and precipitation in the high Alpine.

The format of the data is for Alpine3D (models.slf.ch) model runs but other models could be used, too.

Please cite:

_Vögeli, C., Lehning, M., Wever, N., Bavay M., 2016: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution., Front. Earth Sci. 4: 108. doi: 10.3389/feart.2016.00108._

Dataset is provided as a single zip file.
The archive contains two directories, the valuable distributed snow depth maps for the landscape Davos and the simulation input.
The archive also contains the file: "ReadMeMetadataDataSetPrecipitationScaling" which explains the data structure.

alpine-precipitation

display_name: alpine-precipitation

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-distribution

display_name: snow-distribution

snow-hydrology

display_name: snow-hydrology

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and 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: Precipitation Scaling Data Set (Vögeli et al., Frontiers)


maintainer: EnviDat Support

en: snow-height snow-profile gcos snow snow-water-equivalent

en: STATION METADATA

media_type: text/plain

format: TXT

mimetype: text/plain

en: DATA ACCESS

media_type: text/csv

format: CSV

en:
This dataset contains long-term snow water equivalent and corresponding snow depth data 11 observer sites in Switzerland between 1200 and 2500 m a.s.l. compiled for the Global Climate Observing System (GCOS) and supported by MeteoSwiss. Snow depth (cm) and snow water equivalent (mm) are manually recorded every 2 weeks since the 1947 (depending on station). The attached metadata file gives details for each station.
The measurement series agree with GCOS objectives according to the GCOS Implementation Plan: This inlcudes:
• Raw data are archived in the snow and avalanche database at SLF.
• Measuring techniques are traceable and documented as snow depth and snow water equivalent have in general remained the same since beginning up to now. When planning new systems or changes of existing systems in the future, their impact will be assessed prior to implementation.
• Historical data of these 11 stations have been digitized and all data have been quality controlled.
• Detailed metadata (location of measurements) are available.
• Public availability of the data has been ensured by publishing the data on the Envidat portal (https://www.envidat.ch/dataset/gcos-swe-data).

gcos

display_name: gcos

snow-height

display_name: snow-height

snow-profile

display_name: snow-profile

snow-water-equivalent

display_name: snow-water-equivalent

en: GCOS SWE data from 11 stations in Switzerland


en: satellite hazard-mapping natural-hazard snow avalanche

en: SPOT6 Avalanche outlines 24 January 2018

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset.

format: SHP

en:
Outlines of 18'737 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 24 January 2018. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping.pdf)

The generation of the data is described in:

Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.

Abstract.
Accurate and timely information on avalanche occurrence are key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability considering accuracy, completeness and reliability of the reported avalanche events is limited. In this study we present the spatial continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12’500 km2).

We tested different satellite sensors available for rapid mapping during a first avalanche period. Based on these experiences, we tasked SPOT6/7 data for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18’737 individual avalanche events, applying image enhancement techniques to analyze regions in cast shadow as well as brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having a unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period.

This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data is of great value to validate avalanche bulletins, complete existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.

Koordinate System: CH1903+ LV95 LN02

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

satellite

display_name: satellite


en: solutes precipitation hydrogen-isotopes stream-water groundwater erlenbach catchment oxygen-isotopes catchment-data

en: This dataset includes mean concentrations of solutes (Calcium, Iron, Magnesium, Sodium, Potassium, Silicon, Nitrate, Sulfate, and Chloride) and stable isotope values (hydrogen and oxygen) in water molecules. These were measured in precipitation, groundwater, soil water, and surface water samples collected from the Erlenbach catchment and its close vicinity in Switzerland during Spring and Fall 2021. Detailed information is available in the accompanying documentation (documentation_for_the_hydrochemical_data_collected_during_spring_and_fall_2021_in_the_erlenbach_catchment.pdf).

media_type: application/pdf

en: This document provides a detailed description of the hydrochemical dataset, including, among other things, an overview of the dataset content, an explanation of the column headers, a description of the locations where samples were collected, and an explanation of the abbreviations used in the dataset.

format: PDF

en: The dataset contains hydrochemical data collected from spring to fall 2021 in the Erlenbach catchment, Switzerland. This hydrochemical data includes solute concentrations and stable isotopes of oxygen and hydrogen in water molecules, measured in streamwater, soil water, groundwater, and rainfall samples. Samples were collected during seven rainfall events with higher temporal resolution (hourly) as well as at lower resolution between events. A detailed description of the dataset is provided in the documentation.

catchment

display_name: catchment

catchment-data

display_name: catchment-data

erlenbach

display_name: erlenbach

groundwater

display_name: groundwater

hydrogen-isotopes

display_name: hydrogen-isotopes

oxygen-isotopes

display_name: oxygen-isotopes

precipitation

display_name: precipitation

solutes

display_name: solutes

stream-water

display_name: stream-water

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: Hydrochemical Data Collected during Spring-Fall 2021 in the Erlenbach Catchment


en: hac2 ccn cloud-droplets aerosol hygroscopicity

format: XLS

format: SERVICE

en: This repository contains the datasets that were used for used for the paper:: Drivers of Droplet Formation in East Mediterranean Orographic Clouds, EGUsphere, https://doi.org/10.5194/egusphere-2024-490, 2024.

aerosol

display_name: aerosol

ccn

display_name: ccn

cloud-droplets

display_name: cloud-droplets

hygroscopicity

display_name: hygroscopicity

educ

en: Education, culture and sport

en: Dataset for the publication Drivers of Droplet Formation in East Mediterranean Orographic Clouds


en: winter-energy-production albedo shortwave-radiation photovoltaic snow

en: Data Forward Scattering

en: GROUNDEYE model for multiple forward scattering in snow-covered terrain. Meteorological and topographical input data set for the Meierhofertäli near Davos, Switzerland, September 2017 - August 2018. Output data for September 2017 - August 2018 at hourly resolution and every pixel. Post-processing scripts for plotting and analysis.

en:
 Overview
We present GROUNDEYE, a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow.
Embedded in the surface process model Alpine3D, the new terrain radiation model GROUNDEYE receives interpolated real weather data with diffuse and direct broadband shortwave radiation for each pixel as well as a spatially variable plane albedo from the module SNOWPACK.

 Format
The GROUNDEYE model is written in c++, as is the entire environment of Alpine3d. The input and output data sets are .xlsx or .txt format, pre- and postprocessing including the generation of all figures is in .R format.

 Structure
In Data_Forward_Scattering.zip you will find all necessary data and model details to
reproduce the results of the JGR publication "How forward-scattering snow and terrain change the Alpine radiation balance with application to solar panels"

 - 	Model Input Data contains the meteorological and topographic input data sets, the BRDF, and preprocessing scripts.
 - Model Code contains the full model Alpine3d including the radiative transfer module GROUNDEYE.
 - Model Output Data contains the results of the simulation of terrain irradiance and irradiance of solar panels; hourly resolution, 1. Sptember 2017 - 31. August 2018.
 - Measurements Solar Testsite contains information and measurements of the solar testsite at the Totalp near Davos, Switzerland.
 - Postprocessing contains all R-Scripts used for the analysis and
plotting of the corresponding data.

In each of these folders you will find detailed information in
the file 'About this Folder.txt'.

albedo

display_name: albedo

photovoltaic

display_name: photovoltaic

shortwave-radiation

display_name: shortwave-radiation

snow

display_name: snow

winter-energy-production

display_name: winter-energy-production

en: Data, Code Link and Metadata on Forward Scattering of Snow at Totalp


maintainer: Franziska Gerber

en: surface-mass-balance cryowrf antarctica blowing-snow reproducibility

en: Processed CRYOWRF output used for the analysis and the figures in the paper.

en: Static input data from the WRF simulations (used in the python scripts in Scripts_Reproducibility).

en: Python scripts to reproduces figures in Gerber et al., 2022, revised

mimetype: application/x-zip-compressed

en: Processed WRF output (run with Noah-MP land surface model) used for the analysis and the figures in the paper.

en: Python scripts to reproduces figures in Gerber et al., 2022, submitted.

en: Namelist and input files and information to run the WRF and CRYOWRF simulations used in the paper.

en:
This dataset contains data and scripts for "CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB" (Gerber et al., submitted).

* Simulation_setup: Namelists and input information to run the simulation. Some input files need to be downloaded from Sharma et a., 2021.
* Static_input: Static topography input file of WRF (geo_em.d01).
* WRF_27km_NoahMP: Preprocessed WRF output of the simulation run with the WRF using the surface parameterization Noah-MP to reproduce the figures and results in the paper.
* WRF_27km_CRYOWRF: Preprocessed WRF output of the simulation run with CRYOWRF to reproduce the figures and results in the paper.
* Scripts_Reproducibility: Python scripts to reproduce the figures and results in the paper.

Note:

* To run some of the scripts Atmospheric Weather station data needs to be prepared using Gerber and Lehning, 2022.
* AWS data is not provided and needs to be downloaded from the corresponding databases. Please make sure to comply with the respective terms and conditions.

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

cryowrf

display_name: cryowrf

reproducibility

display_name: reproducibility

surface-mass-balance

display_name: surface-mass-balance

en: Reproducibility dataset for CRYOWRF validation


en: first-order-catchment meteorological-data alptal snow runoff subalpine

en: Manual snow measurements Alptal (1968-2022)

en:
Snow depth, snow density and snow water equivalent have been measured since winter 1968/69 along more than 15 transects distributed across the Alptal valley representing different altitudes, aspects and land use (closed forest and open meadows). Length of the snow courses: 20 to 30 m. The measurement interval has been weekly to monthly.
More details about the locations and methods can be found in Stähli, M.; Gustafsson, D., (2006). Long-term investigations of the snow cover in a subalpine semi-forested catchment. Hydrological Processes, 20, 411-428. doi: 10.1002/hyp.6058.

en: Daily meteorological and snow data (1968-2022) Alptal

en:
Air temperature, dew point temperature, relative humidity, wind speed, precipitation and global radiation have been measured (since 1981) on a meadow (Erlenhöhe) in the Erlenbach catchment. It is located at 1'220 m a.s.l. with an exposure towards west. In order to assess the spatial variability of precipiation, two additional rain gauges have been running in the Vogelbach and Lümpenenbach catchment, respectively.
For the period before 1981, meteorological variables have been extrapolated from the nearby MeteoSwiss station Einsiedeln (ca. 15 km north) to the WSL station Erlenhöhe using a linear correction factor for air temperature, wind speed and precipitation.
Snow depth has been recorded automatically since 2003. Snow water equivalent has been calculated using two numerical models: COUP model (Jansson, 2012) and DeltaSnow model (Winkler et al., 2021)

Jansson, P.-E. (2012). COUP model: model use, calibration, and validation.Transactions of the ASABE, 55(4), 1335–1344.
Winkler, M., et al. (2021): Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model, Hydrol. Earth Syst. Sci., 25, 1165-1187, 10.5194/hess-25-1165-861 2021.

en: Daily runoff data from three steep torrents in the Alptal (1968-2022)

en: Runoff, water temperature and electrical conductivity have been measured since 1968 at the outlet of three torrential catchments (with an area of approximately 1 km2 each) located in the Alptal valley: Erlenbach, Lümpenenbach and Vogelbach. This data set provides daily mean values; data at 10-min resolution can be provided on request for the period 1984-2022 (contact: alptal@wsl.ch).

en: Meteorological data Alptal 1968-2024 EnviDat updated

en:
Air temperature, dew point temperature, relative humidity, wind speed, precipitation and global radiation have been measured (since 1981) on a meadow (Erlenhöhe) in the Erlenbach catchment. It is located at 1'220 m a.s.l. with an exposure towards west. In order to assess the spatial variability of precipitation, two additional rain gauges have been running in the Vogelbach and Lümpenenbach catchment, respectively. For the period before 1981, meteorological variables have been extrapolated from the nearby MeteoSwiss station Einsiedeln (ca. 15 km north) to the WSL station Erlenhöhe using a linear correction factor for air temperature, wind speed and precipitation. Snow depth has been recorded automatically since 2003. Snow water equivalent has been calculated using two numerical models: COUP model (Jansson, 2012) and DeltaSnow model (Winkler et al., 2021)
Jansson, P.-E. (2012). COUP model: model use, calibration, and validation.Transactions of the ASABE, 55(4), 1335–1344. Winkler, M., et al. (2021): Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model, Hydrol. Earth Syst. Sci., 25, 1165-1187, 10.5194/hess-25-1165-861 2021.

en: Runoff daily mean Alptal 1968-2024 EnviDat updated

en: Runoff, water temperature and electrical conductivity have been measured since 1968 at the outlet of three torrential catchments (with an area of approximately 1 km2 each) located in the Alptal valley: Erlenbach, Lümpenenbach and Vogelbach. This data set provides daily mean values; data at 10-min resolution can be provided on request for the period 1984-2024 (contact: alptal@wsl.ch).

en: Snow measurements Alptal 1969-2024 EnviDat updated

en:
This data set includes 56 years of hydrometeorological measurements from small (first-order) catchments in the pre-alpine valley Alptal. Here we provide daily mean values; values in hourly or 10-min resolution can be provided on demand.
Runoff has been measured at the outlet of three small (first-order) catchments of approximately 1 km2 area: Erlenbach (two independent runoff measurements), Vogelbach and Lümpenenbach. The catchments are similar with regard to geology (Flysch) and soil conditions (clay soils), but differ in forest coverage (20 to 60%). A detailed description of the catchments can be found at https://www.wsl.ch/alptal .
Runoff in these small catchments is typically very dynamic and can temporally carry large amounts of sediment and large wood. Thus, the accuracy of the measurements at very large flow is limited.
Meteorological variables have been measured on a meadow (Erlenhöhe) located in the Erlenbach catchment at 1220 m a.s.l. using a standard meteorological station (incl. ventilated air temperature and heated rain gauges). In addition, precipitation has also been recorded at two other locations (in the Vogelbach and Lümpenenbach catchments).
Snow measurements have been conducted weekly to monthly since 1968 at more than 15 locations (30-m transects) representing different altitudes, aspects and land uses (meadow, forest). In addition, snow depth has been recorded continuously since 2003 at Erlenhöhe, and for this location we also include a simulation of snow depth and SWE (using the numerical models COUP and DeltaSnow) that assimilates the manual weekly snow-course measurements. Details on these snow measurements can be found in Stähli, M. and Gustafsson, D. 2006. Hydrol. Proc., 20, 411-428. doi: 10.1002/hyp.6058.
Further information on the methods and sensors can be found at https://www.wsl.ch/alptal .
A first version of this data set (for the period 1968-2017) was uploaded in June 2018 at the occasion of the 50-year anniversary. This original data set was updated in February 2021 (with data from 2018 and 2019), and this data set was used for a longterm trend analysis, submitted for publication in a special issue of Hydrological Processes. Further updates of the data set (with data from 2020 to 2022) were uploaded in March 2023, February 2024 and March 2025.

alptal

display_name: alptal

first-order-catchment

display_name: first-order-catchment

meteorological-data

display_name: meteorological-data

runoff

display_name: runoff

subalpine

display_name: subalpine

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: Longterm hydrological observatory Alptal (central Switzerland)


defoliation-growth-data

title_for_slug: defoliation-growth-data

en: defoliation tree-growth literature-review forest-health relative-growth

en:
Using a literature review extracted data from 26 published papers which had data on individual tree defoliation and growth relationships In Europe.
This data set includes 415 defoliation-growth data points from 16 different tree species covering 15+ countries in Europe. Growth metrics used in individual papers were different, and included increments in terms of volume, tree rings width, radial growth, basal area and biomass. All growth metrics were converted into relative growth (presented in this dataset), expressed as a percentage of the growth of progressively defoliated trees in relation to the growth of those trees adopted as reference (control) defoliation level (i.e. the growth of the lowest defoliation level in each study was used as the reference growth point).

defoliation

display_name: defoliation

forest-health

display_name: forest-health

literature-review

display_name: literature-review

relative-growth

display_name: relative-growth

tree-growth

display_name: tree-growth

educ

en: Education, culture and sport

en: Defoliation-Growth data


maintainer: Tessa Hegetschweiler

en: teenagers forest childhood recreational-activities adolescents

en: Dataset survey at schools

en: The dataset of the questionnaire survey at schools in excel format.

en: Dataset comments

en: The codebook to the dataset

en: Questionnaire school survey

en: The questionnaire used in the survey at 8 schools aimed at teenagers in German.

mimetype: application/pdf

en: Survey at schools 2017

en: The dataset of the questionnaire survey at schools in SPSS-format.

en: The data results from a questionnaire survey conducted at 8 schools in the cantons Zurich, Aargau and St. Gallen. Respondents aged 13-22 years. The aim of the survey was to gain insight into teenagers' relationship to the forest, reasons for visiting or not visiting the forest and activities in the forest.

adolescents

display_name: adolescents

childhood

display_name: childhood

forest

display_name: forest

recreational-activities

display_name: recreational-activities

teenagers

display_name: teenagers

en: Factors influencing teenagers' forest visit frequency


digitizing-historical-plague

maintainer: Christian Ginzler

title_for_slug: digitizing-historical-plague

en: plague-outbreaks yersinia-pestis bubonic-plague black-death historical-plague plague-inventory palaeo-epidemiology plague-mapping

en: We present newly digitized data on 6,929 plague outbreaks that occurred between 1347 and 1900 AD across Europe. The data base on an inventory initially published 1976. For georeferencing the information of Tele Atlas 2009 was used. The coordinates are in the reference systems ETRS89 and WGS84.

black-death

display_name: black-death

bubonic-plague

display_name: bubonic-plague

historical-plague

display_name: historical-plague

palaeo-epidemiology

display_name: palaeo-epidemiology

plague-inventory

display_name: plague-inventory

plague-mapping

display_name: plague-mapping

plague-outbreaks

display_name: plague-outbreaks

yersinia-pestis

display_name: yersinia-pestis

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: Digitizing historical plague


en: List of authors and list of the editorial board of the European Snow Booklet

en:
Here you can find the list of all lead authors of the different chapters of the European Snow Booklet (ESB).
In addition a list of the editorial board that reviewed the ESB is given.

en: COST European Snow Booklet (ESB)

en: Please find here the web version of the COST European Snow Booklet (ESB).

en: European Snow Booklet public metadata

en:
The core of the European Snow Booklet (ESB), the country reports of 38 European countries, are based on information and metadata on snow measurements collected through a comprehensive survey, the ESB Questionnaire, between August 2017 and March 2018. The station metadata (for example location, elevation, measurement start date) from each country was provided by one or several institutions responsible for snow observations. By answering the questionnaire the contacts agreed that the information and metadata provided could be presented in the ESB. For cases where institutions indicated that information on station locations could be made publicly available, the metadata for the variables snow depth (HS), depth of snowfall (HN) and water equivalent of the snow cover (SWE), is additionally provided here for download (METADATA.xlsx).
Please note that not all institutions gave permission to publish their station metadata. This metadata has to be requested from the non-personal institution contacts provided in the METADATA.xlsx file. In some cases, however, no institutional contact is available and thus the contact of the country representative who filled in the questionnaire is used.
For current, updated station metadata we refer to the (non-personal) institutional contacts provided in METADATA.xlsx and/or the institution webpages. Note, however, that we cannot guarantee for completeness of the dataset for all countries because operational networks are prone to changes.

en: COST European Snow Booklet (ESB) - DORA Link

media_type: text/html

en: DORA Link of the COST European Snow Booklet (ESB).

format: HTML

en:
The European Snow Booklet (ESB) is a book of reference for snow measurements that has been produced through collaboration with many European snow practitioners and snow scientists in the framework of the European Cooperation in Science and Technology (COST) Action ES1404 “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (HarmoSnow)”. The ESB provides a unique collection of information about operational snow observations in the European countries and the methods used to perform basic measurements of snow on the ground: snow depth (HS), depth of snowfall (HN), water equivalent of the snow cover (SWE) and presence of snow on the ground (PSG). Information and station metadata (for example location, elevation) for these basic snow variables were collected through a comprehensive survey, the ESB questionnaire between August 2017 and March 2018. Numerous institutions of 38 European countries provided detailed information describing the status of the operational snow observations and the methods used at the time of the survey. Based on the information provided, a country report was written for each European country. Similarities and differences among the countries, that is, the choice of snow variables to be measured, the measurement principles applied, the number of stations, or the spatial and elevational station distribution are pointed out. Thus the collection of country reports demonstrates the relevance of snow measurements for each country. Thus, the intention of the ESB is to foster better knowledge transfer regarding snow measurements between the snow science and operational communities and to improve the communication of information to the general public.

For detailed information on the European countries, we refer to the ESB, which can be downloaded here (envidat.ch). Please note that the ESB is not a living document and information and station metadata are from August 2017 till March 2018, except for Latvia (metadata updated in December 2018).

depth-of-snowfall

display_name: depth-of-snowfall

harmonisation-of-snow-measurements

display_name: harmonisation-of-snow-measurements

snow-depth

display_name: snow-depth

snow-measurement-methods

display_name: snow-measurement-methods

water-equivalent-of-the-snow-cover

display_name: water-equivalent-of-the-snow-cover

educ

en: Education, culture and sport

en: European Snow Booklet – an Inventory of Snow Measurements in Europe


en: avalanche climate-change climate-projections wet-snow-avalanche snow dry-snow-avalanche snow-stratigraphy snow-instability

en: SNOWPACK settings

en: contains .ini and .sno file indicating the settings used to drive SNOWPACK with the climate scenarios

en: Climate scenarios .smet

en: contains one .smet file per combination of station, climate model chain and RCP scenario. Each .smet file contains the downscaled meteorological data (time span 1.9.1981-31.8.2099) that was used to drive the SNOWPACK simulations.

en: Projections of avalanche activity

en: contains one file per IMIS station that indicates the daily probability for the occurrence of dry- or wet-snow avalanches in the vicinity of the respective station for each day during the time period 1.12.1981-31.05.2099 (months December to May) and all climate model chains and RCP scenarios.

en:
This data set includes 21st century projections of dry- and wet-snow avalanche activity for seven sites in the Swiss Alps as well as the downscaled climate scenarios and SNOWPACK configuration files used to produce these projections. The methods used to produce these data are described in the research article:
Mayer, S., Hendrick, M., Michel, A., Richter, B., Schweizer, J., Wernli, H., and van Herwijnen, A.: Changes in snow avalanche activity in response to climate warming in the Swiss Alps, EGUsphere, 2024, 1-32, https://doi.org/10.5194/egusphere-2024-1026, 2024.
We used downscaled climate projections to drive the snow cover model SNOWPACK and evaluated future dry- and wet-snow avalanche occurrences in the vicinity of seven automatic weather stations from the IMIS network using machine learning models trained with observed records of avalanche activity. Projections are available for three different emission scenarios (Representative Concentration Pathways, RCP2.6, RCP4.5 and RCP8.5) and eight climate model chains from the CH2018 ensemble. The output of the model chain is given by a daily probability for dry- or wet-snow avalanche occurrences. These probabilities can then be used to classify a day as a dry- and wet-snow avalanche day (AvD) or non-avalanche day.

avalanche

display_name: avalanche

climate-change

display_name: climate-change

climate-projections

display_name: climate-projections

dry-snow-avalanche

display_name: dry-snow-avalanche

snow

display_name: snow

snow-instability

display_name: snow-instability

snow-stratigraphy

display_name: snow-stratigraphy

wet-snow-avalanche

display_name: wet-snow-avalanche


maintainer: Mathias Bavay

en: air-pressure air-temperature snow-depth soil-temperature relative-humidity treeline dew-point-temperature wind-speed shortwave-radiation precipitation-sum wind-direction stillberg

en: The Davos Stillberg meteorological data (Stillberg_meteo_v1.1.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological research site. Metadata are provided in the folder 'Stillberg_meteo_metadata' and the data in the folder 'Stillberg_meteo_data' with subfolders for the three quality levels 'raw_standardized', ‘raw_edited’, and ‘filtered’.

en: The Davos Stillberg meteorological data (Stillberg_meteo_v1.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological research site. Metadata are provided in the folder 'Stillberg_meteo_metadata' and the data in the folder 'Stillberg_meteo_data' with subfolders for the three quality levels 'raw_standardized', ‘raw_edited’, and ‘filtered’.

en: The Davos Stillberg meteorological data (Stillberg_meteo_v2.0.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological resea...

en:
 Background information
The Stillberg ecological treeline research site is located in the transition zone between the relatively humid climate of the Northern Alps and the continental climate of the Central Alps. In 1975, 92,000 seedlings of the high-elevation conifer species *Larix decidua* Mill. (European larch), *Pinus cembra* L. (Cembran pine), and *Pinus mugo* ssp. *uncinata* (DC.) Domin (mountain pine) were systematically planted across an area of 5 hectares along an elevation gradient of about 150 metres, with the aim to develop ecologically, technically, and economically sustainable afforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Alongside the ecological long-term monitoring of the afforestation, several meteorological stations have recorded local meteorological conditions at the Stillberg research site. Here, we provide the Davos Stillberg meteorological timeseries of five stations from 1975 (01-01-1975), the year of the afforestation establishment, until the end of the year 2022 (31-12-2022).

 Station description
The five meteorological stations were all installed at the same location (46°46′25.015″N 9°52′01.792″E) at 2090 m a.s.l., in the lower part of the afforestation area. In general, the five stations were operated sequentially (Stillberg_meteo_metadata_stations_v1.csv). However, there are some overlapping time periods when more than one station was operated in parallel. The stations have recorded environmental parameters, such as air and soil temperature, dew point temperature, air pressure, relative humidity, wind direction and velocity, radiation, precipitation, and snow depth (Stillberg_meteo_metadata_parameters_v1.csv). The meteorological measurements were recorded hourly from 1975 until 1996 and have been recorded in 10-minute intervals since 1997.

 Data description
We processed the Davos Stillberg meteorological timeseries with the MeteoIO meteorological data pre-processing library (Bavay & Egger, 2014). Data files are provided for each station and quality level separately and named according to the station (see ‘Stillberg_meteo_metadata_stations_v1.csv’). From the raw data in their original formats, we generated three data quality levels: raw standardized (folder ‘raw_standardized’), edited (folder ‘raw_edited’) and filtered (folder ‘filtered’). The processing level is indicated in the headers of the data files. The whole processing protocol is described in a set of human-readable configuration files that are used by MeteoIO to generate the required data quality levels. This improves long-term reproducibility (Bavay et al., 2022), as the data could be regenerated in the future, even using a completely different software, to account for additional data points or to introduce new data corrections. The first quality level (raw standardized) is generated by parsing the original data files and interpreting them in order to convert all data points to a common format and meteorological parameter naming scheme, while excluding unreadable or duplicated data lines. The generated data files are derivatives of CSV files, with a standardised header that contains the metadata that are necessary to interpret and use the data (use metadata) and to populate a data index (search metadata). The latter is a textual implementation of the Attribute Convention for Data Discovery (ACDD) metadata standard (Attribute Convention for Data Discovery 1-3, 2022). The second quality level (edited) builds on the raw data by performing low-level data editing, such as removing some data periods that are known to be unusable (often based on maintenance records or anecdotal evidence) or applying undocumented calibration factors (for example, when there seems to be an obvious offset on a measured parameter for a period between two documented maintenance operations). The third quality level is generated by applying statistical filters on the data (per station and per meteorological parameter) to exclude presumably wrong values. We did not perform gap filling, as no single strategy could be relied upon that would work best for all possible data usage scenarios.

air-pressure

display_name: air-pressure

air-temperature

display_name: air-temperature

dew-point-temperature

display_name: dew-point-temperature

precipitation-sum

display_name: precipitation-sum

relative-humidity

display_name: relative-humidity

shortwave-radiation

display_name: shortwave-radiation

soil-temperature

display_name: soil-temperature

stillberg

display_name: stillberg

treeline

display_name: treeline

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

envidat

en: EnviDat: the Environmental Data Portal

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

en: Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.


maintainer: Adam Winstral

en: snow-depth snow-simulation jim jules-investigation-model snow

en: File/data descriptor

en: DEM

en: 1-km resolution DEM used for downscaling gridded NWP data to stations and for distributing BDE evaluations at HS-stations to SWE-stations

en: HS and SWE observations

en: Automated (HS) and manual (SWE) data used to evaluate model simulations

en: Station forcing data

en: All forcing data need to run JIM at HS and SWE stations. These are data downscaled directly from COSMO NWP data (WY14, WY15) and further enhanced by additional station observations of snow depth (HS) and air temperature (Ta) in WY17. Further information can be found in README file. Note that in WY14 and WY15, shortwave radiation was not split into direct and diffuse (not necessary for JIM) and that the default method in JIM for splitting precipitation phase was used. In WY 17, direct and diffuse were combined as input to JIM and JIM was forced with pre-processed liquid and solid precipitation phases.

en: These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the "deterministic" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations.

jim

display_name: jim

jules-investigation-model

display_name: jules-investigation-model

snow-depth

display_name: snow-depth

snow-simulation

display_name: snow-simulation

educ

en: Education, culture and sport

en: Meteorological data used to develop and validate the bias-detecting ensemble (BDE)


en: xylem-water water-budget water-isotopes forest-ecosystems soil-solution

en: Column descriptions for soil solution, deposition, xylem and bulk soil samples.

media_type: image/png

en: Overview of most bulk soil water isotopic signatures.

format: PNG

en: Water isotope signatures of soil solution samples.

en: Overview of most soil solution water isotopic signatures.

en: Overview of most xylem water isotopic signatures.

en: Water isotope signatures and gravimetric water content of bulk soil samples.

en: Water isotope signatures and gravimetric water content of xylem samples.

en: Water isotopic signature of deposition samples.

en: Overview of most deposition water isotopic signatures.

en:
The isotope composition of different forest water pools was analyzed at 10 sites of the Long-term Forest Ecosystem Research programme in 2021 and 2022. The sampling was done during the vegetation season and included fortnightly xylem water samples taken by stem increment cores and each year 3 bulk soil water sampling campaigns (in April, June, September of 2021 and 2022). Both sample types were cryogenically extracted at WSL and analyzed on a Picarro 2140 with Micro-Combustion-Module. Gravimetric water content in both sample types was computed based on pre-and post-extraction weights. In addition to xylem and bulk soil waters, we analyzed the isotope composition of soil solution from a suction cup lysimeters in three soil depths 0, 15, 50, 80 cm and precipitation from samplers below the canopy and outside of the forest, all collected fortnightly from collection periods of varying length (longest from 2017-2022).
Precipitation samples are further described [here](https://www.doi.org/10.16904/envidat.lwf.15). Lysimeter samples are described further [here](https://www.doi.org/10.16904/envidat.lwf.28).

forest-ecosystems

display_name: forest-ecosystems

soil-solution

display_name: soil-solution

water-budget

display_name: water-budget

water-isotopes

display_name: water-isotopes

xylem-water

display_name: xylem-water

en: Water isotope signatures in Swiss forest ecosystems in 2021 and 2022


maintainer: Marcus Schaub

en: drought forest lwf crown-condition scots-pine diameter-at-breast-height swiss-forest-lab tree irrigation tree-species dbh

en: Dataset

en: Swiss Forest Health Inventory

mimetype: text/html

en: Long-term Irrigation	Experiment Pfynwald homepage

en: Shapefile	of measurement location

en: Site photo 2

en: Site photo 3

en: Site photo 1

en: To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46° 18' N, 7° 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2°C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years.

crown-condition

display_name: crown-condition

dbh

display_name: dbh

diameter-at-breast-height

display_name: diameter-at-breast-height

drought

display_name: drought

forest

display_name: forest

irrigation

display_name: irrigation

lwf

display_name: lwf

scots-pine

display_name: scots-pine

swiss-forest-lab

display_name: swiss-forest-lab

tree

display_name: tree

tree-species

display_name: tree-species

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: Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland


maintainer: Nander Wever

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

en: Manual snow pits and other snow density measurements

en: SnowMicroPen pts files and processed txt files

en: Zip archive containing the acquired *.pts files (binary files recorded by the SnowMicroPen), and the converted *.txt files using the SMP.ipynb jupyter notebook (see the python code in the other resources in this dataset)

en: Python code exported from the Jupyter notebook for processing the SnowMicroPen files

en: List of SnowMicroPen measurements

en: List of all SnowMicroPen measurements, including date and location, and processing status. This list has been generated by the python code (see other resources in this dataset).

en: Jupyter notebook for processing the SnowMicroPen files.

en: Documentation SnowMicroPen measurements

en: This *.zip archive contains all available documentation of the SnowMicroPen measurements, including the mapping of the SMP measurements in the terrestrial laser scan field at PEA (see documentation for 20161220) and at the Hammarryggen Ice Rise (see documentation for 20190104 and 20190111).

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

antarctica

display_name: antarctica

microstructure

display_name: microstructure

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen

educ

en: Education, culture and sport

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


en: metaweb food-webs network-analysis trophich habitat-fragmentation trophic-networks robustness

en:
This resource is broken down into two sections: 01 simulations and 02 analyses
In the 01 simulations section, the program which run the simulations for the paper is provided. It is prepared such that it can be easily run on a cluster. Please note that the results folder is empty, as they would have included 108,000 csv files. Instead, the summarised version is available in the next section.
In the 02 analyses section, all code needed to recreate the analyses and figures from the paper are provided.
Please refer to the separate README.txt files for each section for further information.

en:
This dataset contains all processed data, simulation outputs, and R/Python scripts used to assess the robustness of regional multi-habitat food webs in Switzerland under various non-random species extinction scenarios. It accompanies the manuscript submitted to Communications Biology, and available as a preprint here:
Reji Chacko, M, Albouy, C., Altermatt, F., et al. Decreases in the robustness of regional food webs to sequential species extinctions following habitat loss, 04 December 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-5225132/v1]
Please note that the most up-to-date version of the simulations code is available here: https://github.com/mrejichacko/casCHades

food-webs

display_name: food-webs

habitat-fragmentation

display_name: habitat-fragmentation

metaweb

display_name: metaweb

network-analysis

display_name: network-analysis

robustness

display_name: robustness

trophic-networks

display_name: trophic-networks

trophich

display_name: trophich

en: Simulation data and analyses of regional food web robustness under habitat loss


maintainer: Johanna T. Malle

en: land-cover evapotranspiration land-use snow clm5 high-resolution oshd forest-productivity snow-simulation

en:
12 gridded CLM5 simulations over the spatial extent of Switzerland:
- 1km, 0.25deg, 0.5deg
- 3 different meteorological forcings (Clim_CRU, CLIM_CRU*, CLIM_OSHD)
- 2 different land use forcings
each with the following outputs:
- daily snow depth (file SNOW_DEPTH.nc)
- monthly summed gross primary production (file FPSN_MONTH_SUM.nc)
- monthly summed evapotranspiration (file QFLX_EVAP_TOT_MONTH_SUM.nc).
Temporal extent of all simulations: 01/01/2015 - 31/12/2020.

en: Forcing land surface datasets used for the CLM5 simulations at 1km, 0.25° and 0.5° resolution. For the 1km resolution both the standard global (LU_Gl) and the high-resolution dataset (LU_HR) are provided.

en: Point-scale CLM5 simulation output of snow depth and snow water equivalent for 9 different simulation scenarios, each performed at 36 snow station locations across Switzerland. Please have a look at table A1 in the appendix of the linked publication for coordinates of the stations. Additional information on the setup, and the differences between simulation setups can also be found in the linked publication.

en:
This dataset contains Community Land Model 5 (CLM5) simulation output over the spatial extent of Switzerland at different resolutions and based on a range of input datasets. It further contains land-use surface data used for the CLM5-simulations.

**Detailed description of the CLM5 simulation setup and the various input datasets can be found in the accompanying publication: https://doi.org/10.5194/egusphere-2023-1832.**

 CLM5 simulation output

This dataset includes gridded CLM5 simulations of snow depth, gross primary productivity (GPP) and evapotranspiration at different resolutions ( 1km, 0.25° and 0.5°) and based on a range of input datasets over the spatial extent of Switzerland (see folder *gridded_CLM5_simulations*). Additionally, point-scale CLM5 simulations of snow depth and snow-water-equivalent at 36 snow-station locations (see folder *point_scale_CLM5_simulations*) are included. Latitude, longitude and elevation for these station locations can be found in table A1 of the above-mentioned publication. All simulation output spans from 01/01/2015 - 31/12/2019.

Included CLM5 simulation results are based on 3 different meteorological forcing datasets:
* Clim_CRU: standard global dataset, we used the recent state-of-the-art standrd global dataset CRU-JRA (https://catalogue.ceda.ac.uk/uuid/aed8e269513f446fb1b5d2512bb387ad)

* Clim_CRU*: ClimCRU upraded by downscaling temperature data using a temperature lapse rate of -6.5K/1000m and a high-resolution DEM

* Clim_OSHD: highest level of detail, meteorological forcing generated according to methods developed by the Operational Snow Hydrological Service (OSHD), at 1km spatial and 1hour temporal resolution</li>

 Land-use surface data

This dataset further includes forcing land surface datasets used for the CLM5 simulations at 1km, 0.25° and 0.5° resolution (see folder *surface_landuse_datasets*). For the 1km resolution both the standard global (LU_Gl) and the high-resolution dataset (LU_HR), which includes a higher level of detail and is based on a more up-to-date land use datase, are provided. More details on these two datasets can be found in the above-mentioned publication.

evapotranspiration

display_name: evapotranspiration

forest-productivity

display_name: forest-productivity

high-resolution

display_name: high-resolution

land-cover

display_name: land-cover

land-use

display_name: land-use

oshd

display_name: oshd

snow

display_name: snow

snow-simulation

display_name: snow-simulation

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: Multi-resolution CLM5 simulations across Switzerland


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

en: WPSSetup

en: Scripts that need to be changed to compile WPS (see COSMOWRFdocumentation.pdf, Section 4)

en: StaticData

en:
Scripts to process geographical/static data (see Section 3 in COSMOWRFdocumentation.pdf).
- ascii_to_bin_landuse.f90
- ascii_to_bin_soiltype.f90
- ascii_to_bin_topo_wgs84_ws2sf1.f90
- see_slopes.ncl
- soiltype_writing.py
- toposmooth_wgs84_cosmobound.ncl
- write_geogrid.o

en: COSMO-WRF documentation v1.0

en:
Gerber, Franziska; Sharma, Varun (2018). Running COSMO-WRF on very-high resolution over complex terrain. EnviDat. doi:10.16904/envidat.35

Documentation about the procedure to run COSMO-WRF

en: WRFrun

en: Scripts to run COSMO-WRF simulation (see Section 6 in COSMOWRFdocumentation.pdf)

en: WPSrun

en: Scripts to run the WRF pre-processing (WPS) for COSMO-WRF simulation (see Section 5 in COSMOWRFdocumentation.pdf)

en: Postprocessing

en: Postprocessing scripts to map WRF output on swiss coordinates (see Section 7 in COSMOWRFdocumentation.pdf)

en: MeteorologicalData

en: Scripts to process meteorological data (see Section 2 in COSMOWRFdocumentation.pdf).

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

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

meteoswiss

display_name: meteoswiss

numerical-simulation

display_name: numerical-simulation


en: paleo-climate precipitation glaciers air-temperature orography last-glacial-maximum

en: Technical documentation

en: Technical documentation containing file metadata and variable descriptions.

en: Data Access

en:
High resolution, downscaled climate model data are used in a wide variety of applications in environmental sciences. Here we present the CHELSA-TraCE21k downscaling algorithm to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100 year time steps for the last 21,000 years. Paleo orography at high spatial resolution and at each timestep is created by combining high resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases with the interpolation of a dynamic ice sheet model (ICE6G) and a coupling to mean annual temperatures from CCSM3-TraCE21k. Based on the reconstructed paleo orography, mean annual temperature and precipitation was downscaled using the CHELSA V1.2 algorithm.

The data is published under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

format: GeoTIFF

air-temperature

display_name: air-temperature

glaciers

display_name: glaciers

last-glacial-maximum

display_name: last-glacial-maximum

orography

display_name: orography

paleo-climate

display_name: paleo-climate

precipitation

display_name: precipitation

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: CHELSA-TraCE21k: Downscaled transient temperature and precipitation data since the last glacial maximum


maintainer: Jana von Freyberg

en: stable-water-isotopes ecohydrology plant-water-uptake review uncertainties endmember-mixing-models root-zone

en: Data set of MEE-20-04-264

en: The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal ‘Methods in Ecology and Evolution’.

en: The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal 'Methods in Ecology and Evolution'.

ecohydrology

display_name: ecohydrology

endmember-mixing-models

display_name: endmember-mixing-models

plant-water-uptake

display_name: plant-water-uptake

review

display_name: review

root-zone

display_name: root-zone

stable-water-isotopes

display_name: stable-water-isotopes

uncertainties

display_name: uncertainties

en: Data set of: Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources


en: land-use-change functional-diversity plants phylogenetic-diversity migclim global-change conservation beta-diversity zonation european-alps species-distribution-models taxonomic-diversity

en: Updated SDM outputs

en: Refined scenarios of plants distribution in the European Alps

en:
This repository contains extensive data for the European Alps:
  - Observations of ~3,500 plant species
  - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal)
  - Flora migration rates (categorical) and ecological preferences (continuous indicator values)
  - Regional maps of barriers to migration and water bodies at 100-m resolution
  - Sampling effort, distance to roads and cities predictors at 100-m resolution
  - Present and future abundances over the study region at 1-km resolution (~2,000 species)
  - Present and future multifaceted and uniqueness of the European Alps' Flora at 1-km resolution
  - Present and future conservation recommendations at 1-km resolution (26 current and future strategies)
  - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees)
  - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them

beta-diversity

display_name: beta-diversity

conservation

display_name: conservation

european-alps

display_name: european-alps

functional-diversity

display_name: functional-diversity

global-change

display_name: global-change

land-use-change

display_name: land-use-change

migclim

display_name: migclim

phylogenetic-diversity

display_name: phylogenetic-diversity

plants

display_name: plants

species-distribution-models

display_name: species-distribution-models

taxonomic-diversity

display_name: taxonomic-diversity

zonation

display_name: zonation

en: Present and future multifaceted plant diversity, uniqueness and conservation in the European Alps


maintainer: Rebecca Mott

en: dischmex snow-patch turbulent-sensible-heat-fluxes patchy-snow-covers eddy-covariance-measurements

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 1.7 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.6 m.

en: Output explanation

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 3.2 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.2 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (DA-600, Kaijo-Denki) in 0.3 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.3 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.5 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 0.7 m.

en: Gletschboden setup

en: Background map based on data © 2014/2016 swisstopo (JD100042)

mimetype: image/png

en:
This dataset contains eddy-covariance measurements in the ablation period of 2014-2016. Measurements were taken from two turbulence towers over a long-lasting snow patch, which are 5 m apart from each other (2014 and 2015). The turbulence towers were equipped with two YOUNG ultrasonic anemometers mounted 0.7 m (in 2014) and 3.3 m (in 2015) above snow-free ground, two ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) mounted at 2.6 m (in 2014) and 2.2 m (in 2015) above snow-free ground and one anemometer (DA-600, Kaijo Denki) mounted at 0.3 m above snow surface.

The measurement setup changed in 2016 and includes a measurement above the snow-free ground in upwind direction (Swiss coordinates: 790191/176689). The measurement tower is equipped with one ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 3.3 m above the snow-free ground. Additionally, one measurement tower is installed above the long-Lasting snow patch and equipped with the same setup as 2015.

Turbulence data were sampled at a frequency of 20 Hz. The processing of the data to quality controlled fluxes has been done with the Biomicrometeorology flux software (Thomas et al., 2009). The program applies plausibility tests and a despiking test after Vickers and Mahrt (1997) on the measured data. The routine further applies a time-lag correction and considers the deployment (e.g. the sonic azimuth). A frequency response correction (Moore, 1986) is done and a three-dimensional rotation is performed. Finally, quality assurance/quality control (QA/QC) flags after Foken et al., (2004) are issued and fast Fourier transform power and co-spectra are calculated. The change in snow height is considered in the post-processing for every measurement day. The turbulence data were averaged to 30 minute intervals.

dischmex

display_name: dischmex

eddy-covariance-measurements

display_name: eddy-covariance-measurements

patchy-snow-covers

display_name: patchy-snow-covers

snow-patch

display_name: snow-patch

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

en: DISCHMEX - Impact of extreme land-surface heterogeneity on micrometeorology over spring snow-cover


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

en: CHELSA Technical Documentation

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

en: Data Access

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

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

air-temperature

display_name: air-temperature

century

display_name: century

climate

display_name: climate

climate-layers

display_name: climate-layers

free-climate-data

display_name: free-climate-data

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

timeseries

display_name: timeseries

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Gesa von Hirschheydt

en: biodiversity cryptogams observer-error sessile-species detection-probability swisslichens monitoring occupancy-model bayesian-inference detectability inter-observer-difference lichens

en: Repository containing the full data, code, README and workflow files.

en:
The available lichen data consists of detection/nondetection data (1/0) of 373 tree-inhabiting (*corticolous*) lichen species from 416 plots surveyed 1-2 times. The lichen data were originally collected for the purpose of the Red List of epiphytic lichen species in Switzerland ([Scheidegger *et al.* 2002](https://www.bafu.admin.ch/bafu/de/home/themen/biodiversitaet/publikationen-studien/publikationen/rote-liste-gefaehrdete-arten-baum-erdbewohnende-flechten.html)), but updated to recent nomenclature for the purpose of this study.
This repository contains all the supporting data and R code for the paper:
von Hirschheydt, G., Kéry, M., Ekman, S., Stofer, S., Dietrich, M., Keller, C., Scheidegger, C. (2024) **Occupancy model reveals limited detectability of lichens in a standardised large-scale monitoring**. *Journal of Vegetation Science*.
Results and figures presented in the manuscript should be reproducible (with small differences in the latter digits due to stochasticity of the MCMC sampler) with the provided data and code.
The downloadable `.zip` folder has the following structure:
* `0_data/`
* `1_code/`
* `2_output/`
* `lichen_detectability.Rproj`
* `README.txt`
* `workflow.html`
- `workflow.Rmd`

The main folder and the three subordinate folders each have their own `README*.txt` file. These describe each available file in detail and should be consulted prior to using the data or running any code. The file `lichen_detectability.Rproj` stores the information about the R project. The user can open the project by clicking/double-clicking on this file which will automatically define the repository as working directory for the R session. If the user does not use RStudio/Posit, they may have to set the working directory manually to the stored location in the R files. The files `workflow.*` guide the user through the analysis (`1_code/*.R`) in the correct order so that they can:
- bundle the cleaned data into a data list readable for JAGS
- fit the multi-species occupancy model to the data and store the output
- assess the goodness-of-fit of the model to the data
- conduct a prior sensitivity analysis with 2 additional sets of priors
- extract the summary statistics reported in the manuscript and supplementary materials
- generate the figures shown in the manuscript and supplementary materials

bayesian-inference

display_name: bayesian-inference

biodiversity

display_name: biodiversity

cryptogams

display_name: cryptogams

detectability

display_name: detectability

detection-probability

display_name: detection-probability

inter-observer-difference

display_name: inter-observer-difference

lichens

display_name: lichens

monitoring

display_name: monitoring

observer-error

display_name: observer-error

occupancy-model

display_name: occupancy-model

sessile-species

display_name: sessile-species

swisslichens

display_name: swisslichens

en: Repeated detection-nondetection data of corticolous lichens from a standardised monitoring across Switzerland


maintainer: Lina Torregroza

en: european-landscape-convention landscape landscape-perception ecosystem-services landscape-ecology

en: This is the general readme for all LABES datasets.

en:
The social indicators for LABES were collected with a Swiss-wide representative survey. The first survey was conducted in 2011 (n = 2814 participants), the second in 2020 (n = 2090 participants). The “Auswertungsprotokolle” inform about the results of each indicator in 2020 and compared to the state of 2011.
Indicator list:
Indicator 23 Ortsbindung / place attachment
Indicator 24 Landschaftliche Schönheit / visual landscape quality or beauty
Indicator 25 Besonderheit (Eigenart und Vergangenheitsbezug)/ place identity
Indicator 27 Wahrgenommener Informationsgehalt der Landschaft: Komplexität, Entdeckungspotenzial, Kohärenz, Lesbarkeit / Kaplans’ preference matrix: complexity, mystery, coherence, legibility

Indicator 29 Faszination / soft fascination
Indicator 30 Authentizität /authenticity
Indicator Neu_16 Erholung und Landschaft (Erholungsindex, Zugang, Outdoorsport) / recreation and landscapes (recreationindex, access, outdoor sports)
Indicator Neu_17 Beobachtete Landschaftscharakterisierung / perceived landscape charachterization
Indicator Neu_18 Beobachtete Landschaftsveränderungen / perceived landscape changes
Indicator Neu_20 Wahrgenommene Landschaftsqualität in den Grundnutzungen oder Texturen (Wald, Landwirtschaft, Siedlungsflächen, Gewässer)/ Perceived landscape quality in the different land use areas (forest, agriculture, settlement areas, water bodies)
Indicator Neu_22 Gesamturteil zur wahrgenommenen Landschaftsqualität / overall satisfaction
Indicator Neu_23 Kulturelle Landschafswerte / cultural values and services of the landscape

en: Indicator 8 quantitatively describes the Euclidean distance from each point of a 12.5x12.5m grid outside the building zone to the nearest building. This is a revision based on a new calculation method.

en: Indicator 1 quantitatively describes the forest area of Switzerland and its change. The required data on forest area are calculated using the area statistics (Swiss Federal Statistical Office, SFSO).

en: Indicator 31a describes the accessibility of water bodies in Switzerland. Water bodies are popular recreational areas. In order to be used by the population, therefore they should be freely accessible.

en: This indicator shows the agricultural intensity in Switzerland using satellite data by calculating the NDVI values for each growing season and, in particular, observing the standard deviation of these values over the years. The remote sensing data used to calculate this indicator come from Sentinel-2 MSI and cover the whole of Switzerland. Sentinel-2 is a wide-area, high-resolution, multispectral imaging mission of ESA for monitoring vegetation, soil and water cover. Sentinel-2 Level-1C data were used, which are radiometrically and geometrically corrected top-of-atmosphere products. The corrections include orthorectification and spatial registration on a global reference system (combined UTM projection and WGS84 ellipsoid) with sub-pixel accuracy. Level 1C images are delivered in tiles of 100×100 km. A total of 11 tiles are needed to cover the extent of Switzerland.

en: The degree of imperviousness indicates the sealed surface area as a percentage. The LABES indicator 7 is surveyed with the area statistics Switzerland. The survey interval of the areal statistics is 12 years for the first 3 surveys and 9 years for the fourth survey of the areal statistics; therefore, short-term developments in the change of the sealed surface cannot be recorded.

en: This indicator quantitatively describes land consumption for settlement and the change in Switzerland's settlement area based on data from Switzerland's land use statistics (Swiss Federal Statistical Office FSO).

en: Indicator 35 describes the accessibility with footpaths and hiking trails less than 2m wide. The basis for this is the Topographic Landscape Model (TLM and TLM3D) of the Federal Office of Topography. The data are given in km/km2 and are broken down by land use. The land use categories settlement, forest and open land are extracted from the area statistics of the Federal Statistical Office. Contrary to the initial calculation in 2015, we have abandoned the official attribute "hiking trail", as it has been shown that official "hiking trails" are also routed over longer distances along major roads. The current definition has to do solely with the width of the path (max. width 2m) and not with the official designation. This is closer to the quality of a trail than the official designation "hiking trail".

en: This indicator shows the status of transmission lines in Switzerland and analyses the land cover on which these infrastructures are built. Transmission lines are assessed on the basis of TLM data. An extract from TLM_VERSORGUNGSBAUTE (OBJEKTART: Hochspannungsleitungen) is used to calculate the length consumption. In addition, the area statistics layer (NOLC04) is used to differentiate land types into different categories. The total length of transmission lines in Switzerland, in the different biogeographical regions and in the different cantons is calculated by overlay operations for year 2015 and year 2020 (see appendices for further details).

en: Areas left to nature are areas in which natural processes can take place essentially undisturbed and direct human influences determine the (current) development only to a small extent. In such areas, the composition of animal and plant communities differs from that in the cultivated landscape. The areas left to nature in Switzerland can be divided into "wilderness areas" in the forest (forest wilderness), areas left to nature in the high mountains (alpine fallow) and areas along water bodies (floodplains). The areas may or may not be accessible. Of central importance is the intensity of human activities.

en: As a metric for characterising urban sprawl, the measure of urban sprawl (Z or weighted urban permeation WUP) was first propagated by Jaeger et al. (2008). We use this measure here. Urban sprawl is expressed in DSE/m2. The metric was refined within the framework of the National Research Programme 54 "Sustainable settlement and infrastructure development" in the project "Landscape sprawl Switzerland - Quantitative analysis 1935 to 2002 and implications for spatial planning" (Jaeger et al. 2008).

en: This indicator shows the situation of wind energy in Switzerland. Firstly, we locate the current wind turbines and wind farms in Switzerland and compare their location with the wind speed map and the federal government's base map concerning the main wind potential areas. Secondly, we analyse the development of wind energy in Switzerland over the years and the efficiency of such structures.

en: Indicator 4 describes the length and area of roads and railroads for the year 2020, as well as the changes between different points in time. The calculation of length and area consumption is based on the Topographic Landscape Model TLM and TLM3D of the Federal Office of Topography. Before 2014, changes were recorded in a sampling procedure based on landscape elements and extrapolated to subspaces and the whole of Switzerland. Since 2015, however, it has been possible to use data directly for the whole of Switzerland.

en: Indicator 2 describes the quantitative change in Switzerland's agricultural area excluding summer pastures. It contributes to assessing the basic agricultural use, which is very important for the landscape. This has a significant impact on the character of the landscape and on biodiversity.

en: Transport infrastructure such as highways, connecting roads and railroads, as well as settlement areas divide a landscape into individual areas separated from each other. To measure the degree of landscape fragmentation, the metric of "effective mesh size" (meff) was used (Jaeger 2000). It expresses the probability that there are no separating elements, such as traffic routes or settlements, between two randomly selected points. The more separating elements cut up the landscape, the lower this probability and the smaller the effective mesh size.

en: Summer pastures make up the majority of extensively used agricultural land in Switzerland. Summer pastures are located at altitudes that do not allow intensive use of the land, as is the case in the Central Plateau. Fertiliser use is either non-existent or limited. The indicator helps to measure changes in a basic agricultural use that is very important for the mountain landscape. It is a major determinant of landscape character and biodiversity.

en:
This indicator shows the state of vegetation within urban areas in Switzerland. On the one hand, the entire vegetation period is considered, taking into account all data from mid-May to mid-September. On the other hand, an additional analysis focuses on the spring period from mid-May to the end of June in order to observe only the growing season, which in the vast majority of cases is not yet affected by drought stress.
The remote sensing data used to calculate this indicator come from Sentinel-2 MSI and cover the whole of Switzerland. Sentinel-2 is a wide-area, high-resolution, multispectral imaging mission of ESA for monitoring vegetation, soil and water cover. Sentinel-2 Level-1C data were used, which are radiometrically and geometrically corrected top-of-atmosphere products. The corrections include orthorectification and spatial registration on a global reference system (combined UTM projection and WGS84 ellipsoid) with sub-pixel accuracy. Level 1C images are delivered in tiles of 100×100 km. A total of 11 tiles are required to cover the extent of Switzerland.

en: Light emissions in 500m resolution for entire Switzerland for the periode 2014-2020 using VIIRS-VCSML

en: For many people, "untouched landscape" is the very essence of nature. There are hardly any places left in Switzerland that have no human influence. But you can find regions where human influence is not so obvious. These are areas where no human buildings and installations can be seen. Areas free of facilities are therefore the most natural areas that can be found in Switzerland.

en: This indicator measures the average number of different uses of agricultural land in Switzerland. It is thus a measure of the diversity of agricultural land. The agricultural structure survey is used to record the diversity of use.

en: Indicator 11a describes the change in the length of watercourses in Switzerland between different points in time. The changes are systematically recorded on the basis of the changes in the national maps 1:25,000, later Vector25 and from about 2012 using TLM3D. With this evaluation protocol (2022), the sampling procedure of "Landschaft unter Druck" is used for the last time. It consists of 152 sampling rectangles in which the changes are recorded and extrapolated to the whole of Switzerland. From TLM3D (2020) onwards, the extrapolation is guaranteed for the entire area of Switzerland.

en:
Remoteness implies distance from a reference. In the case of an actual journey through a remote landscape, this distance can mean the physical distance from society or from signs of civilisation. In solitude, individuals face the elements of nature and thus move a step away from the backing of security provided by civilised society.
To calculate the remoteness of each grid cell per time period, the "Path Distance" ArcGIS tool is used. This calculates the cost between each grid cell and the settlement areas (pixels with >15 people) as the shortest trajectory. The surface distance is calculated from the horizontal and vertical distance (including the digital elevation model DHM25) and a cost grid, which assigns to each cell the costs incurred when passing through this area. As soon as the trajectory meets (or starts on) a road on which the settlement can be reached, the journey time by public transport or by private means of transport is calculated and the shorter of the two journey times is used.

en:
The Swiss Landscape Monitoring Program (LABES) records both the physical and the perceived quality of the landscape with about 30 indicators. The surveys of the physical aspects are largely based on evaluations of data available throughout Switzerland from swisstopo and the Federal Statistical Office (FSO). Another significant part of the data comes from a nationwide population survey on landscape perception.
This dataset describes data that have been assembled in the 2020 update of the Swiss Landscape Monitoring Program LABES.

ecosystem-services

display_name: ecosystem-services

european-landscape-convention

display_name: european-landscape-convention

landscape

display_name: landscape

landscape-ecology

display_name: landscape-ecology

landscape-perception

display_name: landscape-perception

en: LABES 2 Indicators of the Swiss Landscape Monitoring Program


dischmex-meteorological-measurements

title_for_slug: dischmex-meteorological-measurements

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

en: Meteorological measurements 2016

en: Location of the meteorological stations

en: Meteorological measurement 2014

en: data

en: Meteorological measurements 2014

en: Overview sensors

en: Meteorological measurement 2015

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: Armin Sigmund

en: stable-water-isotopes snow-sublimation antarctica isotopic-distillation backward-trajectories evaporation lagrangian-analysis

en: Model code

en: Model Sublimation and Model Air Parcel (python scripts)

en: Data

en: Modeled data including the data shown in the figures of the related research article

en: Readme

en: Meta data

en: Python scripts for data visualization

en: Scripts for generating the figures of the related research article and generating the data files (csv) from the model output

en:
 Summary

This data set contains Python programming code and modeled data discussed in a related research article. We developed a simple isotope model to study the drivers of the particularly depleted vapour isotopic composition measured on the ship of the Antarctic Circumnavigation Expedition close to the outlet of the Mertz glacier, East Antarctica, in the 6-day period from 27 January 2017 to 1 February 2017. The model considers the stable water isotopologues H2(16O), H2(18O), and HD(16O). It uses data from the ERA5 reanalysis product with a spatial resolution of 0.25° x 0.25° (Hersbach et al., 2018) and 10-day backward trajectories for the location of the ship, published by Thurnherr et al. (2020a). Our data set includes the model code, Python scripts for visualizing the results, and data produced by the model including the results shown in the figures of the related research article. Here, we summarize the most important model characteristics while further details can be found in the readme.txt file and the related research article including its supporting information.

 Main model characteristics

The modeling approach consists of two steps called *Model Sublimation* and *Model Air Parcel*. The former estimates the isotopic compositions of the snow and sublimation flux across the Antarctic Ice Sheet using an Eulerian frame of reference while the latter models the vapour isotopic composition and specific humidity along air parcel trajectories using a Lagrangian frame of reference. The isotope effects of most phase changes are represented by equilibrium fractionation. Only for ocean evaporation, kinetic fractionation is additionally taken into account (original Craig-Gordon formula). For snow sublimation, two assumptions are tested: *Run E* assumes that sublimation is associated with equilibrium fractionation while *Run N* assumes that sublimation occurs without isotopic fractionation.

 Model Sublimation
Model Sublimation uses a simple one-dimensional mass-balance approach in each grid cell, considering snow accumulation due to snowfall and vapour deposition and snow ablation due to sublimation. The snowpack is represented by 100 layers of equal thickness (e.g., 1 cm) and density (350 kg m-3). The isotopic composition of snowfall is parameterized by generalizing a site-specific, empirical relationship between the daily mean air temperature and snowfall isotopic composition. In the case of vapour deposition, Model Sublimation assumes equilibrium fractionation and estimates the isotopic composition of the atmospheric vapour as the average value for two idealized situations: (i) locally sourced vapour which has the same isotopic composition as the sublimation flux; (ii) non-locally sourced vapour in isotopic equilibrium with snowfall. Model Sublimation is run with a time step of 1 h, independently of Model Air Parcel.

 Model Air Parcel
Every hour, an ensemble of trajectories arrives at different heights in the ABL above the ship. For each of these trajectories, we consider an air parcel with a constant volume of 1 x 1 x 1 m3. The air parcels are initialized at the first suitable time when the trajectories are located in the ABL, either over the ice-free ocean in conditions of evaporation or over snow (Antarctic Ice Sheet or sea ice). Subsequently, the masses of the water isotopologues in the air parcels are simulated with a time step of 3 h, considering vapour uptake or removal due to the moisture flux at the snow or liquid ocean surface (only if the parcel is in the ABL) and cloud/precipitation formation (if the saturation specific humidity is reached). Sea ice is taken into account in a very simplified way. We represent the sea ice by grid cells with a sea-ice cover of more than 90% and assume the isotopic composition of the sublimation flux to be identical to that in the nearest grid cell of the Antarctic Ice Sheet. The isotopic composition of the sublimation flux is taken from Model Sublimation whereas the isotopic composition of the vapour deposition flux (over snow) and condensation flux (over ice-free ocean) is simulated assuming an isotopic equilibrium with the air parcel. Isotope effects of cloud/precipitation formation are represented using the classic Rayleigh distillation model with equilibrium fractionation, where the cloud water is assumed to precipitate immediately after formation.

 References

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J.,... others (2018). *ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. doi: 10.24381/cds.bd0915c6

Thurnherr, I., Wernli, H., & Aemisegger, F. (2020a). *10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017*. Zenodo. doi: 10.5281/zenodo.4031705

antarctica

display_name: antarctica

backward-trajectories

display_name: backward-trajectories

evaporation

display_name: evaporation

isotopic-distillation

display_name: isotopic-distillation

lagrangian-analysis

display_name: lagrangian-analysis

snow-sublimation

display_name: snow-sublimation

stable-water-isotopes

display_name: stable-water-isotopes

en: Modeled Isotopic Composition of Water Vapour Along Air Parcel Trajectories in the Antarctic