en: R data set containing R raster objects with 500m gridded daily modeled soil moisture and net radiation covering Switzerland for the year 2004.

en: evaporation ecohydrology soil-moisture evapotranspiration net-radiation

en: daily 500m gridded net radiation and soil moisture for Switzerland, 2004

de:
Resource Title: R data file containing gridded net radiation and soil moisture values
    Size: 683413598 bytes
    Format: application/x-r-data
    URL: https://www.envidat.ch/dataset/8d398d73-d741-42a3-997a-b63e9893aa9b/resource/d931db26-e4ce-42b3-b8fa-1e38a13a31ba/download/500m_sm_netrad_2004_for_hess_raster.rdata
    Description: Net radiation (W/m2), variable name: NetRadiation, source: Viviroli et al., 2009, spatial resolution: 500 m,
temporal resolution: daily, year 2004, extent: Switzerland.

Soil moisture saturation (-), variable name: SoilMositure, source: simulations from the hydrological model PREVAH,
Brunner et al., 2019; Speich et al., 2015; Orth et al., 2015; Zappa et al., 2003, spatial resolution: 500 m, temporal
resolution: daily, year 2004, extent: Switzerland.

The R command to load the data:
load(“500m_SM_NetRad_2004_for_HESS_raster.RData”)

de:
Resource Title: simple R script to load the data and generate sample plots
    Size: 1368 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/8d398d73-d741-42a3-997a-b63e9893aa9b/resource/b52e1813-276e-49e1-a373-1a5e2677dc42/download/plotting-script-for-hess.r
    Description:

ecohydrology

display_name: ecohydrology

evaporation

display_name: evaporation

evapotranspiration

display_name: evapotranspiration

net-radiation

display_name: net-radiation

soil-moisture

display_name: soil-moisture


$oid: OBSOLETE

en:
This data set contains the data presented in the figures 1-6 in Walthert et al. (2020): From the comfort zone to crown dieback: sequence of physiological stress thresholds in mature European beech trees across progressive drought. Science of the Total Environment. DOI: 10.1016/j.scitotenv.2020.141792.
A detailed methodical description of the data can be found in the Material and Methods section of the paper.
Drought responses of mature trees are still poorly understood making it difficult to predict species distributions under a warmer climate. Using mature European beech (Fagus sylvatica L.), a widespread and economically important tree species in Europe, we aimed at developing an empirical stress-level scheme to describe its physiological response to drought. We analysed effects of decreasing soil and leaf water potential on soil water uptake, stem radius, native embolism, early defoliation and crown dieback with comprehensive measurements from overall nine hydrologically distinct beech stands across Switzerland, including records from the exceptional 2018 drought and the 2019/2020 post-drought period. Based on the observed responses to decreasing water potential we derived the following five stress levels: I (predawn leaf water potential --0.4 MPa): no detectable hydraulic limitations; II (-0.4 to -1.3): persistent stem shrinkage begins and growth ceases; III (-1.3 to -2.1): onset of native embolism and defoliation; IV (-2.1 to -2.8): onset of crown dieback; V (--2.8): transpiration ceases and crown dieback is -20%. Our scheme provides, for the first time, quantitative thresholds regarding the physiological downregulation of mature European beech trees under drought and therefore synthesises relevant and fundamental information for process-based species distribution models. Moreover, our study revealed that European beech is drought vulnerable, because it still transpires considerably at high levels of embolism and because defoliation occurs rather as a result of embolism than preventing embolism. During the 2018 drought, an exposure to the stress levels III-V of only one month was long enough to trigger substantial crown dieback in beech trees on shallow soils. On deep soils with a high water holding capacity, in contrast, water reserves in deep soil layers prevented drought stress in beech trees. This emphasises the importance to include local data on soil water availability when predicting the future distribution of European beech.

en: stem-growth tree-water-deficit transpiration xylem-vulnerability leaf-water-potential stem-shrinking native-embolism early-defoliation drought-stress stress-thresholds stable-isotopes permanent-wilting-point dendrometer soil-water-uptake soil-water-potential fagus-sylvatica crown-dieback

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: Stress thresholds of mature European beech trees

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

format: XLS

crown-dieback

display_name: crown-dieback

dendrometer

display_name: dendrometer

drought-stress

display_name: drought-stress

early-defoliation

display_name: early-defoliation

fagus-sylvatica

display_name: fagus-sylvatica

leaf-water-potential

display_name: leaf-water-potential

native-embolism

display_name: native-embolism

permanent-wilting-point

display_name: permanent-wilting-point

soil-water-potential

display_name: soil-water-potential

soil-water-uptake

display_name: soil-water-uptake

stable-isotopes

display_name: stable-isotopes

stem-growth

display_name: stem-growth

stem-shrinking

display_name: stem-shrinking

stress-thresholds

display_name: stress-thresholds

transpiration

display_name: transpiration

tree-water-deficit

display_name: tree-water-deficit

xylem-vulnerability

display_name: xylem-vulnerability


en: Laboratory experiments are presented on the phase change at the surface of sodium chloride – water mixtures at temperatures between 259 K and 240 K. High selectivity to the upper few nanometres of the frozen solution – air interface is achieved by using electron yield near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. We present the NEXAFS spectrum of the hydrohalite.

en: snow antarctica sea-salt-aerosol ice arctic

en: Data set on interfacial supercooling and the precipitation of hydrohalite in frozen NaCl solutions by X-ray absorption spectroscopy

de:
Resource Title: Fig 1C
    Size: 4075 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/72da8863-8f75-4cc6-8f19-e0794aba1825/download/fig1datac.txt
    Description: Data to Figure 1C

format: TXT

media_type: text/plain

de:
Resource Title: Fig 1B
    Size: 4055 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/e34c6b10-2a60-47ff-9861-c4532f9f6cd5/download/fig1datab.txt
    Description: Data in Figure 1B

de:
Resource Title: Fig 1B_2
    Size: 5115 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/ddfc21ed-441b-457d-b97f-b798769fe961/download/fig1datab_phoenix04042016_007.txt
    Description: Individual data to mean shown in Figure 1B

de:
Resource Title: Fig 1E
    Size: 4834 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/b5116736-f648-48fc-b151-84f9e7425b02/download/fig1datae.txt
    Description: Data to Figure 1E

de:
Resource Title: Fig 1F
    Size: 4852 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/4066e023-0d99-4ee8-9b28-7dcc5bae6e6a/download/fig1dataf.txt
    Description: Data to Figure 1F

de:
Resource Title: Read Me
    Size: 127587 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/06ee35fe-4ee5-4813-812c-5600ac828b25/download/bartelsrausch_cryosphere_nacl_2020-data-repository.pdf
    Description: Description of the data tables

format: PDF

media_type: application/pdf

de:
Resource Title: Code
    Size: 4438 bytes
    Format: application/vnd.wolfram.mathematica.package
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/4f8edf22-d8cf-42e9-aae8-88cf6d84ceec/download/bartelsrausch_cryosphere_nacl_2020.m
    Description: Matlab code to read and plot the data

de:
Resource Title: Fig 1D
    Size: 4831 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/b483a523-00ab-46ae-8dd4-12a80135158a/download/fig1datad.txt
    Description: Data to Figure 1D

de:
Resource Title: Fib 1B_3
    Size: 4849 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/83a80299-55d3-423e-9fbb-e5b0a962dd5d/download/fig1datab_phoenix04042016_020.txt
    Description: Individual data to mean shown in Figure 1B

de:
Resource Title: Fig 1F_1
    Size: 4856 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/927bbb3a-590d-46a1-8cc2-8eef6b2040a9/download/fig1dataf_phoenix04042016_015.txt
    Description: Individual data to mean shown in Figure 1F

de:
Resource Title: Table1
    Size: 15287 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/89079eb2-5bc0-4032-ad8d-41db87a93c81/download/table1.txt
    Description: This table lists all data files in chronological order. "SampleTemp" refers to the reading of the PT-1000 at the side of the sample holder. "cellPressure" is the water vapour pressure during the measurements. From this, in presence of ice, the temperature of the ice sample can be calculated which is given in the column "iceTemp". "dT" is the difference, or temperature offset, between sensor's reading and the ice samples temperature. The offset is due to the sensor being mounted away from the cooled spot of the sample were it experiences heating from warmer parts of the sample holder. The off-set is used to calibrate the temperature sensor and the resulting temperature during the experiments is given in the column "temperature". "relHum" is the relative humidity above (supercooled) liquid and "concBrine" is the concentration of a NaCl solution in mol/l in equilibrium with ice at the experimental temperature. "Area" is the integrated PE signal.

de:
Resource Title: Table 2
    Size: 1399 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/0a3fb7b3-b96d-496a-86e2-00a635c1fdab/download/table2.txt
    Description: This table lists all NEXAFS data files in chronological order with PE carbon to oxygen signal intensity ratios -from which the atomic ratio were calculated in the paper - before ("preCtoO") and after ("postCtoO") each NEXAFS measurement. Also listed is the change in Na ("lossSodi"), O ("lossOxy"), Cl ("lossChlor"), and C ("lossCarb") signal intensity after the NEXAFS measurement compared to prior to the NEXAFS measurement.

de:
Resource Title: Fig 1F_2
    Size: 4855 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/016e32e9-3a1d-433e-95b8-66a0a8cef4ec/download/fig1dataf_phoenix04042016_016.txt
    Description: Individual data to mean shown in Figure 1F

de:
Resource Title: Fig 1B_1
    Size: 4068 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/8b41e95e-ed56-46fd-96ff-1edfd7e95592/download/fig1datab_phoenix04042016_006.txt
    Description: Individual data to mean shown in Figure 1B

de:
Resource Title: Fig 1A
    Size: 5110 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4f67a0e6-accb-40a1-b9f1-3c8f8eebf886/resource/b90f9e5e-d4a1-47ef-8654-659b2ee7734a/download/fig1dataa.txt
    Description: Data Figure 1A

antarctica

display_name: antarctica

arctic

display_name: arctic

ice

display_name: ice

sea-salt-aerosol

display_name: sea-salt-aerosol

snow

display_name: snow


en:
Hydrometerological and ecohydrological time series from Sagehen Creek and Independence Creek, Sierra Nevada, USA, illustrating hydrological responses to daily cycles in snowmelt and evapotranspiration forcing.
Data include 30-minute time series of
- weather variables,
- sap flow fluxes,
- groundwater levels (in two riparian transects of shallow groundwater wells),
- and stream stages (at 12 sites spanning a 500-meter elevation gradient),
and daily time series of
- temperature, precipitation, and snow water equivalent at three nearby snow telemetry stations
- diel cycle index values for groundwater levels and stream stages,
- and MODIS normalized difference snow index (NDSI) and enhanced vegetation index (EVI2) values averaged over selected subcatchments.
Google Earth Engine scripts for extracting the MODIS data are also provided.

en: sap-flow streamflow diurnal-cycle evapotranspiration diel-cycle groundwater snowmelt ecohydrology

envidat

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

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: Daily cycles in solar flux, snowmelt, transpiration, groundwater, and streamflow at Sagehen and Independence Creeks, Sierra Nevada, USA

de:
Resource Title: MODIS EVI2 script
    Size: 5924 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/03332157-eed1-47a2-8eb9-15c4963f66cc/download/google-earth-engine-script-modis-evi2-averages.txt
    Description: Google Earth Engine script for computing MODIS two-band enhanced vegetation index (EVI2) values and averaging them over subcatchments.

de:
Resource Title: 30-minute time series
    Size: 20691717 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/839acf4c-db88-4743-9de4-13859430facf/download/sagehen-and-independence-30min-data-for-archive.txt
    Description: 30-minute hydrometeorological and ecohydrological time series.

de:
Resource Title: MODIS NDSI script
    Size: 5976 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/88a7c3f1-0623-435d-9e93-f9a463b0211d/download/google-earth-engine-script-modis-ndsi-averages-excluding-cloudy-pixels.txt
    Description: Google Earth Engine script for computing MODIS normalized difference snow index (NDSI) values and averaging them over subcatchment areas.

de:
Resource Title: Subcatchment polygons
    Size: 24333 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/5b6edcd5-514b-4928-bf8c-5d1b475b59e9/download/google-earth-engine-subcatchment-polygons.txt
    Description: Subcatchment polygons for use in the Google Earth Engine scripts.

de:
Resource Title: Daily time series
    Size: 758116 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/1273e282-0cf2-4ac1-8676-187bb027ccbe/download/sagehen-and-independence-daily-data-for-archive.txt
    Description: Daily hydrometeorological and ecohydrological time series

de:
Resource Title: Metadata
    Size: 150575 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/e9a393f6-f3fe-4917-b533-249eeefbde5b/resource/2376965d-33fc-4c95-842c-c788d5d22830/download/sagehen-and-independence-data-archive-metadata.pdf
    Description: Column descriptions for both data files, and map coordinates for all measurement locations.

diel-cycle

display_name: diel-cycle

diurnal-cycle

display_name: diurnal-cycle

ecohydrology

display_name: ecohydrology

evapotranspiration

display_name: evapotranspiration

groundwater

display_name: groundwater

sap-flow

display_name: sap-flow

snowmelt

display_name: snowmelt

streamflow

display_name: streamflow


en: Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.

en: raclets temperature wind holoballoon radiosonde relative-humidity wind-direction pressure

en: Radiosondes

envi

de: Umwelt

de:
Resource Title: Radiosond Template Files
    Size: 123564 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f6c38c8c-ab20-43a6-ae37-82117733df96/resource/4c57e9a6-e0b8-430d-964a-710306dadf78/download/template.zip
    Description: The "template" files contain standard parameters, i.e. ambient pressure (mbar and Pa), temperature (°C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), as well as additional information, e.g. time (UTC and local time), dew point (°C), mixing ration (g/kg), density (kg m³), altitude (m MSL), height above ground (ft and m AGL), and geographic coordinates (longitude, latitude). If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

format: ZIP

media_type: application/zip

de:
Resource Title: Radiosond Sounding Files
    Size: 43199 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f6c38c8c-ab20-43a6-ae37-82117733df96/resource/969c41f7-d197-45cc-8db2-259fedd02b9f/download/soundings.zip
    Description: The "soundings" radiosond data contains information on ambient pressure (mbar), temperature (C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), and are ordered with respect to the height above ground (m AGL) with a step size of 20 m. The first line of each file idicates the geographic location and the start time (in UTC) of the ascent. If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

wind

display_name: wind

wind-direction

display_name: wind-direction


en: The data base contains timeseries of stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK. One data set contains weekly stable water isotope data from the Lower Hafren and Tanllwyth catchments, and the other data set contains 7-hourly stable water isotope data from Upper Hafren. Both data sets also include chloride concentrations in precipitation and streamflow.

en: plynlimon stable-water-isotopes deuterium chloride streamflow tracer catchment precipitation

en: Stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK

de:
Resource Title: 7-hourly stable water istopes
    Size: 1068983 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f4e1355d-d645-4419-bf89-67c70dd09dfc/resource/b95e376b-5e72-4b1e-ae70-16a77fbe8b2c/download/plynlimon_isotopes_and_chloride_7hourly_2007_2009.txt
    Description: Data set of 7-hourly stable water isotope data measured at Carreg Wen (precipitation) and Upper Hafren (streamflow).

de:
Resource Title: Data Set Documentation
    Size: 442506 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/f4e1355d-d645-4419-bf89-67c70dd09dfc/resource/e4e4cc20-e46a-404c-88d4-963dce6a00b2/download/dataset_documentation__read_me_first.pdf
    Description: This file contains a documentation of the data sets, including references to other helpful documentations.

de:
Resource Title: Weekly stable water isotopes
    Size: 154294 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f4e1355d-d645-4419-bf89-67c70dd09dfc/resource/caa392e5-341e-412e-ba17-04b5ca2f9ee7/download/plynlimon_isotopes_and_chloride_weekly_2004_2009.txt
    Description: Data set of weekly stable water isotope data collected at Carreg Wen (precipitation) and Lower Hafren and Tanllwyth (streamflow).

catchment

display_name: catchment

chloride

display_name: chloride

deuterium

display_name: deuterium

plynlimon

display_name: plynlimon

precipitation

display_name: precipitation

stable-water-isotopes

display_name: stable-water-isotopes

tracer

display_name: tracer


endsplit

en:
R scripts and demonstration data for end-member mixing and splitting: using isotopes and other tracers to determine where streamflow comes from (end-member mixing) and where precipitation goes (end-member splitting).
 This package includes two R scripts:
"EndSplit_v1.0_20200516.R" implements end-member mixing and splitting.
"EndSplit_demo_v1.0_20200516.R" demonstrates the application of EndSplit to the Hubbard Brook Watershed 3 isotope data set (see below).
Both of these scripts are copyright (C) 2020 ETH Zurich and James Kirchner.  Public use is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/
But… READ THIS CAREFULLY:
ETH Zurich and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose.  Users are solely responsible for determining the suitability and reliability of this software for their own purposes.
These scripts implement end-member mixing and end-member splitting, as described in Kirchner and Allen, "Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis", Hydrology and Earth System Sciences, 24, 17-39, https://doi.org/10.5194/hess-24-17-2020, 2020.  Users publishing results based on these scripts should cite that paper.
Build 2020.05.16 is a minor bug fix of build 2019.10.25, which was previously released as EndSplit_v1.0_20191025.R.
 The zip file "demonstration input data.zip" contains 8 demonstration data files (all tab-delimited text):
"Hubbard Brook WS3 isotope data split by sampling date.txt" contains streamflow and precipitation isotope data from Hubbard Brook Watershed 3 isotope data (Campbell and Green, 2019).
"Hubbard Brook WS3 daily P and Q 1956-2014.txt" contains daily precipitation and streamflow totals for Hubbard Brook Watershed 3.  (USDA Forest Service Northern Research Station, 2016a and 2016b).
"Hubbard Brook WS3 isotope data WY2007.txt", "Hubbard Brook WS3 isotope data WY2008.txt", "Hubbard Brook WS3 isotope data WY2009.txt", "Hubbard Brook WS3 daily P and Q WY2007.txt", "Hubbard Brook WS3 daily P and Q WY2008.txt", and "Hubbard Brook WS3 daily P and Q WY2009.txt" contain subsets of these data for the designated water years.
As the work product of US federal employees, the data in these files are in the public domain.  However, any users of these data should cite the original sources:
Campbell, J. L., and Green, M. B.: Water isotope samples from Watershed 3 at Hubbard Brook Experimental Forest, 2006-2010, https://doi.org/10.6073/pasta/f5740876b68ec42b695c39d8ad790cee, 2019.
USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Daily Streamflow by Watershed, 1956 - present, https://doi.org/10.6073/pasta/38b11ee7531f6467bf59b6f7a4d9012b, 2016a.
USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Total Daily Precipitation by Watershed, 1956 - present, https://doi.org/10.6073/pasta/163e416fb108862dc6eb857360fa9c90, 2016b.
 The zip file "demonstration output files.zip" contains demonstration output files
These tab-delimited text files were generated by running EndSplit_demo_v1.0_20200516.R (which in turn calls EndSplit_v1.0_20200516.R) under R version 3.6.0, using the input files contained in "demonstration input data.zip"

en: end-member-mixing streamflow tracer precipitation end-member-splitting isotopes evapotranspiration

en: EndSplit

title_for_slug: endsplit

de:
Resource Title: EndSplit_demo version 1.0 build 2020.05.16
    Size: 14802 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/b12fafab-7730-4648-a8bc-6afb1944bf3b/resource/19e05849-5bd7-4d0e-9c90-98f9f2d78435/download/endsplit_demo_v1.0_20200516.r
    Description: EndSplit_demo_v1.0_20200516.R demonstrates the application of EndSplit to the Hubbard Brook Watershed 3 isotope data set.  It calls EndSplit_v1.0_20200516.R.

Both of these scripts are copyright (C) 2020 ETH Zurich and James Kirchner.  Public use is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

But… READ THIS CAREFULLY:
ETH Zurich and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose.  Users are solely responsible for determining the suitability and reliability of this software for their own purposes.

These scripts implement end-member mixing and end-member splitting, as described in Kirchner and Allen, "Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis", Hydrology and Earth System Sciences, 24, 17-39, https://doi.org/10.5194/hess-24-17-2020, 2020.  Users publishing results based on these scripts should cite that paper.

de:
Resource Title: demonstration output files
    Size: 14152 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b12fafab-7730-4648-a8bc-6afb1944bf3b/resource/02360e0a-ceec-4bc8-968e-1b17a5a7ad1e/download/demonstration-output-files.zip
    Description: These tab-delimited text files were generated by running EndSplit_demo_v1.0_20200516.R (which in turn calls EndSplit_v1.0_20200516.R) under R version 3.6.0, using the input files contained in "demonstration input data.zip"

de:
Resource Title: demonstration input data
    Size: 387131 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b12fafab-7730-4648-a8bc-6afb1944bf3b/resource/00207c87-63c7-4c9d-86c9-4873bf699035/download/demonstration-input-data.zip
    Description: # The zip file "demonstration input data.zip" contains 8 demonstration data files (all tab-delimited text): #

"Hubbard Brook WS3 isotope data split by sampling date.txt" contains streamflow and precipitation isotope data from Hubbard Brook Watershed 3 isotope data (Campbell and Green, 2019).

"Hubbard Brook WS3 daily P and Q 1956-2014.txt" contains daily precipitation and streamflow totals for Hubbard Brook Watershed 3.  (USDA Forest Service Northern Research Station, 2016a and 2016b).

"Hubbard Brook WS3 isotope data WY2007.txt", "Hubbard Brook WS3 isotope data WY2008.txt", "Hubbard Brook WS3 isotope data WY2009.txt", "Hubbard Brook WS3 daily P and Q WY2007.txt", "Hubbard Brook WS3 daily P and Q WY2008.txt", and "Hubbard Brook WS3 daily P and Q WY2009.txt" contain subsets of these data for the designated water years.

As the work product of US federal employees, the data in these files are in the public domain.  However, any users of these data should cite the original sources:

Campbell, J. L., and Green, M. B.: Water isotope samples from Watershed 3 at Hubbard Brook Experimental Forest, 2006-2010, https://doi.org/10.6073/pasta/f5740876b68ec42b695c39d8ad790cee, 2019.

USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Daily Streamflow by Watershed, 1956 - present, https://doi.org/10.6073/pasta/38b11ee7531f6467bf59b6f7a4d9012b, 2016a.

USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Total Daily Precipitation by Watershed, 1956 - present, https://doi.org/10.6073/pasta/163e416fb108862dc6eb857360fa9c90, 2016b.

de:
Resource Title: EndSplit version 1.0 build 2020.05.16
    Size: 18979 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/b12fafab-7730-4648-a8bc-6afb1944bf3b/resource/bdc05c4b-dd74-4868-b1fb-08cb26107158/download/endsplit_v1.0_20200516.r
    Description: EndSplit_v1.0_20200516.R implements end-member mixing and splitting.

This script is copyright (C) 2020 ETH Zurich and James Kirchner.  Public use is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

But… READ THIS CAREFULLY:
ETH Zurich and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose.  Users are solely responsible for determining the suitability and reliability of this software for their own purposes.

This script implements end-member mixing and end-member splitting, as described in Kirchner and Allen, "Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis", Hydrology and Earth System Sciences, 24, 17-39, https://doi.org/10.5194/hess-24-17-2020, 2020.  Users publishing results based on these scripts should cite that paper.

end-member-mixing

display_name: end-member-mixing

end-member-splitting

display_name: end-member-splitting

evapotranspiration

display_name: evapotranspiration

isotopes

display_name: isotopes


en: Long-term (1980-2011) average annual precipitation (pcp_ch_longterm_yr_avg.tif) and potential evapotranspiration (pet_ch_longterm_yr_avg.tif) at 500m resolution.  Units are mm per year.  Files are GeoTIFF rasters, and can be read in R using the command raster("pcp_ch_longterm_yr_avg.tif), after installing packages "raster" and "rgdal".

en: weather potential-evapotranspiration aridity climate precipitation

envidat

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

de: EnviDat

en: Average precipitation and PET over Switzerland at 500m resolution

de:
Resource Title: 1980-2011 average potential evapotranspiration (in mm per year) at 500m resolution over Switzerland
    Size: 1200978 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/475208bd-42d0-4a12-a1b0-e322f64fce5f/resource/f6c57849-bf7a-4083-b65c-930d13e8e18a/download/pet_ch_longterm_yr_avg.tif
    Description: Long-term (1980-2011) average potential evapotranspiration (in mm per year) at 500m resolution over Switzerland. GeoTIFF raster file can be read in R using the command raster(“pet_ch_longterm_yr_avg.tif”), after installing packages "raster" and "rgdal".

format: TIFF

media_type: image/tiff

de:
Resource Title: 1980-2011 average precipitation over Switzerland at 500m resolution
    Size: 1197880 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/475208bd-42d0-4a12-a1b0-e322f64fce5f/resource/02355670-67a9-4236-ae0f-1bab203db336/download/pcp_ch_longterm_yr_avg.tif
    Description: Long-term (1980-2011) average precipitation (in mm per year) at 500m resolution over Switzerland. GeoTIFF raster file can be read in R using the command raster(“pet_ch_longterm_yr_avg.tif”), after installing packages "raster" and "rgdal".

aridity

display_name: aridity

climate

display_name: climate

potential-evapotranspiration

display_name: potential-evapotranspiration

weather

display_name: weather


en: A Young 81000 sonic anemomenter was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849) to record three components of the wind velocity (u, v, w in [m s&8254; &185;]) and air temperature (Ts in [°C]). The anemomenter was mounted in direction North at a height of 1.5 m above snow surface at the beginning. The time within each data set is given in UTC+1. Instrument specifications can be found [here](http://www.youngusa.com/Manuals/81000-90(I).pdf) .

en: wind-direction wind-speed anemometer wind-velocity raclets wind

en: Snow Drift Station - 3D Ultrasonic

envi

en: Environment

de:
Resource Title: Sonic temperature
    Size: 513827192 bytes
    Format: application/x-npy
    URL: https://www.envidat.ch/dataset/efa1aa57-2aef-41de-8d36-d5847445a2dc/resource/cc5577c8-af52-4af3-a16c-bef536dbe363/download/sonic_ts.npy
    Description: The data set contains values of temperature (°C) measured with the anemometer at a sampling frequency of 20 Hz.

de:
Resource Title: Sonic time
    Size: 513827192 bytes
    Format: application/x-npy
    URL: https://www.envidat.ch/dataset/efa1aa57-2aef-41de-8d36-d5847445a2dc/resource/d345ded1-1574-41f3-ae30-d257dc87b926/download/sonic_time.npy
    Description: The data set contains the time in YYYY-mm-dd HH:MM:SS.sss for the anemometer data. The time is given in UTC+1.

de:
Resource Title: Sonic wind component v
    Size: 513827192 bytes
    Format: application/x-npy
    URL: https://www.envidat.ch/dataset/efa1aa57-2aef-41de-8d36-d5847445a2dc/resource/a485c9f7-72e0-474c-9bc8-3d9aea695a30/download/sonic_v.npy
    Description: The data contains values for v direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

de:
Resource Title: Sonic wind component u
    Size: 513827192 bytes
    Format: application/x-npy
    URL: https://www.envidat.ch/dataset/efa1aa57-2aef-41de-8d36-d5847445a2dc/resource/54108b5f-082b-483b-8ac0-a56780e44217/download/sonic_u.npy
    Description: The data contains values for u direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

de:
Resource Title: Sonic wind component w
    Size: 513827192 bytes
    Format: application/x-npy
    URL: https://www.envidat.ch/dataset/efa1aa57-2aef-41de-8d36-d5847445a2dc/resource/a0d480eb-e00a-4508-9d76-16da99c61b61/download/sonic_w.npy
    Description: The data contains values for w direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

anemometer

display_name: anemometer

raclets

display_name: raclets

wind

display_name: wind

wind-speed

display_name: wind-speed

wind-velocity

display_name: wind-velocity


en: The dataset contains information on precipitation amount and type for Davos Wolfgang (LON: 9.853594, LAT: 46.835577) from February 8 to March 19 2019. It includes: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

en: raclets fall-velocity disdrometer davos precipitation

en: Disdrometer Data Davos Wolfgang

envi

de: Umwelt

davos

display_name: davos

disdrometer

display_name: disdrometer

fall-velocity

display_name: fall-velocity


en: A weather station (Lufft WS600) measured meteorological parameters at Klosters (LON: 9.880413, LAT: 46.869019). Detailed information on the specifications can be found [here](https://www.lufft.com/products/compact-weather-sensors-293/ws600-umb-smart-weather-sensor-1832/productAction/outputAsPdf/).

en: weather temperature wind precipitation klosters raclets

en: Weather Station Klosters

klosters

display_name: klosters

precipitation

display_name: precipitation

temperature

display_name: temperature


en: The RADAR wind profiler from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 2171 m above sea level to 11079 m, with a temporal resolution of 10 minutes.

en: raclets wind-direction wind-speed radar wolfgangpass

en: RADAR Wind profiler Davos Wolfgang

radar

display_name: radar

wind-direction

display_name: wind-direction

wolfgangpass

display_name: wolfgangpass


en: Scanning wind Lidar from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 200 m above ground to 8100 m. The time resolution is up to 5 seconds. The Lidar was measuring wind profiles but also performed plan position indicator (PPI) and range height indicator (RHI) scans.

en: wolfgangpass profiles raclets lidar wind

en: Wind LIDAR Davos Wolfgang

lidar

display_name: lidar

profiles

display_name: profiles


en: A present weather sensor (Vaisala PWD22) was deployed in Klosters (LON: 9.880413, LAT: 46.869019) for weather observation, combining the functions of  a forwardscatter visibility meter and a present weather sensor. Besides measuring ambient light, it detects the intensity as well as the amount of both liquid and solid precipitation. More information can be found in the [User's Manual](ftp://ftp.cmdl.noaa.gov/aerosol/doc/manuals/PWD22_Manual.pdf).

en: visibility precipitation weather raclets sensor

envidat

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

en: EnviDat: the Environmental Data Portal

en: Present Weather Sensor Klosters

sensor

display_name: sensor

visibility

display_name: visibility

weather

display_name: weather


en: Cloud base height (m) and vertical visibility (m) were measured with the VAISALA Ceilometer CL31 in Klosters (LON: 9.880413, LAT: 46.869019). The CL31 is an instrument with constant reliability for all weather conditions and simultaneous detection of three cloud layers in heights up to 7.6 km.

en: klosters vertical-visibility raclets ceilometer cloud-base-height

en: Ceilometer Klosters

envi

en: Environment

ceilometer

display_name: ceilometer

cloud-base-height

display_name: cloud-base-height

raclets

display_name: raclets

vertical-visibility

display_name: vertical-visibility


en: Backward trajectories were calculated from two positions: Davos Wolfgang (LON: 9.85361, LAT: 46.83551) and Weissfluhjoch (LON: 9.80646 LAT: 46.83304) for the time period February 2 until March 27 2019 using COSMO or ECMWF, respectively.

en: cosmo atmospheric-dynamics alps lagrangian-analysis raclets backward-trajectories complex-topopgraphy

en: Backward  Trajectories

alps

display_name: alps

atmospheric-dynamics

display_name: atmospheric-dynamics

backward-trajectories

display_name: backward-trajectories

complex-topopgraphy

display_name: complex-topopgraphy

cosmo

display_name: cosmo

lagrangian-analysis

display_name: lagrangian-analysis


en:
This dataset contains the parameters used in the statistical analyses for the manuscript SREP-19-40170-T, submitted in Scientific Reports. This study is part of the WSL Drought Initiative 2018 (C3 - Analysis of the beech litterfall of the drought year 2018).
Data originate from the Long-term Forest Ecosystem Research Programme LWF (litterfall, soil matric potential, deposition (precipitation) and meteo (temperature)), and from the Swiss Federal Office of Meteorology and Climatology MeteoSwiss (pollen).
Datafile:
 _LWF_beech_plots_litterfall_pollen.xlsx_
1. Sheet _extreme_weather_: values used for analysis of weather conditions in strongest mast years compared to years with fruit abortion.
2. Sheet _weather_and_resource_allocation_: values used for analysis of weather impacts on mast occurrence and resource allocation models.

en: heat beech forest litterfall pollen mast drought soil-matric-potential fruit-abortion

en: Litterfall and pollen data of three LWF beech plots

beech

display_name: beech

drought

display_name: drought

forest

display_name: forest

fruit-abortion

display_name: fruit-abortion

heat

display_name: heat

litterfall

display_name: litterfall

mast

display_name: mast

pollen

display_name: pollen

soil-matric-potential

display_name: soil-matric-potential


en:
This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps).
The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting.
The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.
Additional notes for vector data:
Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API -  https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any  perimeter or area can be digitised freely using the QGIS editing toolbar.
The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:
- Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
- Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif
This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.
Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.
This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

en: seilaplan openstreetmap forest qgis accessible-forest geoprocessing

en: Sample Geodata and Software for Demonstrating Geospatial Preprocessing for Forest Accessibility and Wood Harvesting at FOSS4G2019

de:
Resource Title: Digitised sample perimeter
    Size: 2083 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/710a065d-d50e-4a3d-a993-2a65197cef7b/download/digitized_sample_perimeter.zip
    Description: EPSG:25832

de:
Resource Title: Preview
    Size: 391085 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/729c975c-df90-4af2-979a-af44069dc02f/download/preview.png
    Description:

format: PNG

media_type: image/png

de:
Resource Title: Forest vector data (for sample forest range)
    Size: 288311 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/16db97c5-5546-4f80-9cb9-e562637b589c/download/osm_forest_export_25832.zip
    Description: EPSG:25832

de:
Resource Title: Juypter Notebook for Data Preparation
    Size: 29318 bytes
    Format: application/x-ipynb+json
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/e0c2a4f8-ee8e-4ca9-b05f-0adf2401c879/download/data_preparation_foss4g2019.ipynb
    Description:

de:
Resource Title: DEM (for the area of the sample forest range)
    Size: 1695063 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/1bd80d45-a8fd-44dc-b153-7abfa6345d1c/download/eudem_ogdbayern_25m_25832.tif
    Description: EPSG:25832

de:
Resource Title: Power lines vector data (close to the area of the sample forest range)
    Size: 66908 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/c567f81c-26c9-4122-8fbd-045c55fb73c0/download/osm_powerlines_export_25832.zip
    Description: EPSG:25832

de:
Resource Title: Railways vector data (close to the sample forest range)
    Size: 50776 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/a787f798-0c3d-4cd3-a9d1-c93f51176465/download/osm_railways_export_25832.zip
    Description: EPSG:25832

de:
Resource Title: Juypter Notebook for Data Preparation (online)
    Size: 0
    Format: application/x-ipynb+json
    URL: https://nbviewer.jupyter.org/urls/www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/e0c2a4f8-ee8e-4ca9-b05f-0adf2401c879/download/data_preparation_foss4g2019.ipynb
    Description:

accessible-forest

display_name: accessible-forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan


en:
Naturally, large parts of the Swiss Plateau are characterised by wetlands
and meandering rivers. That this is no longer the case today is the result
of centuries of efforts to obtain dry land. But how did this process take
place? What were the relevant actors and what were their motivations? And
what can be said about the ecological consequences of this development?
In a research project on the history of wetlands in Switzerland since 1700,
we conducted (a) a historical analysis of the development of land use in
wetlands and the actors involved, (b) a historical-cartographic
reconstruction of wetland extent since 1850 and (c) an evaluation of
ecological effects of changes in wetlands on various organisms groups.
The series of GIS layers on wetland history stem from the second part of
the project. The area reconstruction is based on digitized and homogenized
signatures from national map series, as they have been available since
about 1850. Details about the digitalization process and the homogenization
procedures applied ("Rekonstruktionen") are included in Stuber & Bürgi
2019.
Book Citation:
 - Stuber M, Bürgi M (2019) Vom «eroberten Land» zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. "Bristol Schriftenreihe", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.

en: switzerland historical-carthography ecology environmental---history wetlands environmental--policy

envidat

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

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: History of wetlands in Switzerland since 1850

ecology

display_name: ecology

environmental---history

display_name: environmental---history

environmental--policy

display_name: environmental--policy

historical-carthography

display_name: historical-carthography

switzerland

display_name: switzerland

wetlands

display_name: wetlands


en: Mircocosm experiment to identify the individual patterns and controls of C, N, and P mobilization in soils under beech forests. Organic and mineral horizons sampled along a nutrient availability gradient in Germany were exposed to either permanent moist conditions or to dry spells in microcosms and quantified the release of inorganic and organic C, N, and P.

en: forest nitrogen drying-rewetting soil dissolved-organic-matter carbon stoichiometry mineralization phosphorus

envidat

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

en: EnviDat: the Environmental Data Portal

en: Data Broedlin CNP

de:
Resource Title: Data Broedlin-CNP-Release
    Size: 1249259 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9dd9a649-405d-413a-ad44-d859ea084bba/resource/2ec747a1-3a2b-4743-b89f-db13a372c49f/download/broedlin_et_al_cnp_rawdata_v2.xlsx
    Description: Raw data of C-N-P mobilization experiment from Oi, Oe/Oa, and A horizons of beech forest soils along a nutrient availability gradient.

de:
Resource Title: Data Broedlin-CNP
    Size: 1189635 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9dd9a649-405d-413a-ad44-d859ea084bba/resource/c4fce049-b79d-495f-a44d-94f1d59b913a/download/broedlin_cnp_rawdata.xlsx
    Description:

carbon

display_name: carbon

dissolved-organic-matter

display_name: dissolved-organic-matter

drying-rewetting

display_name: drying-rewetting

forest

display_name: forest

mineralization

display_name: mineralization

nitrogen

display_name: nitrogen

phosphorus

display_name: phosphorus

soil

display_name: soil

stoichiometry

display_name: stoichiometry


en: This dataset contains the classification and localization results obtained during the automatic classification of avalanches during the winter season 2017.

en: localization seismic machine-learning monitoring avalanche

en: Automatic Classification of Avalanches

envi

en: Environment

avalanche

display_name: avalanche

localization

display_name: localization

machine-learning

display_name: machine-learning

monitoring

display_name: monitoring

seismic

display_name: seismic


en:
Daily discharge and the related hydro-meteorological variables precipitation, snowmelt, and soil moisture are provided for current (1981-2017) and for future climate conditions (1981-2100) for 307 medium-sized catchments in Switzerland. The catchments have a median catchment area of 117 km². The 307 catchments together form a set representative of the climatological conditions and runoff characteristics in Switzerland. The four variables were simulated at a daily resolution using the hydrological model PREVAH. PREVAH is a conceptual process-based model that was run in this study in its fully distributed version on a 500 m grid (Viviroli et al. 2009a). For the calibration, runoff time series from 140 mesoscale catchments covering the different runoff regimes were used. The model calibration was conducted over the period 1993-1997. Verification was performed on the period 1983-2005 using (i) volumetric deviation (Viviroli et al. 2007) and (ii) benchmark efficiency (Schäfli et al 2007) as objective functions. The calibration and validation procedures are described in detail in Köplin et al. (2010). The parameters for each model grid cell were derived by regionalizing the parameters obtained for the 140 catchments with a procedure based on ordinary kriging (Viviroli et al. 2009b, Köplin et al. 2010).
The calibrated and validated model was then driven with transient meteorological data (precipitation, temperature, radiation, and wind) representing both reference (1981-2017) and future climate conditions (2018-2099). The data were derived from the CH2018 climate scenarios (NCCS 2018) provided by the Swiss National Centre for Climate Services (NCCS). They were obtained from climate experiments produced with different climate modeling chains, consisting of a global and a regional circulation model each, within EUROCORDEX  for three representative concentration pathways (RCP) emission scenarios. Downscaled output of ten climate model chains derived by quantile mapping were considered. The focus was on the chains of the EUR-11 domain with a horizontal resolution of 0.11 degrees (roughly 12.5 km). The climate model chains (GCM, RCM, RCP, and grid resolution) used are listed below:
- ICHEC-EC-EARTH	DMI-HIRHAM5	2.6	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	4.5	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	8.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	2.6	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	4.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	8.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	4.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	8.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	4.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	8.5	EUR-11
*References*:
-	Köplin, N., D. Viviroli, B. Schädler, and R. Weingartner (2010), _How does climate change affect mesoscale catchments in Switzerland? - A framework for a comprehensive assessment_, Advances in Geosciences, 27, 111-119, doi:10.5194/adgeo-27-111-2010.
-	National Centre for Climate Services (2018), CH2018 - _Climate Scenarios for Switzerland_, Tech. rep., NCCS, Zurich.
-	Schäfli, B., and H. V. Gupta (2007), _Do Nash values have value?_, Hydrological Processes, 21, 2075-2080, doi:10.1002/hyp.6825.
-	Viviroli, D., J. Gurtz, and M. Zappa (2007), _The hydrological modelling system PREVAH.  Part II - Physical model description_, Geographica Bernensia, 40, 1-89.
-	Viviroli, D., M. Zappa, J. Gurtz, and R. Weingartner (2009a), _An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools_, Environmental Modelling & Software, 24, 1209-1222, doi:10.1016/j.envsoft.2009.04.001.
-	Viviroli, D., H. Mittelbach, J. Gurtz, and R. Weingartner (2009b), _Continuous simulation  for flood estimation in ungauged mesoscale catchments of Switzerland-Part II: Parameter regionalisation and flood estimation results_, Journal of Hydrology, 377 (1), 208-225, doi:10.1016/j.jhydrol.2009.08.022.

en: climate-change precipitation snowmelt discharge soil-moisture hydrology

en: Simulated future discharge and climatological variables for medium-sized catchments in Switzerland

de:
Resource Title: Shapefile of 307 catchments in Switzerland
    Size: 103266 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/6c1c0a28-a23f-4777-88b2-982841adb9b8/resource/05c1a038-40c2-423a-8ad9-f11b43689cf1/download/catchment_shapes.zip
    Description: Shapefile of the 307 medium-sized catchments in Switzerland. The "gridcode" provides the ID of the catchment used in the datafiles.

de:
Resource Title: Daily simulated data for the reference period
    Size: 31447784 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/6c1c0a28-a23f-4777-88b2-982841adb9b8/resource/a67b4308-25ca-47ea-9aed-f529b8dc7f4d/download/reference.zip
    Description: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the reference period 1981-2017 in mm.

de:
Resource Title: Daily simulated data for the future period
    Size: 1010416415 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/6c1c0a28-a23f-4777-88b2-982841adb9b8/resource/be17bcca-3f34-414f-960e-bcbdd855ba81/download/climate_simulations.zip
    Description: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the period 1981-2100 in mm for the 307 catchments.

climate-change

display_name: climate-change

discharge

display_name: discharge

hydrology

display_name: hydrology

precipitation

display_name: precipitation

snowmelt

display_name: snowmelt

soil-moisture

display_name: soil-moisture


en: The dataset provides simulated 1) precipitation, 2) discharge, 3) soil moisture, and 4) low-flow simulations for 307 medium-sized catchments in Switzerland for the period 1981-2018. The data were simulated using the hydrological model PREVAH in its gridded-version. The simulated time series are provided at daily resolution. A detailed description of the modeling approach can be found in Brunner et al. 2019 submitted to NHESS.

en: prevah hydrological-simulations precipitation discharge drought-2018 soil-moisture low-flow

en: Hydro-meteorological simulations for the period 1981-2018 for Switzerland

envi

de: Umwelt

de:
Resource Title: Discharge
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/4c11fec4-0846-48a0-8dfe-9f7f5a4b574f/download/discharge.txt
    Description: Simulated discharge for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

de:
Resource Title: Shapefile of 307 catchments in Switzerland
    Size: 103266 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/7ebed8a1-98ce-4833-b1dd-64246bd07e5f/download/catchment_shapes.zip
    Description: Shapefile of 307 catchments for which simulations of precipitation, discharge, soil moisture, and low flow are provided. The OBJECTID entry corresponds to the data columns in the textfiles of the simulated data provided.

de:
Resource Title: Soil moisture
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/0ad6127b-f7d6-4356-9ac4-283166385578/download/soil_moisture.txt
    Description: Simulated plant-available soil moisture for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

de:
Resource Title: Precipitation
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/0f551cfd-804d-4f3a-80d9-c33562ea8b45/download/precipitation.txt
    Description: Simulated precipitation for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

de:
Resource Title: Low-flow storage
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/a1338c95-09df-4bce-8d9c-7b059ae2f6a1/download/low_flow.txt
    Description: Simulated low-flow storage for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

prevah

display_name: prevah


en:
The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)).
 Manuscript Abstract:
Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.

en: sediment-transport erosion mars snow model

en: Energy- and momentum-conserving model of splash entrainment in sand and snow saltation

de:
Resource Title:
    Size: 2190 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/82479779-1b20-486f-b4bc-414c6024c25d/download/figure3b.txt
    Description: Datasets of Figure 3b

de:
Resource Title:
    Size: 1434 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/8ab9bafb-62f5-4420-80da-a2a96d22be74/download/figure3a.txt
    Description: Datasets of Figure 3a

de:
Resource Title:
    Size: 3587 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/1f30b883-fc62-4bbf-a8f5-88f716008097/download/figure2.txt
    Description: Datasets of Figure 2

de:
Resource Title:
    Size: 2904 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/709f4330-9a35-4685-aac8-347067a5e483/download/figure4b.txt
    Description: Datasets of Figure 4b

de:
Resource Title:
    Size: 3626 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/6ee21796-08ad-4069-a4cb-da6c35ffede7/download/figure4a.txt
    Description: Datasets of Figure 4a

erosion

display_name: erosion

mars

display_name: mars

model

display_name: model

sediment-transport

display_name: sediment-transport

snow

display_name: snow


en:
The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData).
For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the
state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground - 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths - 15m and smaller than - -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth.
The work is published in:
Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015

en: dsm aerial-imagery avalanche image-matching snow-depth snow-water-equivalent snow-hydrology

envidat

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

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: Snow Depth Mapping

de:
Resource Title: Snow depth map 20150415
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/578e95ce-ffa0-4fc6-8109-d9653b2c622a/download/snowmap_2015_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

de:
Resource Title: Snow depth map 20120320
    Size: 412272526 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/aff41055-8e6e-4287-b52d-2c2fb61633c2/download/snowmap_2012_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

de:
Resource Title: Snow depth map 20160126
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/9229984f-b417-45e5-8dbe-031f295d0406/download/snowmap_20160126_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m
Because of very diffuse light conditions at time of image acquisition the quality of the resulting snow depth values are in some regions quite poor.

de:
Resource Title: Snow depth map 20130415
    Size: 412272526 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/da3ca703-c629-45c0-8dac-e78645b0fa5e/download/snowmap_2013_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

de:
Resource Title: Snow depth map 20160420
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/bc035833-3193-4394-af92-f8d195d5e034/download/snowmap_20160420_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

de:
Resource Title: Snow depth map 20100416
    Size: 412324171 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/4ba96052-9e3e-4b7a-9968-2dcc58fc96c0/download/snowmap_2010_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

de:
Resource Title: Snow depth map 20140417
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/5cf5d3a5-555d-429e-9819-6c93ccce6dfc/download/snowmap_2014_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.18 m

de:
Resource Title: Snow depth mapping in high-alpine catchments using digital photogrammetry
    Size: 8122296 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/3b711e29-cea1-48e8-8de6-a008850c4f94/download/snow-depth-mapping-in-high-alpine-catchments.pdf
    Description:

de:
Resource Title: Snow depth map 20160309
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/ceae68a6-28f9-43f4-8ff5-9715588ade57/download/snowmap_20160309_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

aerial-imagery

display_name: aerial-imagery

avalanche

display_name: avalanche

dsm

display_name: dsm

image-matching

display_name: image-matching

snow-depth

display_name: snow-depth

snow-hydrology

display_name: snow-hydrology

snow-water-equivalent

display_name: snow-water-equivalent


en:
Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap.
In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.

en: fagus-orientalis-lipsky calcium magnesium biogeochemical-cycling potassium

envidat

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

en: EnviDat: the Environmental Data Portal

en: Base cation dynamics in an Oriental beech forest

envi

de: Umwelt

biogeochemical-cycling

display_name: biogeochemical-cycling

calcium

display_name: calcium

fagus-orientalis-lipsky

display_name: fagus-orientalis-lipsky

magnesium

display_name: magnesium

potassium

display_name: potassium


seilaplan

en:
Cable-based technologies have been a backbone for harvesting on steep slopes. The layout of a single cable road is challenging because one must identify intermediate support locations and heights that guarantee structural safety and operational efficiency while minimizing set-up and dismantling costs. Seilaplan optimizes the layout of a cable road by
Seilaplan stands for Cable Road Layout Planner. Seilaplan is able to calculate the optimal rope line layout (position and height of the supports) between defined start and end coordinates on the basis of a digital elevation model (DEM).
The program is designed for Central European conditions and is designed on the basis of a fixed suspension rope anchored at both ends. For the calculation of the properties of the load path curve an iterative method is used, which was described by Zweifel (1960) and was developed especially for standing skylines. When testing the feasibility of the cable line, care is taken that 1) the maximum permissible stresses in the skyline are not exceeded, 2) there is a minimum distance between the load path and the ground and 3) when using a gravitational system, there is a minimum inclination in the load path. The solution is selected which has a minimum number of supports in the first priority and minimizes the support height in the second priority.
The newly developed method calculates the load path curve and the forces occurring in it more accurately than tools available on the market to date (status 2019) and is able to determine the optimum position and height of the intermediate supports.
The reason for the more accurate results of the new tool is the assumption that the skyline is anchored at both end points. Forest cable yarders used in Europe have a skyline that is fixed at both ends. The behaviour of fixed-anchored suspension ropes is very difficult to describe mathematically and cannot be solved analytically. For this reason, simplified linearized assumptions have so far been used in the forestry sector, which corresponds to the behaviour of a weight-tensioned suspension rope and is known as the Pestal method (1961). Weight-tensioned suspension ropes are used for passenger transport.
For the calculation of the load path curve we use an iterative method, which was described by Zweifel (1960) and developed especially for fixed anchored suspension ropes. This makes mathematics much more demanding, but leads to more accurate and realistic results. Since there are no current models which describe the installation costs with adequate accuracy, the solution sought is the one which has a minimum number of supports in the first priority and minimises the support height in the second priority (Figure 2). The presented method is the first one, which starts from a fixed anchored supporting rope and identifies the mathematically optimal column layout at the same time. In contrast to methods that assume a weight-tensioned suspension rope, this approach achieves more realistic solutions with longer spans and lower support heights, which ultimately leads to lower installation costs. Background information on rope mechanics and calculation methods is documented in Bont and Heinimann (2012).
License: GNU, General Public License, Version 2 or newer.
Literature:
Bont, L., & Heinimann, H. R. (2012). Optimum geometric layout of a single cable road. European journal of forest research, 131(5), 1439-1448.

en: cable-planning steep-terrain-harvesting catenary-load-path qgis python cable-yarding software

en: Seilaplan

title_for_slug: seilaplan

envi

en: Environment

cable-planning

display_name: cable-planning

cable-yarding

display_name: cable-yarding

catenary-load-path

display_name: catenary-load-path

python

display_name: python

qgis

display_name: qgis

software

display_name: software

steep-terrain-harvesting

display_name: steep-terrain-harvesting


en: An R Computing Software package which provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG, a new Fortran implementation of 3-PG, serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) -doi:10.1016/S0378-1127(97)00026-1- and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) -doi:10.1016/j.ecolmodel.2015.07.010-.

en: callibration swiss-forest-lab forest-biomass forest-productivity forest-growth-model 3-pg

en: r3PG – An r package for simulating forest growth

de:
Resource Title: r3PG_0.1.6.tar.gz
    Size: 434143 bytes
    Format: application/x-gzip
    URL: https://www.envidat.ch/dataset/62e09c91-4526-4675-bc7e-2431eba72495/resource/26fa1f66-9d30-4e68-a40d-4c474a08d6fb/download/r3pg_0.1.6.tar.gz
    Description:

de:
Resource Title: r3PG source files v 0.1.2
    Size: 353308 bytes
    Format: application/x-tar
    URL: https://www.envidat.ch/dataset/62e09c91-4526-4675-bc7e-2431eba72495/resource/bcd9e0e9-c928-40d9-aeff-f1c35dfa43e8/download/r3pg_0.1.2.tar.gz
    Description:

de:
Resource Title: link to 3PG github R package
    Size: 1024 bytes
    Format: No Info
    URL: https://github.com/trotsiuk/r3PG
    Description: A package source code (development version).

de:
Resource Title: link to 3PG cran R package
    Size: 361472 bytes
    Format: No Info
    URL: https://cran.r-project.org/package=r3PG
    Description: A package source code (latest release).

callibration

display_name: callibration

forest-biomass

display_name: forest-biomass

forest-growth-model

display_name: forest-growth-model

forest-productivity

display_name: forest-productivity

swiss-forest-lab

display_name: swiss-forest-lab


en: Countrywide vegetation height models (VHM) were generated for Switzerland based on Copernicus Sentinel-2 imagery and the digital terrain model (DTM) swissALTI3D from the Swiss Federal Office of Topography swisstopo. A Convolutional Neural Network (CNN) model was trained to estimate the maximum vegetation height at the spatial resolution of the Sentinel-2 pixel of 10 m. Vegetation heights from the spatially higher-resolved VHM Lidar NFI were used as reference data for the CNN training. Within the framework of the Swiss National Forest Inventory (NFI), the VHMs were modelled annually based on available Sentinel-2 imagery from May – September of the respective year. Further details on the creation of the VHM Sentinel NFI can be found in the paper Jiang et al. (2023, https://doi.org/10.1016/j.srs.2023.100099). Contains modified Copernicus Sentinel data.

en: sentinel-2 cnn vegetation-height remote-sensing switzerland nfi forest

envidat

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

en: Vegetation Height Model Sentinel NFI

de:
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    Description:

de:
Resource Title: VHM_Sentinel_NFI_2019
    Size: 796005325 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/1320cdac-5544-47d8-acaf-983fd88744a4/download/landesforstinventar-vegetationshoehenmodell_sentinel_2019_2056.tif
    Description:

de:
Resource Title: VHM_Sentinel_NFI_2021
    Size: 793778198 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/8fc7f306-c788-4b1c-9a17-8dfe946c9681/download/landesforstinventar-vegetationshoehenmodell_sentinel_2021_2056.tif
    Description:

de:
Resource Title: VHM_Sentinel_NFI_2017
    Size: 791212333 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/771519e8-6435-463c-a2cb-57031ee25861/download/landesforstinventar-vegetationshoehenmodell_sentinel_2017_2056.tif
    Description:

de:
Resource Title: VHM_Sentinel_NFI_2020
    Size: 793832708 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/f0496550-ac9a-4dfd-bfea-68e601e9f3f7/download/landesforstinventar-vegetationshoehenmodell_sentinel_2020_2056.tif
    Description:

de:
Resource Title: VHM_Sentinel_NFI_2024
    Size: 799033995 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/284e4292-df10-4227-85b5-1fc7024def5b/download/landesforstinventar-vegetationshoehenmodell_sentinel_2024_2056.tif
    Description:

de:
Resource Title: VHM_Sentinel_NFI_2025
    Size: 789365244 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/cd9f3543-87a3-4eb5-ab05-a4388a7967c4/download/landesforstinventar-vegetationshoehenmodell_sentinel_2025_2056.tif
    Description:

de:
Resource Title: VHM_Sentinel_NFI_2018
    Size: 791849627 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/6f9a20f7-78bf-4cc5-b734-c52d63bbfd0a/download/landesforstinventar-vegetationshoehenmodell_sentinel_2018_2056.tif
    Description:

de:
Resource Title: VHM_Sentinel_NFI_2022
    Size: 794990349 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/12ed6aff-aeed-45f4-aed3-81e17797a8da/download/landesforstinventar-vegetationshoehenmodell_sentinel_2022_2056.tif
    Description:

cnn

display_name: cnn

forest

display_name: forest

nfi

display_name: nfi

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-height

display_name: vegetation-height


en:
**Schweizweite Karte des sky view factors (Kronenöffnungsgrad im Wald) in 5 m Auflösung (Stand LFI VHM 2024)**
This is a dataset for Swiss forestry, for a description in English please see below.
Il s'agit d'un ensemble de données relatives à la sylviculture suisse. Pour une description en français, voir ci-dessous.
Si tratta di una serie di dati relativi alla silvicoltura svizzera. Per una descrizione in francese, vedere sotto.
Dieser Datensatz enthält eine schweizweite Karte mit einer Auflösung von 5 m, die den sky view factor darstellt. In Wäldern gibt sie den Kronenöffnungsgrad während der Vegetationsperiode wieder, ausserhalb von Wäldern zeigt die Karte lediglich den Terrain-Effekt. Wichtig: Der Kronenöffnungsgrad wurde anhand einer 180° Perspektive ermittelt, was dazu führt, dass die Werte nicht direkt vergleichbar sind mit Schätzungen im Feld, welche meistens auf einem kleineren Sichtradius basieren. Der Datensatz wurde mit dem auf synthetischen hemisphärischen Bildern basierendem Raytracing-Canopy-Radiation-Modell (CanRad) berechnet, unter Verwendung des LiDAR-basierten Vegetationshöhenmodells gemäss LFI (Stand 2024). Die Karte stellt die Bedingungen zum Zeitpunkt der LiDAR Datenerfassung dar. Informationen zu den Jahren der LiDAR-Erfassung finden Sie im Abschnitt „Data and ressources“. Der Datensatz wird jährlich aktualisiert, sobald ein neues LFI-LiDAR-Vegetationshöhenmodell verfügbar ist
**Carte nationale du facteur de vue du ciel (degré d'ouverture de la canopée dans la forêt) avec une résolution de 5 m (état IFN VHM 2024)**
Cet ensemble de données contient une carte couvrant l'ensemble du territoire suisse, avec une résolution de 5 m, qui représente le « sky view factor ». Dans les forêts, elle reflète le degré d'ouverture de la canopée pendant la période de végétation ; en dehors des forêts, la carte indique uniquement l'effet du relief. Important : le degré d'ouverture de la canopée a été déterminé à partir d'une perspective à 180°, ce qui signifie que les valeurs ne sont pas directement comparables aux estimations effectuées sur le terrain, qui se basent généralement sur un rayon de vision plus petit. L'ensemble de données a été calculé à l'aide du modèle CanRad (Raytracing-Canopy-Radiation), basé sur des images hémisphériques synthétiques, en utilisant le modèle altimétrique de végétation basé sur le LiDAR selon l'IFN (état en 2024). La carte représente les conditions au moment de la collecte des données LiDAR. Vous trouverez des informations sur les années de collecte LiDAR dans la section « Données et ressources ». L'ensemble de données est mis à jour chaque année dès qu'un nouveau modèle d'altitude de la végétation LFI-LiDAR est disponible.
**Mappa svizzera dello sky view factor (grado di apertura della chioma nella foresta) con risoluzione di 5 m (stato LFI VHM 2024)**
Questo set di dati contiene una mappa a copertura nazionale con una risoluzione di 5 m che rappresenta lo sky view factor. Nelle aree boschive essa riflette il grado di apertura della chioma durante il periodo vegetativo, mentre al di fuori delle aree boschive la mappa mostra semplicemente l'effetto del terreno. Importante: il grado di apertura della chioma è stato determinato sulla base di una prospettiva a 180°, il che comporta che i valori non siano direttamente comparabili con le stime sul campo, che si basano per lo più su un raggio visivo più ridotto. Il set di dati è stato calcolato con il modello Raytracing-Canopy-Radiation (CanRad), basato su immagini emisferiche sintetiche, utilizzando il modello altimetrico della vegetazione basato su LiDAR secondo l'IFN (aggiornato al 2024). La mappa rappresenta le condizioni al momento dell'acquisizione dei dati LiDAR. Le informazioni relative agli anni di acquisizione LiDAR sono disponibili nella sezione «Data and resources». Il set di dati viene aggiornato annualmente non appena è disponibile un nuovo modello di altezza della vegetazione LFI-LiDAR.
**Switzerland-wide map of the sky view factor (canopy openness in forests) with 5 m resolution (as of NFI VHM 2024)**
This dataset contains a map covering the whole of Switzerland with a resolution of 5 m, showing the sky view factor. In forests, it reflects the degree of canopy openness during the growing season; outside forests, the map simply shows the terrain effect. Important: The degree of canopy openness was determined using a 180° perspective, which means that the values are not directly comparable with field estimates, which are usually based on a smaller field of view. The dataset was calculated using the Raytracing Canopy Radiation model (CanRad), which is based on synthetic hemispheric images, utilising the LiDAR-based vegetation height model in accordance with the LFI (as of 2024). The map depicts conditions at the time of LiDAR data acquisition. Information on the years of LiDAR acquisition can be found in the ‘Data and resources’ section. The dataset is updated annually as soon as a new LFI LiDAR vegetation height model becomes available.

en: forestry canopy-openness canrad light-availability lidar

envidat

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: Switzerland-wide maps of sky view factor (canopy openness in forests) at 5 m resolution (as of NFI VHM 2024)

de:
Resource Title: Sky_view_factor_CH_LI_2024.tif
    Size: 1642824991 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/fa3b2b7f-c0f0-4fd0-b04e-78b0b60ebb4b/resource/ca5d0cc0-1838-4e3d-90da-4c5e0b1992ff/download/map_chli.tif
    Description:

de:
Resource Title: Lidar_Befliegungsjahr_VHM_2024.png
    Size: 536750 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/fa3b2b7f-c0f0-4fd0-b04e-78b0b60ebb4b/resource/d55237b2-558a-49c5-a26d-7c558f5a54c8/download/lidar_befliegungsjahr_vhm2024.png
    Description:

canopy-openness

display_name: canopy-openness

canrad

display_name: canrad

forestry

display_name: forestry

lidar

display_name: lidar

light-availability

display_name: light-availability


en:
Monitoring fish diversity through eDNA metabarcoding along the Kinabatangan river
In 2019, water samples were collected in 25 locations along the Kinabatangan in Malaysia. Samples were taken either from a boat or by filtering from the side of the river. At each station, two filtration replicates were performed using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 30 min, during which approximately 30 liters of water were filtered through each filtration capsule. After filtration, the water inside the capsules was removed, and the capsules were filled with 50 ml of conservation buffer for preservation at room temperature. We followed strict contamination control protocols throughout both the fieldwork and laboratory processes, adhering to the guidelines of Valentini et al. (2016). To prevent contamination, each sample was processed using disposable gloves and single-use filtration equipment. We use primers for eukaryote and fish. The MiSeq Reagent Kit v3 (2x75 bp) (Illumina, San Diego, CA, USA) was used for paired-end sequencing at a theoretical sequencing depth of 200,000 reads per sample.
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

en: malaysia teleo edna metabarcoding fish

en: Environmental DNA Freshwater Malaysia Kinabatangan 2019

envi

de: Umwelt

edna

display_name: edna

fish

display_name: fish

malaysia

display_name: malaysia

metabarcoding

display_name: metabarcoding

teleo

display_name: teleo


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.

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

de:
Resource Title: CHELSA Technical Documentation
    Size: 0
    Format: application/pdf
    URL: http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf
    Description: 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.

de:
Resource Title: CHELSA_tech_specification_V1.2.pdf
    Size: 1146678 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/9c9c699a-e57f-49e9-b489-c6abdfa9b53b/resource/1d294699-c738-4a00-8500-2d64d3226819/download/chelsa_tech_specification_v1.2.pdf
    Description:

de:
Resource Title: Data Access
    Size: 3298534883328 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsav1%2F&prefix=chelsa_cruts%2F
    Description: 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.

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


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

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

envidat

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

en: Code of TreeMig Package version 1

envi

en: Environment

dynamic-modelling

display_name: dynamic-modelling

forest-model

display_name: forest-model

gui

display_name: gui

landscape

display_name: landscape

r-package

display_name: r-package