return to catalog
Dataset: soilnet.rollesbroich.wc.2014.11.nc
Catalog: https://service.tereno.net/thredds/catalog/RO/catalog.html
dataFormatNetCDF
authoritytereno.net
featureTypeGrid
dataSize244299
idro_swc_scan/soilnet.rollesbroich.wc.2014.11.nc
Access
Preview

Access:

ServiceTypeDescription
OPENDAP Data Access Access dataset through OPeNDAP using the DAP2 protcol.
HTTPServer Data Access HTTP file download.

Viewers:

ViewerTypeDescription
default_viewer.ipynb Jupyter Notebook The TDS default viewer attempts to plot any Variable contained in the Dataset.
Documentation
Dates
Creators
Publishers
Variables
Geospatial Coverage
Time Coverage

Description:

  • rights: CC BY-SA 4.0
  • summary: TERENO Eifel-Rur Observatory. TERENO (TERrestrial ENvironmental Observatories) spans an Earth observation network across Germany that extends from the North German lowlands to the Bavarian Alps. This unique large-scale project aims to catalogue the longterm ecological, social and economic impact of global change at regional level. The central monitoring site of the TERENO Eifel/Lower Rhine Valley Observatory is the catchment area of the River Rur. It covers a total area of 2354 km² and exhibits a distinct land use gradient: The lowland region in the northern part is characterised by urbanisation and intensive agriculture whereas the low mountain range in the southern part is sparsely populated and includes several drinking water reservoirs. Furthermore, the Eifel National Park is situated in the southern part of the Rur catchment serving as a reference site. Intensive test sites are placed along a transect across the Rur catchments in representative land cover, soil, and geologic settings. The Rollesbroich site is located in the low mountain range “Eifel” near the German-Belgium border and covers the area of the small Kieselbach catchment (40 ha) with altitudes ranging from 474 to 518 m.a.s.l.. The climate is temperate maritime with a mean annual air temperature and precipitation of 7.7 °C and 1033 mm, respectively, for the period from 1981 to 2001. The study site is highly instrumented. All components of the water balance (e.g. precipitation, evapotranspiration, runoff, soil water content) are continuously monitored using state-of-the-art instrumentation, including weighable lysimeters, runoff gauges, cosmic-ray soil moisture sensors, a wireless sensor network that monitors soil temperature, and soil moisture at 189 locations in different depths (5, 20 and 50 cm) throughout the study site. Periodically also different chamber measurements were made to access soil or plant gas exchange. Soil water content was determined using the wireless sensor network SoilNet (Bogena et al., 2010) in 15 minute intervals at 87 locations within the southern part of catchment. The SPADE soil water content sensors (Hübner et al., 2009; Qu et al., 2013) were installed at 5 cm, 20 cm and 50 cm depth along a vertical profile. In order to increase the measurement volume and to allow examination of inconsistencies in sensor output (e.g. due to imperfect contact of sensors with the soil matrix), two sensors were installed in parallel at each depth with a distance of ~8 cm. Soil water content measurements outside the physical plausibility range (0.05 to 0.85 cm3cm-3) caused by temporary sensor failure or reduced current supply were identified and flagged. The same was done for soil temperature (-5 and 30 °C). Unreliable measurements were identified and flagged based on the continuity of the time series data. For this, the first derivative of the soil water content time series was used. If the increase in soil water content at a particular time step was larger than two times the standard deviations of the soil water content measurements in the preceding 24 hours, the soil water content measurement was identified and flagged as an unreliable measurement. All the data from the wireless sensor network were visualized to identify the performance of this automatic flagging method. Literature Bogena, H.R., M. Herbst, J.A. Huisman, U. Rosenbaum, A. Weuthen and H. Vereecken (2010): Potential of wireless sensor networks for measuring soil water content variability. Vadose Zone J., 9 (4): 1002-1013, doi:10.2136/vzj2009.0173. Hübner, C., Cardell-Oliver, R., Becker, R., Spohrer, K., Jotter, K., and Wagenknecht, T., 2009, Wireless soil moisture sensor networks for environmental monitoring and vineyard irrigation: Helsinki University of Technology, no. 1, p. 408-415. Qu, W., Bogena, H. R., Huisman, J. A., and Vereecken, H., 2013, Calibration of a novel low-cost soil water content sensor based on a ring oscillator: Vadose Zone Journal, v. 12, no. 2., doi: 10.2136/vzj2012.0139.
  • TERENO Homepage

Dates:

  • modified : 2019-09-30T10:00:15.397Z

Creators:

Publishers:

Variables:

  • Vocabulary [ GRIB-1]:
  • Vocabulary [ ]:
    • SoilTemperature.0.05mSensor1 (degC) = Soil Temperature [degC] in depth 5 cm =
    • SoilTemperature.0.05mSensor2 (degC) = Soil Temperature [degC] in depth 5 cm =
    • SoilTemperature.0.2mSensor1 (degC) = Soil Temperature [degC] in depth 20 cm =
    • SoilTemperature.0.2mSensor2 (degC) = Soil Temperature [degC] in depth 20 cm =
    • SoilTemperature.0.5mSensor1 (degC) = Soil Temperature [degC] in depth 50 cm =
    • SoilTemperature.0.5mSensor2 (degC) = Soil Temperature [degC] in depth 50 cm =
    • SoilWaterContent.0.05mSensor1 (%_Sat) = Soil Water Content [%_Sat] in depth 5 cm =
    • SoilWaterContent.0.05mSensor2 (%_Sat) = Soil Water Content [%_Sat] in depth 5 cm =
    • SoilWaterContent.0.2mSensor1 (%_Sat) = Soil Water Content [%_Sat] in depth 20 cm =
    • SoilWaterContent.0.2mSensor2 (%_Sat) = Soil Water Content [%_Sat] in depth 20 cm =
    • SoilWaterContent.0.5mSensor1 (%_Sat) = Soil Water Content [%_Sat] in depth 50 cm =
    • SoilWaterContent.0.5mSensor2 (%_Sat) = Soil Water Content [%_Sat] in depth 50 cm =

GeospatialCoverage:

  • Longitude: 6.30074 to 12.60994 degrees_east
  • Latitude: 50.62025 to 101.24762 degrees_north

TimeCoverage:

  • Start: 2010
  • End: present
  • Duration: 15.357 years
return to catalog