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This article in VZJ

  1. Vol. 9 No. 4, p. 1002-1013
    OPEN ACCESS
     
    Received: Nov 30, 2009
    Published: Nov, 2010


    * Corresponding author(s): h.bogena@fz-juelich.de
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doi:10.2136/vzj2009.0173

Potential of Wireless Sensor Networks for Measuring Soil Water Content Variability

  1. H.R. Bogena *,
  2. M. Herbst,
  3. J.A. Huisman,
  4. U. Rosenbaum,
  5. A. Weuthen and
  6. H. Vereecken
  1. Agrosphere Inst., ICG-4, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

Abstract

Soil water content (SWC) plays a key role in partitioning water and energy fluxes at the land surface and in controlling hydrologic fluxes such as groundwater recharge. Despite the importance of SWC, it is not yet measured in an operational way at larger scales. The aim of this study was to investigate the potential of wireless sensor network technology for the near-real-time monitoring of SWC at the field and headwater catchment scales using the recently developed wireless sensor network SoilNet. The forest catchment Wüstebach (∼27 ha) was instrumented with 150 end devices and 600 EC-5 SWC sensors from the ECH2O series by Decagon Devices. In the period between August and November 2009, more than six million SWC measurements were obtained. The observed spatial variability corresponded well with results from previous studies. The very low scattering in the plots of mean SWC against SWC variance indicates that the sensor network data provide a more accurate estimate of SWC variance than discontinuous data from measurement campaigns, due, e.g., to fixed sampling locations and permanently installed sensors. The spatial variability in SWC at the 50-cm depth was significantly lower than at 5 cm, indicating that the longer travel time to this depth reduced the spatial variability of SWC. Topographic features showed the strongest correlation with SWC during dry periods, indicating that the control of topography on the SWC pattern depended on the soil water status. Interpolation results indicated that the high sampling density allowed capture of the key patterns of SWC variation.

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