Potential of Wireless Sensor Networks for Measuring Soil Water Content Variability

TLDR

Soil water content is crucial for land‑surface water and energy fluxes but has not yet been measured operationally at large scales. This study aimed to assess the feasibility of using the wireless sensor network SoilNet for near‑real‑time monitoring of soil water content at field and catchment scales. The Wüstebach forest catchment (∼27 ha) was equipped with 150 end devices and 600 EC‑5 soil moisture sensors from the ECH 2 O series. Over six million measurements revealed that high‑density sensor data matched previous studies, showed reduced variability at 50 cm depth, highlighted topographic control during dry periods, and confirmed that the network captured key spatial patterns with low scatter between mean and variance.

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 ECH 2 O 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.