Multicompartment and multiscale long‐term observation and research are important prerequisites to tackling the scientific challenges resulting from climate and global change. Long‐term monitoring programs are cost intensive and require high analytical standards, however, and the gain of knowledge often requires longer observation times. Nevertheless, several environmental research networks have been established in recent years, focusing on the impact of climate and land use change on terrestrial ecosystems. From 2008 onward, a network of Terrestrial Environmental Observatories (TERENO) has been established in Germany as an interdisciplinary research program that aims to observe and explore the long‐term ecological, social, and economic impacts of global change at the regional level. State‐of‐the‐art methods from the field of environmental monitoring, geophysics, and remote sensing will be used to record and analyze states and fluxes for different environmental compartments from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere.

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.

[1] Our understanding of short-and long-term dynamics of spatial soil moisture patterns is limited due to measurement constraints. Using new highly detailed data, this research aims to examine seasonal and event-scale spatial soil moisture dynamics in the topsoil and subsoil of the small spruce-covered Wüstebach catchment, Germany. To accomplish this, univariate and geo-statistical analyses were performed for a 1 year long 4-D data set obtained with the wireless sensor network SoilNet. We found large variations in spatial soil moisture patterns in the topsoil, mostly related to meteorological forcing. In the subsoil, temporal dynamics were diminished due to soil water redistribution processes and root water uptake. Topsoil range generally increased with decreasing soil moisture. The relationship between the spatial standard deviation of the topsoil soil moisture (SD ) and mean water content () showed a convex shape, as has often been found in humid temperate climate conditions. Observed scatter in topsoil SD () was explained by seasonal and event-scale SD () dynamics, possibly involving hysteresis at both time scales. Clockwise hysteretic SD () dynamics at the event scale were generated under moderate soil moisture conditions only for intense precipitation that rapidly wetted the topsoil and increased soil moisture variability controlled by spruce throughfall patterns. This hysteretic effect increased with increasing precipitation, reduced root water uptake, and high groundwater level. Intense precipitation on dry topsoil abruptly increased SD but only marginally increased mean soil moisture. This was due to different soil rewetting behavior in drier upslope areas (hydrophobicity and preferential flow caused minor topsoil recharge) compared with the moderately wet valley bottom (topsoil water storage), which led to a more spatially organized pattern. This study showed that spatial soil moisture patterns monitored by a wireless sensor network varied with depth, soil moisture content, seasonally, and within single wetting and drying episodes. This was controlled by multiple factors including soil properties, topography, meteorological forcing, vegetation, and groundwater.

Low‐budget sensors used in wireless soil water content sensor networks typically show considerable variation. Because of the large number of sensors in sensor network applications, it is not feasible to account for this variability using a calibration between sensor response and soil water content. An alternative approach is to split the calibration into two parts: (i) determination of sensor response–permittivity relationships using standard liquids with a defined reference permittivity, and (ii) site‐specific calibration between permittivity and soil water content using a subset of sensors. In this study, we determined sensor response–permittivity relationships for several ECH2O, EC‐5, TE, and 5TE sensors by Decagon Devices (Pullman, WA). The objectives of this study were to determine (i) the sensor‐to‐sensor variability and precision of these sensor types, and (ii) the increase in accuracy when a sensor‐specific calibration is used instead of a single calibration. The results showed that the sensor‐to‐sensor variability was significantly larger than the measurement noise for each sensor type. When a sensor‐specific calibration was used, the RMSE expressed in (equivalent) soil water content ranged from 0.008 cm3 cm−3 for the TE sensor to 0.014 cm3 cm−3 for the EC‐5 sensor in a permittivity range between ∼2 and 35. When a single calibration was used, the RMSE was higher and ranged from 0.01 cm3 cm−3 for the 5TE sensor to 0.02 cm3 cm−3 for the TE sensor. An improvement in accuracy of nearly 0.01 cm3 cm−3 can be reached in the high‐permittivity range for each sensor type by calibrating each sensor individually.

The measurement accuracy of low‐cost electromagnetic soil water content sensors is often deteriorated by temperature and soil bulk electrical conductivity effects. This study aimed to quantify these effects for the ECH2O EC‐5 and 5TE sensors and to derive and test correction functions. In a first experiment, the temperature of eight reference liquids with permittivity ranging from 7 to 42 was varied from 5 to 40°C. Both sensor types showed an underestimation of permittivity for low temperature (5–25°C) and an overestimation for high temperature (25–40°C). Next, NaCl was added to increase the conductivity of the reference liquids (up to ∼2.5 dS m−1 for a permittivity of 26–42, up to ∼1.5 dS m−1 for a permittivity of 22–26). The permittivity measured with both sensors showed a strong and complicated dependence on electrical conductivity, with both under‐ and overestimation of permittivity. Using these experimental data, we derived empirical correction functions. The performance of the correction functions for the 5TE sensor was evaluated using coarse sand and silty clay loam soil samples. After correcting for temperature effects, the measured permittivity corresponded well with theoretical predictions from a dielectric mixing model for soil with low electrical conductivity. The conductivity correction function also improved the accuracy of the soil moisture measurements, but only within the validity range of this function. Finally, both temperature and electrical conductivity of the silty clay loam were varied and a sequential application of both correction functions also resulted in permittivity measurements that corresponded well with model predictions.