In October 2011 the soils and geophysical tomography teams at the BGS installed a network of soil moisture sensors into a hillside in North Yorkshire, UK.
These sensor networks represent a major advance in ways to study soil moisture because they transmit information constantly from the field site back to scientists' computers.
The sensors have been installed according to a nested sampling scheme so that we can examine how variation in soil moisture is affected by factors that operate at different spatial scales.
Eight clusters of sensors (such as the one shown in Figure 1) are spread across the site. Differences in soil moisture readings between these sensors will be due mainly to:
Figure 1 shows how sensors within a cluster can be arranged so that comparisons can be made over distances from 9 metres to 30 centimetres. This will allow us to investigate the importance of finer-scale factors in determining soil moisture content, such as the shape of the land surface and the size of the particles in the soil.
We have installed 96 sensors across the field site in this nested scheme so that we can quantify the effects at different scale of soil type, slope position and other factors on soil moisture following rainfall events.
It is important that we know how soil moisture varies over different spatial scales and over time. For example, the information from this project would enable us to design the most efficient sample network for specific tasks such as monitoring soil water content at field scale, perhaps as input to catchment scale hydrological models, or for monitoring variations over a few metres on unstable ground, where variations in water content may be risk factors for landslides.
This understanding of scale-dependence will also help us to integrate information from sensors like the ones we are deploying at Hollin Hill with other sources of information. For example, satellites can now estimate soil moisture content over large regions (kilometre scale) of the Earth’s surface.
This information will be most useful if it can be combined with direct measurements from an efficiently designed sensor network, and so contribute to a better understanding of feedbacks between the land surface and atmosphere, limitations on crop yield by drought, and how storage of water in soil affects hydrological processes and so the risk of flooding.