FUSE: Floodplain Underground SEnsors- A high-density, wireless, underground Sensor Network to quantify floodplain hydro-ecological interactions

Improved understanding of the functioning of hydrological systems and dependent ecology is essential for optimal environmental management. Floodplains in particular are important due to the ecosystem services they provide. The species composition of floodplain vegetation and their ecosystem functions (e.g. leaf CO2 uptake and transpiration) are very sensitive to the soil hydrological regime, which is highly variable both spatially and temporally. The hydrological regime also affects the temperature and nutrient regime of the root environment, leading to indirect impacts on vegetation. However, the mechanisms controlling these interdependencies are not well established. The proposed project, FUSE, aims to advance this knowledge at a variety of scales. A better understanding of these vulnerable ecosystems will allow improved environmental management, under current and future conditions. A field study is proposed in the Oxford Floodplain (OFP). This study will build upon an existing hydrological monitoring network currently in place in the Oxford Meadows Special Area of Conservation (SAC). The aims will be achieved by a sophisticated combination of environmental data and computer models. This involves state-of-the-art tools: a Wireless Underground Sensor Network (WUSN) and related monitoring of environmental variables, as well as high-resolution Earth Observation (EO, i.e. satellite) data. WSNs are a relatively recent application of technology; uptake of this technology by environmental scientists enables continuous monitoring that is both scalable and less intrusive on its surroundings. It is desirable for land-based sensor networks to have few or no above-ground components, for aesthetic and security reasons, as well as to avoid interference with land management practices. Recently, this has led to the introduction of WUSNs where all or at least the majority of the sensing and transmitting components are underground. WUSNs are rare, especially in the UK, and have not been tested long-term in a challenging environment such as the OFP. Reliability and the potential distance of data transmission depend on a number of factors, including the soil type, sensor installation depth, soil moisture content and technological factors. These will be researched extensively in the FUSE project, initially using existing data on the OFP hydrological regime, soils and vegetation height/density. The precise design of the WUSN will be determined with the aid of a geostatistical procedure. FUSE will allow researchers to reliably measure underground spatial variability at hitherto unachievable resolutions of less than a metre. The project will use a mesh of simple wireless sensor nodes previously developed at Imperial College ('Beasties'). These nodes will gather environmental data, and route these to a base-station that transmits to a remote database via GPRS. The low-cost, low-power Beasties have been used extensively in similar, but less challenging environments. The enhanced sensor technology will be entirely transferable. Theoretical tools in FUSE comprise of a simulation model (SCOPE_SUB), that can be used to describe and predict the interaction between the soil (soil moisture content, soil temperature and nutrient status), the vegetation (root water/nutrient uptake, CO2 uptake and transpiration), and the hydrometeorological regime. Furthermore we will use geospatial models to spatially interpolate between measured, modelled and EO data, thereby increasing data-density. EO data will serve to guide the continuous (in time) simulation model predictions. In that way high resolution maps of key soil and vegetation variables can be constructed. Computer Science tools, e.g. a so-called Integrated Development Environment to help environmental scientist to set up and test the WUSN, and a Web portal for quality control, sensor calibration, time series- and geospatial-analysis, parameter estimation and real-time model output, will be developed.