Cosmic-ray soil moisture sensor installed at the Tonzi Ranch Ameriflux site in California
Soil moisture plays a major role in the environment/climate system because the transports of water within the land and at the land-atmosphere interface are strongly dependent on the state of soil water in a region. Despite its importance, lack of soil moisture measurements at various spatial scales has limited our understanding of how individual physical factors control soil moisture dynamics.
The project seeks to identify whether a unified science of land-atmosphere interactions across multiple-scales can be achievable, or if simpler scale-dependent (and sometimes empirical) parameterizations in combination with data assimilation can provide uniquely acceptable predictions of soil moisture dynamics and land surface processes (e.g., evapotranspiration). The AMUSED project has three main objectives: (1) to evaluate the uncertainties of multiple sources of soil moisture observations, including traditional point-scale, satellite remote sensing and the new cosmic-ray technology; (2) to identify key processes that control soil moisture dynamics in land surface models at various spatial and temporal scales; and (3) to provide high-quality soil moisture estimates at hyper-resolution and evaluate impacts on simulated land surface processes for various applications in UK regions.
Federal University of Santa Maria SulFlux site in Brazil
The £330,000 New Investigator grant was awarded by the Natural Environmental Research Council (NERC) to fund the three-year project. Dr Rosolem is the solely Principal Investigator of the project, which has a strong collaborative effort with the Centre for Ecology and Hydrology (CEH), as part of the COSMOS-UK initiative, a national network of cosmic-ray neutron sensors to monitor soil moisture at near real-time in the UK.
AMUSED will therefore expand the notion of operational data assimilation implementation by further introducing model diagnostics in order to identify which model structures or parameterizations are likely to affect state estimation via data assimilation. These findings will enhance our current understanding and the representation of soil moisture and surface processes in numerical weather prediction and climate models in the UK.