Improved forecasting of pollutant flux, erosion and flood deposits across river catchments using satellite and UAV techniques - part of the SENSE Centre for Doctoral Training

Particulates carried through river catchments are a pollutant in their own right, but are also associated with the transport of other contaminants (e.g. microplastics, pesticides, pharmaceuticals, phosphorus). Our ability to map and forecast the sources, transport, and deposition of particulates is essential for catchment management and improved water quality. To support catchment management plans, forecasts of particulate flux will need to account for climate change projections and increased storage of pollutants on floodplains during high precipitation events, and capture changes in discharge (and therefore erosion and deposition) through time and space. Satellite imagery and unmanned aerial vehicle (UAVs) offer the opportunity for detailed assessment of temporal and spatial changes in erosion and deposition from highland to lowland areas of catchments, and the flux of pollutants to estuaries and coastal zones. A recent scenario-modeling study of the River Derwent catchment, Yorkshire, used satellite data to indicate clear spatial and temporal trends in erosion risk (Richardson et al., 2019), which were not identified using existing methods that rely on static land use maps. Satellite-derived maps showed that traditional land-use maps do not capture seasonal variation in erosion risk.
Producing seasonal land use maps that are integrated with UKCP18 climate change projections could allow for adaptable management plans to be developed. However, seasonal variation in agricultural practices, and the timing and location of heavy precipitation events, will complicate the forecasting of particulate transport and deposition. Satellite imagery can also help capture antecedent conditions (e.g., soil moisture), which further complicate the flux of particulates. By combining satellite imagery, UAV surveying, and sediment sampling after large precipitation events, the location of erosion hotspots (‘critical source areas’) across a catchment can be identified, and suspended load concentrations can be estimated from EO imagery.
Ultimately, the student will aim to produce integrated workflows and end-member scenarios using artificial intelligence techniques to help inform where catchment management should be targeted, and whether seasonal interventions should be implemented.

This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org