Rock and Roll: Passive and automated sensing of fluvial sediment and wood transport (BENNETTUENV18NEX)

Fluvial bedload is a fundamental process by which coarse sediment is transferred through landscapes by fluvial action. Large wood is a major component of many rivers, but its influence on bedload transport is poorly understood. Rivers across the western USA are currently experiencing increased wood loading due to infestation of forests by the mountain pine beetle. This project will investigate the impact of increased wood loading on bedload sediment transport dynamics in a stream within an infected forest.

Individual grains/wood pieces will be tagged with Passive Integrative Transponders (PIT) to track bedload and wood transport and data will be acquired on the impact of wood on bed-particle rest intervals and travel distances for the first time. Unmanned aerial vehicles (UAVs) will be applied and developed to quantify changes in wood loading to the stream and channel geomorphology, and technology will be developed to aid the use of UAVs in forested environments with tight flying corridors.

The student will be part of a large scale PIT tracer experiment of bedload sediment transport in St Louis Creek, an alpine stream in Fraser Experimental Forest (FEF). In August 2016, 1000 PIT-tagged rocks were seeded in the stream. An initial survey one year later found 90% of the rocks, with 30% of these showing movement from their initial seed location to up to 100 m along the river bed. The student will update and analyze a growing database of sediment transport and wood recruitment - from the seeded site and other subwatersheds at FEF. The student will compare data acquired on sediment transport with flow data in order to establish hydrologic controls on sediment transport. Furthermore, they will use an Unmanned Aerial Vehicle (UAV) to assess changes in wood loading to the stream, channel geomorphology and structural controls on particle movement. The student will work to develop UAV anti-collision technology for flying through forested catchments.

The NEXUSS CDT provides state-of-the-art, highly experiential training in the application and development of cutting-edge Smart and Autonomous Observing Systems for the environmental sciences, alongside comprehensive personal and professional development. There will be extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial / government / policy partners. The student will be registered and hosted at UEA and will have the opportunity of spend time at the US Forest Service Rocky Mountain Station in Fort Collins, Colorado, USA and will spend 3 weeks each summer at the newly refurbished Fraser Experimental Forest facility. Specific training will include training in surveying, hydrological measurements, PIT tag technology, UAV in-flight technology and Structure from Motion (SfM) to process UAV-acquired imagery.

Secondary supervisors: Dr Sandra Ryan, U.S. Forest Service, Colorado, USA, Prof Gerard Parr, UEA, Prof David Sear, University of Southampton.

For further information, please visit www.enveast.ac.uk/nexuss

For more information on the supervisor for this project, please go here: [Email Address Removed]
Type of programme: PhD
Start date of project: October 2018
Mode of study: Full time or part time
Length of studentship: 3 years, 8 months

Acceptable first degree: Any Earth or Environmental Sciences discipline, Engineering, Geology, or Geophysics.

Minimum entry requirement: 2:1 or equivalent.