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River Robots: Utilizing novel autonomous technologies, observations and models to investigate freshwater coastal dispersion.

Scottish Association for Marine Science

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Prof M Inall , Dr N Banas No more applications being accepted Funded PhD Project (Students Worldwide)

About the Project

Imagine an ocean as thick and viscous as peanut butter, and that is how climate models work! Closer to the coast, with smaller scale models and higher resolution, think of water with the consistency of warm honey. The issue is one of turbulent ‘eddies’ of every size from 100s km to a few mm. Eddies do the mixing; if we do not know about them, then we have to make up for their effects in models by crudely thickening our virtual seas so that they get the large-scale movements about right. Practically, that means artificially high horizontal viscosity and dispersion coefficients, often denoted KH.

This project will develop and use novel autonomous, robotic measuring platforms and state-of-the-art hydrodynamic coastal models to investigate two questions: Q1) How far into the coastal ocean do rivers retain their characteristics? [In the warm honey model, artificially high KH means that they lose their identity far too rapidly and uniformly]; Q2) How, where and when is terrestrially-derived, water-borne material dispersed into the coastal ocean?

Understanding the variation of dispersal of material in the coastal ocean under different scenarios is relevant to, for example, blue carbon budgets, pollutant dilution, marine ecosystem sustainability, and aquaculture regulation. Furthermore, there is a set of specific questions regarding the fate and large-scale effects of multiple (thousands) of smaller, rapidly time-varying (tidal and spate) river inputs compared with large continuous river sources. These specific questions arise because forecast models treat multiple small rivers as single, larger sources or diffuse, freshened boundaries. Neither approach is realistic, particularly in highly mountainous coastlines that house much of the world’s fin-fish aquaculture in Scotland, Norway, Chile or Spain.

It is generally accepted the shelf seas and coastal physics models have two major hurdles still to overcome: getting the salinity right, and getting the stratification right. River input is key to the former, and highly relevant to the latter, both through direct buoyancy input and through optical opacity. Optical properties of riverine input further relate to the sediment, dissolved inorganic carbon, and organic nutrient loading, linking though to fundamental effects on blue carbon budgets, biogeochemistry and the growth conditions for phytoplankton in coastal seas.

Issues surrounding poor knowledge of riverine input have persisted for decades, hampered by lack of small-scale, high-resolution observation, primarily down to lack of appropriate measurement platform. Until very recently no measurement platforms existed capable of continuously observing the littoral zone from entirely fresh shallow rivers to the coastal ocean. This project will have a technology development aspect with the “ImpYak” autonomous surface vehicles (ASV) and EcoSub autonomous underwater vehicles (AUV), addressing precisely this issue.

Specific research questions arising from Q1 and Q2 above:

Riverine identity: how does the horizontal dispersion of river input to a coastal system at contrasting study sites vary with: tidal amplitude, river flow rate, local wind state?

Stratification and ambient coastal properties: how do seasonal patterns of coastal ocean properties (T, S and density) respond to varying riverine fresh or brackish inputs?

Neighbouring coastal morphology: What specific coastal morphological features (e.g. buffs, coves, sills and troughs) of the carefully selected study sites have greatest impact on dispersion of river inputs?

Numerical simulation: How well do models (specifically the Scottish Shelf Model and WeStCOM model) reproduce the above findings, and what model resolution and physics are necessary to capture the highest-order effects? How does domain-scale model dispersion respond to increased accuracy of multiple small, time varying riverine sources? What are the implications for coastal ecosystems of future climatic states with higher/more extreme rainfall patterns?

Funding Notes
Funded by NERC Studentships awarded to the SUPER Doctoral Training Partnership. The SUPER DTP partner Universities are St Andrews University, Aberdeen University, Edinburgh Napier University, Heriot-Watt University, the University of the Highlands and Islands, Stirling University, University of Strathclyde and the University of the West of Scotland. Underpinning these research partners, providing additional training and projects are Marine Scotland, Scottish Natural Heritage, and the James Hutton Institute, among a total of 40 stakeholder organisations including industry and government agencies and international collaborators.

The start date of this project is: 27 September 2021

The 3½ year studentships cover:
• Tuition fees each year (UK fee rate only* - for 2020/21 this amounts to £4,407 for full-time study
• A maintenance grant each of around £15,000 per annum (for full-time study)
• Funding for research training
• Part-time study is an option, with a minimum of 50% of full-time effort being required.

*please note: for International students, there may be funding available to cover the full international tuition fee and this will be discussed at interview. If funds are not available, the candidate will be required to cover the difference in fees – for 2020/21 this difference amounts to £10,932. Annual tuition fees are subject to revision and typically increase by between 1.5-3% per annum.

Applicants should normally have, or be studying for:
• A postgraduate Master’s degree from a degree-awarding body recognised by the UK government, or equivalent, or
• A first or upper second class honours degree from a degree awarding body recognised by the UK government, or equivalent, or
• Other qualifications or experience that affords sufficient evidence of an applicant’s ability to work at the academic level associated with doctoral study.
• Graduate or postgraduate qualification that included a mathematics course/module.

Enquiries (Name, Phone number, Email address)

Project specific enquiries: [Email Address Removed]

General enquiries: Graduate School Office [Email Address Removed]

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