Scaling connectivity science in fluvial systems

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This project considers issues of connectivity in relation to sediment transport in fluvial systems, with a particular focus on networks containing bedrock-alluvial channels. A key aspect of this project will be consideration of what forms the fundamental units of a network, and how this can be considered across different scales. The network units that will be investigated are sediment grains, channel bars, exposed bedrock and river reaches. The key question is to what extent an understanding of the structure of these units is necessary to understand sediment fluxes at larger scales. The project will use both field data and numerical modelling. A range of field techniques (e.g. terrestrial laser scanning, structure-from-motion) will be used to collect nested datasets to quantify the structure of these units within one or more UK or international river networks. Field monitoring will also be undertaken to establish sediment mobility within these units. Numerical modelling of network-scale sediment fluxes with varying levels of process representation will be undertaken to address the identified objectives.

Objectives

  1. To determine the level of process understanding required at one scale in order to predict behaviour at the larger scale;
  2. to identify whether a (predictive) framework can be developed that incorporates connectivity within and between the different scales;
  3. to assess can simple measurements be used to predict future events, e.g. as predictions of efficacy of neurofeedback or other intervention.

Expected Results

  1. Identification of key network units at different scales in fluvial systems;
  2. Understanding of the extent to which considering a range of scales aids system prediction;
  3. Development of a connectivity framework that can be used to integrate systems operating at a range of scales;
  4. Biomarkers for predictions for future behavioural changes, e.g. neurofeedback or other intervention efficacy from resting state measurements using existing EEG data at AAISCS collected as part of the Horizon 2020 project SmokeFreeBrain.

Secondments

Host Months Aim

Ronald Pöppl, UNIVIE

16-19

UNIVIE (RP): Training in river network analysis, water and sediment dynamics and network evolution (landscape evolution modelling and GIS-based approaches).

Andreas Ioannides, AAISCS

24-26

Multiscale connectivity and common field role in brain function. Analysis of resting state EEG data before and after neurofeedback sessions and correlation of candidate biomarkers (combinations of measures at different temporal scales) with smoking cessation.

Other Positions in Structures and Properties

ESR 11

AAISCS (Cyprus)

Connectivity within network processes and coupling with global flows

ESR 14

University of Groningen (Netherlands)

Understanding the emergence of connectivity science in practice: a network of network colleagues

ESR 15

Durham University (United Kingdom)

Use connectivity science to determine the fate (source-pathway-interceptors) of specific diffuse chemicals and pathogens in the water supply chain

ESR 6

Durham University (United Kingdom)

Scaling connectivity science in fluvial systems