A study of channel geometry - discharge relationships in semi-natural British rivers as a basis for river restoration and management.

PhDRiver restoration has developed over the last three decades in the context of a more holistic approach to river management. One of the most important issues facing river managers is the design of river channel dimensions. The successful design of cross-sectional dimensions requires an understanding of river channel stability and the sensitivity of rivers to change. A need for more work in this field was identified. This research investigates the variability of downstream channel geometry discharge as a basis for assessing river channel stability and sensitivity to change. A national database containing 124 semi-natural UK river sites is used to develop a linear regression model relating bankfull width with dominant discharge. The magnitude and direction of standardised residual values from the model are then investigated in terms of their geomorphological significance. Particular groups of residual values are found to be related to specific controlling factors. Extreme high magnitude positive residuals (>1.0) are dominated by baseflow dominated chalk rivers. Negative residuals (>-0.5 to -1.0) are found to have a bedrock control. Other controlling factors operating at a local scale, including bank materials, bed materials and vegetation cannot be identified as having an exclusive influence on residual values. The variability in channel geometry - discharge relationships is broadly indicative of river channel adjustment based results from the field study of a subset of 50 sites. Residuals closest to the regression line demonstrate active or inactive stability and negative and positive residuals show a tendency towards erosional and depositional processes respectively. A study of temporal changes in a subset of 16 rivers supports these findings whilst highlighting the importance of the mutual adjustment of channel parameters through time. To investigate river channel adjustment at a reach downstream within a single river, a more detailed catchment study forms the second part of the research. Three contrasting catchments were used to investigate the implications of changing downstream channel geometry and stability on river channel adjustment at a reach. The results reinforce the importance of assessing the mutual adjustment of width, depth, and gradient to identify the dominant form of adjustment at a reach. Drainage basin form was found to exert an important control on channel geometry adjustment both laterally and longitudinally downstream. The results support the findings from the national model which show that stability is a function of complex combination of controlling factors which are represented by the residual values. River channel stability can be viewed as a function of the flow regime related to local environmental factors. The variability of channel geometry - discharge relationships does appear to represent the direction of river channel adjustment, but stability at a reach must be evaluated in the context of the adjustment of downstream parameters related to both catchment and local scale controls

Getting the most out of maths : how to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK

In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics

Getting the most out of maths: How to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK.

In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics"