Smart Rivers: Using sensor networks to understand carbon cycling in regulated rivers

Project outline

Rivers play a fundamental role in transporting terrestrial carbon into the world’s oceans where it is sequestered, thereby reducing the release of greenhouse gases (GHG; e.g. carbon dioxide and methane) back into the atmosphere.  The increasing regulation of rivers via damming and reservoir creation however, disrupts this transport and regulation of carbon, resulting in an increase in the release of carbon dioxide back into the atmosphere in addition to altering water quality downstream of regulated rivers1.  Recent studies have also suggested that rivers will play an increasingly important role in global greenhouse gas emissions as a result of increased release of GHG due to climate change2. Although we are beginning to understand the role and importance of inland water bodies in contributing to GHG emissions, our understanding of how river management and modified flow regimes can alter the rate of release is limited.  This is in part due to the current lack of detailed environmental observations relating to the linkages between river management, metabolism and biogeochemical cycling which determine the fate of nutrients and GHG emissions along the aquatic continuum3.

The paucity of knowledge in this field is creating problems for river managers who are under pressure to identify and implement management measures (including environmental flows) as a way of improving river quality and in reaching climate change goals. Current decision making is hindered particularly by our inability to understand the fundamental empirical drivers of GHG emissions, including responses to flow changes, nutrient pulses, change in river temperature and light availability seasonally, across rivers of different sizes and degrees of regulation.  In the past, field sampling and laboratory-based analyses have been limited to yielding results on the timeframe of months-years after flow changes are implemented. However, recent advances in environmental sensor design, datalogging, telemetry and computer science are underpinning the potential for the collection, analysis and visualisation of river ecosystem responses to flow changes in near real-time. For example, recent work has shown how sensors that underpin integrated estimates of river ecosystem metabolism can be used to show immediate effects of sedimentation4 as well as understanding river basin scale metabolic patterns and processes5. Machine learning algorithms can also be adapted to help ‘fill-in’ gaps in river network monitoring programs6.

Figure: The project will entail field work and experimentation in rivers downstream of reservoirs in addition to ‘machine learning’ techniques

This project will design and implement a sensor based monitoring network in selected strategically important water resource catchments. The sensor netwok will include deployment of floating GHG flux chambers, allowing for the quantification of GHG emissions via both diffusion and ebullition.  This will allow the successful applicant to examine the potential of changing river flows, seasonal drivers, and nutrient regimes in controlling GHG emissions across rivers of different degrees of management. A specific focus will be on monitoring changes in river ecosystem primary production, respiration and net metabolism using dissolved oxygen, water temperature, PAR and flow sensors. It will provide recommendations to a major UK water utility, Yorkshire Water, on whether their operations can help the company to achieve good ecological potential downstream of reservoirs in addition to minimising GHG emissions as part of their net zero commitments.

Project Goals

This research will address the potential for monitoring environmental flow changes (e.g. seasonal compensation flows, artificial floods) using sensors, to gain an improved understanding of how water managers can use technology in their efforts to improve the status of heavily modified, regulated river ecosystems. An interdisciplinary approach will allow the student to develop a project that integrates elements of (1) hydrology, (2) physicochemical processes (water quality, temperature), and (3) river functional responses (e.g. primary production, ecosystem respiration, whole system metabolism). There will be opportunities to work alongside UK water companies to inform their decision making through the development of automated data collection, quality control and visualisation techniques. The project will incorporate periods of fieldwork to install and maintain sensors in rivers downstream of reservoirs as well as paired unregulated ‘control’ rivers. Laboratory work will focus on developing automatic data quality control and visualisation techniques, for example to underpin a web interface for use by water managers.


The successful candidate will benefit from inter-disciplinary training in hydrology and aquatic science as part of the River Basin Processes and Management, and the Leeds institute of Data Analytics (LIDA) in the School of Geography. Training at Leeds deals fully with the elements described in the Joint Research Centre statement on skills training for research students. PhD students take modules provided by the staff development unit (e.g. starting your PhD, small group teaching) and a 15-week faculty-training course (covering elements such as planning, critical reading and writing, oral presentations, writing research papers). Students present results and receive constructive feedback from peers in a Research Support Group, from colleagues in the River Basins research group, and at a university postgraduate research day. The student will also benefit from being part of water@leeds, the largest interdisciplinary water centre in any UK university, which runs a postgraduate forum. You will furthermore be integrated into a British Council ‘Wohl Clean Growth Alliance’ grant with Ben Gurion University of the Negev, Israel.

The nature of the project means that the student will be trained in project specific research methods including literature reviews, fieldwork techniques, laboratory water quality analysis, and modelling/statistics for analysing data, both internally and at external workshops. An additional important part of the training will be to attend national and international conferences to present results and gain feedback. The student will be encouraged to submit papers for publication in international journals during the project.


The prospective student should have, or expect to receive, a minimum 2.1 BSc and/or MSc degree in an appropriate discipline, and have interests and experience in most, if not all, of the following topics: hydrology, freshwater ecosystems, computing, the water industry and environmental policy/management. Informal enquiries should be directed to Megan Klaar at Further details about postgraduate research degrees at the School of Geography, University of Leeds can be found here.


  1. Aufdenkampe et al. 2011. Frontiers in Ecology and the Environment 9: 53-60
  2. Campeau & del Giorgio 2013. Global Change Biology 20: 1075-1088
  3. Ward et al. 2017. Frontiers in Marine Science 4:7
  4. Aspray et al. 2017. Ecohydrology 10: e1855
  5. Rodríguez-Castillo et al. 2017. Ecosystems 22: 892–911
  6. Segatto et al. 2021. Ecosystems 24: 1792-1809