Data-driven performance assessments for river channel restoration schemes

• Main Author: Cox, Jenny
• Other Authors: Soar, Philip John
• Published: University of Portsmouth 2018
• Subjects: 550
• Online Access: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749273

River restoration is a developing global industry and science working to improve river health. Monitoring river restoration projects is critical to confirm that this practice is benefitting river health. Data-led monitoring has often been neglected due to resource constraints. Technological advancements have recently presented opportunities to improve the uptake of data-driven performance assessments for river restoration schemes. However, there appear to be few examples of these technologies being applied outside of academia. Therefore, this research aims to explore and present guidance on how cost-effective data collection, analysis and communication of geomorphological and physical habitat datasets may be routinely undertaken within industry. A review of emerging technologies suggested that the Acoustic Doppler Current Profiler may be an effective tool for river restoration monitoring. The feasibility of this was evaluated by undertaking a data-driven performance assessment of the River Rother Habitat Enhancement Scheme, West Sussex. The scheme was assessed over an 18month period and was found to be successful in achieving its overall objective of improving spawning habitat restoration within the targeted reach. Through utilising this technology and catchment baseline data, recommendations for the future sustainable management of the River Rother were outlined. Data collection using the Acoustic Doppler Current Profiler was easy and efficient but the data processing and analysis components of this research required a significant investment of time and technical knowledge. This is likely to be a substantial barrier for widespread data-driven performance assessments beyond academia. The future development of open source software may go some way to alleviate these issues and improve the feasibility of such monitoring approaches. High resolution datasets afford the opportunity for more accurate results and the development of excellent visual dissemination tools. These may foster learning amongst both technical and non-technical stakeholders. This thesis presents the concept of a performance tracking framework for river restoration schemes relative to their objectives. The concept is presented such that, with development, it could be integrated with existing learning platforms to improve opportunities for non-technical experts to track river restoration performance over time and highlight any needs for further restoration.