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🎤Speaker🎤 Dr Diego Panici Senior Lecturer Centre for Resilience in Environment, Water and Waste (CREWW) @universityofexeter 💡Abstract💡 High-frequency water quality monitoring offers new opportunities to understand how catchments respond to rainfall and runoff events. While these techniques have been widely applied in natural, rural systems, they are less explored in complex catchments influenced by both groundwater and human activities. This study analyses multiple rainfall events using high-resolution measurements of key water quality parameters to identify how different processes interact over time. A new metric, the Crossing Index, is also introduced to describe the complexity of event responses and complement existing hysteresis-based indices. Results show that water quality behaviour is shaped by the combined influence of groundwater buffering, rapid surface runoff, and shifting sediment sources. These findings demonstrate how high-frequency data can uncover short-term dynamics that conventional monitoring misses, providing valuable insights for managing mixed-use catchments and informing new real-time monitoring programmes, such as the forthcoming Section 82 requirements in England and Wales. ✨About Dr Diego Panici✨ I am a Civil and Environmental Engineer specialised in hydraulics and hydrology, with applications to riverine processes to investigate the effects on river geomorphology, infrastructures (e.g. bridges), and water resources. My research interest focuses specifically on understanding and modelling riverine and hydrological processes to address key questions linking water resources and environmental protection, with an emphasis on cross-disciplinarity. I am currently working on river restoration solutions using large wood for enhancing aquatic wildlife habitat and water quality, as well as the impact of ‘uncontrolled’ large wood on stability of hydraulic structures and flood risk. More recently, I started specialising on water resources availability and water security, with specific work in water quality processes and the use of high-frequency data for catchment scale processes, as well as prediction of harmful algal blooms in lakes and reservoirs leveraging multiple data sources and data science.