GI1.3 | Instrumentation & measurements for water systems
Instrumentation & measurements for water systems
Co-organized by ESSI4/HS13
Convener: Andrea Scozzari | Co-conveners: Francesco Soldovieri, Anna Di Mauro

Instrumentation and measurement technologies are currently playing a key role in the monitoring, assessment and protection of water resources.
This session focuses on measurement techniques, sensing methods and data science implications for the observation of water systems, emphasizing the strong link between measurement aspects and computational aspects characterising the water sector.
This session aims at providing an updated framework of the observational techniques, data processing approaches and sensing technologies for water management and protection, giving attention to today’s data science aspects, e.g. data analytics, big data, cloud computing and Artificial Intelligence.
Building a community around instrumentation & measurements for water systems is one of the aims of the session. In particular, participants to the EGU2020 edition of this session contributed to this book: A. Di Mauro, A. Scozzari & F. Soldovieri (eds.), Instrumentation and Measurement Technologies for Water Cycle Management, Springer Water, ISBN: 978-3-031-08261-0, 2022.
We welcome contributions about field measurement approaches, development of new sensing techniques, low cost sensor systems and measurement methods enabling crowdsourced data collection.
Therefore, water quantity and quality measurements as well as water characterization techniques are within the scope of this session. Remote sensing techniques for the monitoring of water resources and/or the related infrastructures are also welcome. Contributions dealing with the integration of data from multiple sources are solicited, as well as the design of ICT architectures (including IoT concepts) and of computing systems for the user-friendly monitoring of the water resource and the related networks.
Studies about signal and data processing techniques (including machine learning) and the integration between sensor networks and large data systems are also very encouraged.