Integrated Monitoring Systems for Coastal Communities

Abstract

With approximately 40 percent of the world's population living within 100 km of the coast, it is critical that the ocean environment upon which these people depend be persistently monitored. Fortunately, coastal communities are ideally situated to take the leading role in this effort by leveraging their knowledge and understanding of the surrounding environment and by taking advantage of existing community infrastructure to support a range of complementary monitoring systems. Ocean Networks Canada (ONC) operates ocean observatories on the west and east coasts of Canada and the Arctic and continuously gathers data in real-time for scientific research that helps communities, governments, and industry make informed decisions about the future. ONC is best known for its NEPTUNE and VENUS cabled observatories located off-shore southern British Columbia, and for the Oceans 2.0 data management system that supports ocean sensor health monitoring and troubleshooting, asset management, data capture, QA/QC, metadata, data delivery, data products, and web services. Building on the experience and expertise garnered from the installation and maintenance of VENUS and NEPTUNE, and on the data management foundation provided by Oceans 2.0, Ocean Networks Canada has advanced its capacity to monitor the ocean. ONC's world leading understanding of cabled community observatories and community-led monitoring programs leverages advancements in mobile oceanographic and meteorological instruments by making use of purpose-built mobile application software. The Cambridge Bay, NU observatory has provided a number of instrument manufacturers with opportunities to test and demonstrate their technologies under extreme Arctic conditions while simultaneously providing a wide range of persistent, real-time data to both the global science and local Arctic communities. Since the installation in 2012, the growing use of the Cambridge Bay observatory physical infrastructure and data has increased reliance on its continued successful operation and increased demand on its capabilities. In fact, support for this particular community observatory continues to grow with Cambridge Bay residents providing logistic assistance for observatory maintenance, participating in outreach and formal learning activities, and, more recently, collecting critical, winter-time data on snow depth and ice thickness. Its success enabled the expansion of a number of community observatories across the BC coast, as part of ONC's Smart Ocean SystemsTM program. Meanwhile, data collected by community members themselves (commonly referred to as “citizen science”) is a concept that continues to evolve with changing technology, data demands, and collection techniques. This method has remarkable potential in coastal communities, as the people who live there are, without question, the best suited to be collecting data in the field. Their understanding of the local environment and the risks and challenges of working in their unique coastal environments remains unparalleled, allowing them to capture critical data in a relatively large area through all of the seasons. Moreover, Arctic research is estimated to cost eight-times that of work in temperate regions [1]. A community approach tends to be cost effective by taking advantage of local resources, providing an often-overlooked connection between the data and the people it affects the most, and it has the potential to integrate local and traditional knowledge into the decision-making process. Mature citizen science programs, such as those being supported and co-developed by ONC, endeavor to leverage this community knowledge and to pair it with the funds and person time needed to supply the equipment and training to a population that is extremely capable and willing. This contribution will discuss the range of technologies and instrumentation used in ONC's community observatories and citizen science programs. It will also present the relationship between the communities, the technology, and the data that are produced.