Svalbard is a critical region for studying the impacts of global environmental change, yet observational data remain sparse due to the constraints of conventional monitoring methods. Emerging technologies, particularly marine robotic platforms, are transforming our capacity to collect high-resolution, spatially distributed oceanographic and environmental data in these remote and challenging environments. This study emphasizes the value of robotic systems in enabling sustained, low-impact data acquisition in polar marine settings. The dataset compiled through these robotic deployments includes measurements of hydrographic properties, pollutant concentrations, and biogeophysical parameters at the land-ocean interface, particularly in fjord and estuarine systems. These observations provide key insights into freshwater discharge, pollutant transport, stratification processes, microbiological modifications and other key parameters supporting the interpretation and validation of satellite-derived products. In addition, in-situ data contribute to the characterization of local environmental variability and to the assessment of anthropogenic pressures on fragile Arctic ecosystems. The work focusing on datasets from recent robotic deployments–specifically during two recent missions, RELOAD (2022) quantifying heavy metals in meltwater from tidewater glaciers in Hornsund Fjord, and ARIVE (2023, SIOS Access) examinting pollutant runoff into Adventfjorden– also highlights the role of data management practices aligned with FAIR (Findable, Accessible, Interoperable, Reusable) principles, ensuring that these unique datasets remain accessible and actionable for future research and long-term environmental monitoring. Overall, the study demonstrates the growing importance of data-centric, robotic-enabled oceanographic missions in advancing our understanding of polar marine systems.
Svalbard at the Forefront of Global Change: The Role of Marine Robotics in Polar Observations
Aracri Simona
Primo
;Odetti Angelo;Bruzzone Gabriele;Caccia Massimo;Di Blasi Davide
;Ferretti roberta;
2025
Abstract
Svalbard is a critical region for studying the impacts of global environmental change, yet observational data remain sparse due to the constraints of conventional monitoring methods. Emerging technologies, particularly marine robotic platforms, are transforming our capacity to collect high-resolution, spatially distributed oceanographic and environmental data in these remote and challenging environments. This study emphasizes the value of robotic systems in enabling sustained, low-impact data acquisition in polar marine settings. The dataset compiled through these robotic deployments includes measurements of hydrographic properties, pollutant concentrations, and biogeophysical parameters at the land-ocean interface, particularly in fjord and estuarine systems. These observations provide key insights into freshwater discharge, pollutant transport, stratification processes, microbiological modifications and other key parameters supporting the interpretation and validation of satellite-derived products. In addition, in-situ data contribute to the characterization of local environmental variability and to the assessment of anthropogenic pressures on fragile Arctic ecosystems. The work focusing on datasets from recent robotic deployments–specifically during two recent missions, RELOAD (2022) quantifying heavy metals in meltwater from tidewater glaciers in Hornsund Fjord, and ARIVE (2023, SIOS Access) examinting pollutant runoff into Adventfjorden– also highlights the role of data management practices aligned with FAIR (Findable, Accessible, Interoperable, Reusable) principles, ensuring that these unique datasets remain accessible and actionable for future research and long-term environmental monitoring. Overall, the study demonstrates the growing importance of data-centric, robotic-enabled oceanographic missions in advancing our understanding of polar marine systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


