A case study of biogeophysical and optical water quality indicators-of-change along marine-terminating glaciers including fjördal zones west of Iceland and Greenland

The discharge of particulate and dissolved material into high latitude estuarine systems has been increasing due to melting of ice sheets, sea ice and glaciers. High quality in-situ datasets of biogeophysical and optical water quality variables (WQVs, indicators-of-change) are therefore essential to improve scientific evidence-based knowledge especially of the dynamic changes along marine-terminating glaciers and related remote high latitude waters. Here, a quantification of WQVs is described based on the surveyed surface waters west of Greenland and Iceland between July and August 2012. Measured WQVs included chlorophyll-a, coloured dissolved organic matter (CDOM), Forel-Ule colour Index, hyperspectral radiometric quantities, salinity, Secchi disk depth, suspended particulate material, temperature and turbidity covering a wide geo-spatial gradient. The study also investigated the geo-spatial distribution and trends exhibited by the gathered WQVs. Statistical tests were applied to investigate associations among the WQVs and to establish predictive regression models. The spectral slope of CDOM was observed to share an indirect relationship with the derived absorption coefficient. Study algorithms were evaluated and showcased as alternative solutions to support filling gaps in WQVs measurements by leveraging ocean colour remote sensing as well as classic water clarity tools. The presented research emphasizes the need and contributes towards high quality open-access datasets that can be assimilated in relevant ocean models and observing repositories (e.g., Greenland Ice Sheet - Ocean Observing System). Furthermore, gathering more WQVs from remote high latitude waters using in-situ and remote sensing approaches has potential to improve scientific knowledge about environmental changes linked to the Triple Planetary Crisis. Additionally, large diverse high quality datasets of WQVs will play a role in accurate predictive regression models supported by machine learning for now- and forecasting environmental changes.