Abstract

Environmental data to support the planning and management of water catchments now come from an increasingly diverse range of sources, including manual sampling, digitally instrumented catchments, remote sensing, numerical models, and citizen science. Using data from more than one source potentially offers complementary insights, creating opportunities to address challenges such as investigating multiple chemicals in water quality, predicting fine scale catchment and hydrological changes, and designing sampling strategies that are both financially and environmentally efficient. At the same time, these data streams can differ in accuracy, resolution, structure, and representativeness, shaping what analyses are possible.

This presentation will provide examples of the statistical and data science approaches we are developing at Glasgow, in collaboration with our external partners, to integrate and analyse these heterogeneous datasets and address such environmental challenges. This research is partly funded by the UK Natural Environment Research Council.