Traditionally, freshwater science has approached research from either a 'Rivers' or 'Lakes' perspective on a case-by-case basis. Various levels of governments, citizen science groups, and academics have worked separately to collect their data. The result is multifarious approaches and data sets that are scattered and not integrated.
Stephanie Melles is working with Ontario’s Ministry of Natural Resources and Forests and the Ministry of Environment and Climate Change, in addition to the Federation of Ontario Cottagers Association, to interweave existing ‘big data’ sets with GIS spatial analytics to create predictive models of lake quality for governments and public alike.
Stephanie is also working with a private sector data analytics firm (Eramosa) to analyze heterogeneous data sets on water quality, climate, land use, and fish contaminants in Ontario. The aim is to develop predictive models that relate fish contaminants (e.g., mercury) to broad-scale climatic variation and land use patterns.