Grant: Discovery Grants Program - Individual

Soil erosion has been identified as one of the most serious threats leading to deteriorating water quality and lowering of crop yields throughout the world. Soil erosion and associated sediment-related impacts - such as sedimentation of reservoirs and salmonid spawning gravels, and the transport of pollutants attached to the sediment - cost billions of dollars each year to the Canadian economy. With an increasing population, and changes in climate and related wildfires and floods, these costs are expected to increase. Determining the origin of the erosion will allow for the mitigation of soil and channel bank erosion, leading to cost savings. Sediment fingerprinting is an emerging technique that directly links problematic sediment to its source by using key diagnostic properties or tracers like sediment colour, geochemistry and fallout radionuclides. The long-term objective of this research program is to improve and expand the sediment source fingerprinting technique by advancing the use of some recently identified tracers and by testing the use of other tracers in a novel way. Work will focus on two watersheds Quesnel (British Columbia, BC) and South Tobacco Creek (Manitoba) which are representative of contrasting conditions (e.g. mountainous with forests/grassland vs flat with cropland), and where a considerable amount of complementary data exists. One project will improve the use of compound-specific stable isotopes (CSSIs) as tracers to identify sediment coming from specific vegetation types, such as certain grasses or crops. A second project will evaluate the application of persistent organic pollutants (POPs, like PCBs and legacy pesticides) and polycyclic aromatic hydrocarbons (PAHs) as tracers to determine the sediment coming from areas with specific land use activities and from wildfires, such as those that occurred in BC in summer 2017. A third project will improve the use of models often referred to as mixing or unmixing models that are able to quantitatively apportion sediment to sources. This work will specifically use a Bayesian-based model (MixSIAR) with the goal of improving how the model utilises information on the variability associated with tracers in sediments and source materials like soils, and how the model incorporates tracer properties such as CSSIs, POPs and PAHs. This work will strengthen the use of the sediment source fingerprinting approach by testing and evaluating several novel and developing groups of tracers and by improving the ability of unmixing models to quantitatively identify the sources of problematic sediment. These improved techniques will advance the use of sediment fingerprinting and will directly benefit Canada's economy by providing an accurate means to protect our soil and water resources.