Water quality modeling of a prairie river-lake system.

Eutrophication of an under-ice river-lake system in Canada has been modeled using the Water Quality Analysis Simulation Program (WASP7). The model was used to assess the potential effect on water quality of increasing inter-basin transfer of water from an upstream reservoir into the Qu'Appelle River system. Although water is currently transferred, the need for increased transfer is a possibility under future water management scenarios to meet water demands in the region. Output from the model indicated that flow augmentation could decrease total ammonia and orthophosphate concentrations especially at Buffalo Pound Lake throughout the year. This is because the water being transferred has lower concentrations of these nutrients than the Qu'Appelle River system, although there is complex interplay between the more dilute chemistry, and the potential to increase loads by increasing flows. A global sensitivity analysis indicated that the model output for the lake component was more sensitive to input parameters than was the model output of the river component. Sensitive parameters included dissolved organic nitrogen mineralization rate, phytoplankton nitrogen to carbon ratio, phosphorus-to-carbon ratio, maximum phytoplankton growth rate, and phytoplankton death rate. Parameter sensitivities on output variables for the lake component were similar for both summer (open water) and winter (ice-covered), whereas those for the river component were different. The complex interplay of water quality, ice behaviors, and hydrodynamics of the chained river-lake system was all coupled in WASP7. Mean absolute error varied from 0.03-0.08 NH4-N/L for ammonium to 0.5 to1.7 mg/L for oxygen, and 0.04-0.13 NO3-N/L for nitrate.

Surface water retention systems for cattail production as a biofuel.

Surface water retention systems act to reduce nutrient pollution by collecting excess nutrients within a watershed via runoff. Harvesting aquatic biomass, such as the invasive cattail, from retention systems removes nutrients absorbed by the plant from the ecosystem permanently. Harvested biomass can be used as a renewable energy source in place of fossil fuels, offsetting carbon emissions. The purpose of this research was to simulate cattail harvest from surface water retention systems to determine their ability to provide suitable growing conditions with annual fluctuations in water availability. The economic and environmental benefits associated with nutrient removal and carbon offsets were also calculated and monetized. A proposed upstream and existing downstream water retention system in southern Manitoba were modelled using a system dynamics model with streamflow inputs provided by a physical hydrologic model, Modélisation Environmentale Communautaire - Surface and Hydrology (MESH). Harvesting cattail and other unconventional feedstocks, such as reeds, sedges, and grasses, from retention systems provided a viable revenue stream for landowners over a ten-year period. This practice generates income for landowners via biomass and carbon credit production on otherwise underutilized marginal cropland invaded with cattail. The economic benefits promote wetland habitat restoration while managing cattail growth to maintain biodiversity. Excess nitrogen and phosphorus are also removed from the ecosystem, reducing downstream nutrient loading. Utilizing surface water retention systems for cattail harvest is a best management strategy for nutrient retention on the landscape and improving agricultural resilience.

Bridging science and traditional knowledge to assess cumulative impacts of stressors on ecosystem health.

Cumulative environmental impacts driven by anthropogenic stressors lead to disproportionate effects on indigenous communities that are reliant on land and water resources. Understanding and counteracting these effects requires knowledge from multiple sources. Yet the combined use of Traditional Knowledge (TK) and Scientific Knowledge (SK) has both technical and philosophical hurdles to overcome, and suffers from inherently imbalanced power dynamics that can disfavour the very communities it intends to benefit. In this article, we present a 'two-eyed seeing' approach for co-producing and blending knowledge about ecosystem health by using an adapted Bayesian Belief Network for the Slave River and Delta region in Canada's Northwest Territories. We highlight how bridging TK and SK with a combination of field data, interview transcripts, existing models, and expert judgement can address key questions about ecosystem health when considerable uncertainty exists. SK indicators (e.g., bird counts, mercury in fish, water depth) were graded as moderate, whereas TK indicators (e.g., bird usage, fish aesthetics, changes to water flow) were graded as being poor in comparison to the past. SK indicators were predominantly spatial (i.e., comparing to other locations) while the TK indicators were predominantly temporal (i.e., comparing across time). After being populated by 16 experts (local harvesters, Elders, governmental representatives, and scientists) using both TK and SK, the model output reported low probabilities that the social-ecological system is healthy as it used to be. We argue that it is novel and important to bridge TK and SK to address the challenges of environmental change such as the cumulative impacts of multiple stressors on ecosystems and the services they provide. This study presents a critical social-ecological tool for widening the evidence-base to a more holistic understanding of the system dynamics of multiple environmental stressors in ecosystems and for developing more effective knowledge-inclusive partnerships between indigenous communities, researchers and policy decision-makers. This represents new transformational empirical insights into how wider knowledge discourses can contribute to more effective adaptive co-management governance practices and solutions for the resilience and sustainability of ecosystems in Northern Canada and other parts of the world with strong indigenous land tenure.