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.

Currently, no river ice models are available that are free and open source software (FOSS), which can be a hindrance to advancement in the field of modelling river ice processes. This paper introduces a non-proprietary (conditional), open-source option to the scientific and engineering community, the River Ice Model (RIVICE). RIVICE is a one-dimensional, fully-dynamic wave model that mimics key river ice processes such as ice generation, ice transport, ice cover progression (shoving, submergence and juxtapositioning) and ice jam formation, details of which are highlighted in the text. Three ice jam events at Fort McMurray, Alberta, along the Athabasca River, are used as case studies to illustrate the steps of model setup, model calibration and results interpretation. A local sensitivity analysis reveals the varying effects of parameter and boundary conditions on backwater flood levels as a function of the location of ice jam lodgment along the river reach and the location along the ice jam cover. Some limitations of the model and suggestions for future research and model development conclude the paper.

Flows along the upper Qu’Appelle River are expected to increase in the future via increased discharge from Lake Diefenbaker to meet the demands of increased agricultural and industrial activity and population growth in southern Saskatchewan. This increased discharge and increased air temperature due to climate change are both expected to have an impact on the water quality of the river. The Water Quality Analysis Simulation Program (WASP7) was used to model current and future water quality of the upper Qu’Appelle River. The model was calibrated and validated to characterize the current state of the water quality of the river. The model was then used to predict water quality [nutrient (nitrogen and phosphorus) concentrations and oxygen dynamics] for the years 2050–2055 and 2080–2085. The modelling results indicate that global warming will result in a decrease in ice thickness, a shorter ice cover period, and decreased nutrient concentrations in 2050 or 2080 relative to 2010, with a greater decrease of nutrient concentrations in open water. In contrast to the effect of warmer water temperatures, increased flow through water management may cause increases in ammonium, nitrate, and dissolved oxygen concentrations and decreases in orthophosphate concentrations in summer.

Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources but are challenged in cold regions. Ground‐based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper discusses the scientific and technical challenges of the large‐scale modelling of cold region systems and reports recent modelling developments, focussing on MESH, the Canadian community hydrological land surface scheme. New cold region process representations include improved blowing snow transport and sublimation, lateral land‐surface flow, prairie pothole pond storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multistage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision‐support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km²). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. These illustrate the current capabilities and limitations of cold region modelling, and the extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology.

Dissolved oxygen is an influential factor of aquatic ecosystem health. Future predictions of oxygen deficits are paramount for maintaining water quality. Oxygen demands depend greatly on a waterbody’s attributes. A large sediment–water interface relative to volume means sediment oxygen demand has greater influence in shallow systems. In shallow, ice-covered waterbodies the potential for winter anoxia is high. Water quality models offer two options for modelling sediment oxygen demand: a zero-order constant rate, or a sediment diagenesis model. The constant rate is unrepresentative of a real system, yet a diagenesis model is difficult to parameterise and calibrate without data. We use the water quality model CE-QUAL-W2 to increase the complexity of a zero-order sediment compartment with limited data. We model summer and winter conditions individually to capture decay rates under-ice. Using a semi-automated calibration method, we find an annual pattern in sediment oxygen demand that follows the trend of chlorophyll-a concentrations in a shallow, eutrophic Prairie reservoir. We use chlorophyll-a as a proxy for estimation of summer oxygen demand and winter decay. We show that winter sediment oxygen demand is dependent on the previous summer’s maximum chlorophyll-a concentrations.