Application of WASP8 for modeling water temperature and eutrophication dynamics in the Liard River Basin, Canada

Across Canada, multiple drivers—including contaminant and nutrient inputs from human activity, landscape alteration, and climate change—are reshaping freshwater quality and biodiversity, creating significant challenges for water management and operations. In this study, we developed a numerical water quality modeling framework using the Water Quality Analysis Simulation Program (WASP8) and applied it to the Liard River in northwestern Canada to simulate eutrophication dynamics, including water temperature, nutrient cycles, dissolved oxygen (DO), and suspended sediment concentrations. The simulation results indicate that the model successfully reproduces the general trends and seasonal patterns of water temperature, DO, suspended solids, and most nutrient-cycling parameters. DO levels remain within a healthy range throughout the simulation period, with no indication of hypoxic conditions under current natural or anthropogenic influences. The analysis further shows that light—not nitrogen or phosphorus—is the dominant factor limiting phytoplankton growth rates in the basin under the current model structure and assumptions. Phytoplankton growth appears to be limited by light-related physical conditions under the model’s current structure and assumptions, including river depth and water turbidity, rather than by nutrient availability. Simulating multiple phytoplankton loading scenarios also shows that elevated phytoplankton loadings exert a negligible influence on DO, even under unrealistically high loading scenarios. A detailed analysis of the net DO fluxes further suggests that river physical processes may be the dominant controlling factors on DO dynamics in the Liard River, which could also help explain the negligible impact of elevated phytoplankton concentration on DO under the simulated scenarios. While the lack of extensive water quality data poses a significant challenge to fully validate the model, this study provides the first baseline mechanistic water quality model for this region, and the flexibility of the WASP8 framework ensures that the model can be progressively improved as additional monitoring data become available.