Climate change driven temperature fluctuations have intensified the unpredictability of river ice jams and associated flooding. Accurate forecasting of ice cover formation and breakup processes is therefore essential for river ice related flood risk assessment and mitigation planning. Recent advances in computing resources and numerical methods have enabled the development of computational models for investigating ice jam phenomena and river hydrodynamics under a wide range of conditions. Improving the computational efficiency of these numerical models can significantly enhance statistical analyses of model outputs and parameters, which is particularly important for assessing the impacts of climate change. This work investigates the computational performance of the river ice model RIVICE using a case study setup calibrated for the Liard River at Fort Simpson. Numerical simulations of ice jam formation and breakup for this case study are conducted to examine the effects of spatial resolution on model stability, accuracy, and computational efficiency. Three spatial resolution configurations–low, medium, and high–are tested. Additionally, the impact of the number of reaches specified in the model on computational efficiency is examined. Based on the analysis, potential strategies for improving the computational efficiency of RIVICE are discussed. Overall, this study highlights the current capabilities of the RIVICE model and identifies future directions for enhancing its performance, ultimately supporting more efficient and reliable river ice related risk assessments under changing climate conditions.