The Canadian Prairies are subject to cold winter dynamics, spring snowmelt runoff, and summer storms; a process variability that makes it difficult to identify an adequate sampling strategy for capturing representative water quality data. Hence, our research objective was to compare multiple water quality sampling strategies for Prairie watersheds and rank them based on operational and statistical criteria. The focus was on the Catfish Creek Watershed (Manitoba, Canada), which drains into the hypereutrophic Lake Winnipeg. Water samples were collected every 7 h during the 2013 open-water season and notably analyzed for nitrate and orthophosphate. The original high-frequency dataset (7 h) was then deconstructed into lower-frequency datasets to mimic strategies involving sample collection on a daily, weekly, bi-weekly, monthly, and seasonal basis. A comparison and decision matrix was also built to assess the ability of the lower-frequency datasets to retain the statistical properties of the original (7 h) dataset. Results indicate that nutrient concentrations vary significantly over short timescales and are affected by both sampling time (day versus night) and water level fluctuations. The decision matrix revealed that seasonal sampling is sufficient when the goal is only to capture mean water quality conditions; however, sub-daily to daily sampling is required for accurate process signal representation. While we acknowledge that sampling programs designed by researchers and public agencies are often driven by different goals, we found daily sampling to be the most parsimonious strategy for the study watershed and suggest that it would help to better quantify nutrient loads to Lake Winnipeg.