A good water quality model needs sufficient data to characterise the waterbody, yet monitoring resources are often limited. Inadequate boundary data often contribute to model uncertainty and error. In these situations, the same water quality model can also be used to determine where sampling efforts are best concentrated for improving model reliability. A sensitivity analysis using a one-at-a-time approach on a shallow, eutrophic, Prairie reservoir model investigates which boundary conditions are contributing most to variability in the model. The model results show the lake model has greater sensitivity to its catchment processes than to its in-lake processes. Flows are shown to have the greatest influence on model predictions for all water quality variables tested, followed by air temperature. The lake is facing pressure from climate change, and water management decisions. Results indicate defining the water balance accurately will be a crucial factor in future monitoring programs and modelling efforts.

良好的水质模型需要足够的数据来表征水体，但监测资源通常有限。边界数据不足通常会导致模型不确定性和误差。在这些情况下，相同的水质模型也可以用于确定采样工作最集中在哪里，以提高模型的可靠性。在浅水富营养化草原模型上一次使用一次方法进行敏感性分析，调查哪些边界条件对模型的可变性影响最大。模型结果表明，湖泊模型对集水过程的敏感性高于对湖中过程的敏感性。对于所有测试的水质变量，显示出流量对模型预测的影响最大，其次是气温。该湖正面临气候变化的压力，和水管理决策。结果表明准确定义水平衡将是未来监测计划和建模工作中的关键因素。