Mechanistic phytoplankton functional group (PFG) models are used to develop water quality targets designed to mitigate cyanobacteria blooms, but it remains unclear whether PFG models adequately simulate cyanobacteria dynamics as most are evaluated against observations of chlorophyll-a instead of PFG biomass. To address this challenge, we analyzed an application of CE-QUAL-ICM, a 3D mechanistic PFG model used by water managers and modelers. Global Sensitivity Analysis was employed to assess the sensitivity of modeled chlorophyll-a, cyanobacteria biomass, and eukaryotic phytoplankton biomass to 42 uncertain input factors in CE-QUAL-ICM's PFG growth and loss functions. Results revealed that parameterization of CE-QUAL-ICM captured bloom variation but underpredicted bloom peaks, and simulated chlorophyll-a with greater skill than PFG biomass. Additionally, when run across realistic ranges of PFG parameter values, model outputs were highly sensitive to chlorophyll-to-carbon ratios and phosphorus uptake parameters, indicating that these factors should be the focus of targeted parameterization efforts.

机械浮游植物官能团（PFG）模型被用来开发旨在减轻蓝藻水华的水质目标，但无论PFG模型充分模拟蓝藻的动态，因为大多数是针对叶绿素的观察评估仍不清楚一个，而不是PFG生物质。为了应对这一挑战，我们分析了CE-QUAL-ICM的应用，CE-QUAL-ICM是水管理人员和建模人员使用的3D机械PFG模型。全球敏感性分析用于评估建模的叶绿素a的敏感性CE-QUAL-ICM的PFG生长和损失函数中，蓝藻生物量和真核浮游生物量达到42个不确定的输入因子。结果表明，CE-QUAL-ICM的参数化设置可以捕获水华变化，但预测水华高峰不足，并且模拟叶绿素a的技术要比PFG生物量高。此外，当在实际的PFG参数值范围内运行时，模型输出对叶绿素/碳比和磷吸收参数高度敏感，表明这些因素应成为目标参数化工作的重点。