There is a growing need to address water pollution that demands advanced tools to predict the fate and transport of water quality constituents. Existing stream solute transport models use simple first-order kinetics to evaluate nutrient loss, however these ignore biochemical reactions and lack a user-friendly interface. To address this shortcoming, we integrated the One-dimensional Transport with Inflow and Storage (OTIS) model and the Enhanced Stream Water Quality Model (QUAL2E) and developed an improved interface for a physically-based solute transport model. With background algal concentration as the only calibration parameter, a generalized model was developed and evaluated. The new model performed reasonably well in predicting nutrient uptake of newly collected experimental data and published data from 32 other datasets (R² = 0.76, NSE = 0.47 and Percent Bias = -4.3%). Inclusion of biochemical reactions from QUAL2E improves model confidence and provides and options for incorporating actual process-based data which is unfeasible in existing first-order decay-based models.

解决水污染的需求日益增长，需要先进的工具来预测水质成分的命运和转移。现有的物流溶质运移模型使用简单的一阶动力学来评估养分流失，但是这些模型忽略了生化反应并且缺乏用户友好的界面。为了解决此缺点，我们将一维运输与入水和存储（OTIS）模型和增强型溪水水质模型（QUAL2E）集成在一起，并为基于物理的溶质运输模型开发了改进的接口。以背景藻类浓度作为唯一的校准参数，开发并评估了通用模型。新模型在预测新收集的实验数据和来自其他32个数据集（R²  = 0.76，NSE = 0.47，偏差百分比= -4.3％）。从QUAL2E中包含生化反应可提高模型的可信度，并提供并提供选项，以将基于过程的实际数据合并到现有的基于一阶衰减的模型中是不可行的。