Modeling stream water quality is informed by knowledge about pertinent factors and processes. The models must be validated against water quality observations, which may exist sufficiently in some watersheds (data rich watersheds) but may be limited or lacking in other cases (i.e., ungauged and poorly gauged watersheds). Machine learning (ML) algorithms have been growingly applied for water quality modeling, but they are limited to the data used for their training and validation. The question arises whether an ML-based model developed in one watershed can be transferred to adjacent watersheds. Here, we developed a unified subregional framework (i.e., one single consistent model configuration and standardized input variables) for modeling daily in-stream concentrations of nutrients-total phosphorus (TP) and total nitrogen (TN)-fecal coliform (FC) and dissolved oxygen (DO) in watersheds of a hydrologic subregion. The watersheds differ in their characteristics in terms of dominant land use/land cover (LULC) and topography. The framework was presented in the Peace-Tampa Bay subregion located in Southwest Florida. We found that the unified framework can be successfully developed for the watershed-scale modeling of DO and TP (Nash Sutcliffe Efficiency [NSE] > 0.75), and to a lesser extent for TN and FC (NSE > 0.49). The influence of dominant LULC was most prominent in modeling FC and TP, while the effect of topography was more pronounced for FC and TN than TP and DO. We also observed that longer-term antecedent conditions were more influential in modeling FC and TP, while shorter term saturation was more influential for modeling TN and DO. Insights from this study can be used to develop similarity criteria based on watershed characteristics, which support development of transferable models for predicting stream water quality in ungauged and poorly gauged watersheds.
