Novel predictors related to hysteresis and baseflow improve predictions of watershed nutrient loads: An example from Ontario's lower Great Lakes basin

Eutrophication has re-emerged in the lower Great Lakes basin resulting in critical water quality issues. Models that ac-curately predict nutrient loading from streams are needed to inform appropriate nutrient management decisions. Gen-eralized additive models (GAMs) that use surrogate data from sensors to predict nutrient loads offer an alternative to commonly applied linear regression and may better handle relationship non-linearities and skewed water quality data. Five years (2015-2020) of water quantity and quality data from 11 agricultural watersheds in southern Ontario were used to develop GAMs to predict total phosphorus (TP) and nitrate (NO3-) loads. This study aimed to 1) use GAMs to predict nutrient loads using both common and novel predictors and 2) quantify and examine the variability in seasonal and annual nutrient loads. Along with routine surrogate model predictors (i.e., flow, turbidity, and seasonality), the addition of the baseflow proportion and the hydrograph position of flow observations improved model performance. Conversely, including the antecedent precipitation index minimally affected model performance, regardless of constit-uent. Seasonal and annual patterns in TP and NO3- load predictions mirrored that of the hydrologic regime. This study showed that parsimonious GAMs featuring novel model predictors can be used to predict nutrient loads while account-ing for the partitioning of surface and subsurface flow paths and hysteresis between streamflow and water quality pa-rameters that are frequently observed in a wide range of environments.

Automated summary

Eutrophication has become a critical water quality issue in the lower Great Lakes basin. Generalized additive models (GAMs) using surrogate data from sensors can accurately predict nutrient loads and offer a better approach than linear regression. This study developed GAMs to predict total phosphorus (TP) and nitrate (NO3-) loads using five years of data from agricultural watersheds in southern Ontario. The addition of novel predictors improved model performance, while the antecedent precipitation index had minimal impact. Seasonal and annual nutrient load predictions aligned with the hydrologic regime, demonstrating the usefulness of GAMs in predicting nutrient loads while considering surface and subsurface flow paths.