Improving the representation of stream water sources in surrogate nutrient models with water isotope data

An important part of meeting nutrient reduction goals in the lower Great Lakes basin and assessing the success of different land management strategies is modeling nutrient losses from agricultural land. This study aimed to improve the representation of water source contributions to streamflow in generalized additive models for predicting nutrient fluxes from three headwater agricultural streams in southern Ontario monitored during the Multi-Watershed Nutrient Study (MWNS). The previous development of these models represented baseflow contributions to streamflow using the baseflow proportion derived using an uncalibrated recursive digital filter. Recursive digital filters are commonly used to partition stream discharge into separate components from slower and faster pathways. In this study, we calibrated the recursive digital filter using stream water source information from stable isotopes of oxygen in water. Across sites, optimization of the filter parameters reduced bias in baseflow estimates by as much as 68 %. In most cases, calibrating the filter also improved agreement between filter-derived baseflow and baseflow calculated from isotope and streamflow data: the average Kling-Gupta Efficiencies using default and calibrated parameters were 0.44 and 0.82, respectively. When incorporated into the generalized additive models, the revised baseflow proportion predictor was more often statistically significant, improved model parsimony, and reduced prediction uncertainty. Moreover, this information allowed for a more rigorous interpretation of how different stream water sources influence nutrient losses from the agricultural MWNS watersheds.

Automated summary

This study aimed to improve the prediction of nutrient fluxes from agricultural streams in southern Ontario by improving the representation of water source contributions to streamflow in generalized additive models. By calibrating a recursive digital filter using stable isotopes of oxygen in water, the accuracy of baseflow estimates was significantly improved. The revised model provided a more accurate prediction of nutrient losses from the agricultural watersheds.