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.

Total and dissolved phosphorus losses from agricultural headwater streams during extreme runoff events

Eutrophication continues to be a concerning global water quality issue. Managing and mitigating harmful algal blooms demands clear information on the conditions promoting large phosphorus losses from contributing watersheds. Of particular concern is the amount and form of phosphorus loading to receiving water bodies during extreme runoff events, which are expected to increase in frequency due to climate change. Five years (2015 to 2020) of water quantity and quality data from 11 agricultural watersheds in the lower Great Lakes basin were analyzed and used to model total and dissolved phosphorus losses. This study aimed to assess temporal dynamics in phosphorus concentrations and losses over runoff events covering a wide range of hydrologic conditions and to quantify their relative importance on annual phosphorus losses. Event concentration-discharge relationships for total and dissolved phosphorus were hysteretic and had contrasting dominant patterns across watersheds. The proportion of annual phosphorus losses during events was highly variable between watersheds, accounting for 47-94 %. Extreme events were particularly impactful: as few as three events per year were found to be responsible for nearly half of total phosphorus (20-50 %) and total dissolved phosphorus (14-44 %) losses. Variability in total and dissolved phosphorus losses and concentrations over a wide range of flow conditions suggests that event magnitude is an important control on the relative mobility of particulate and dissolved phosphorus fractions. This study showed that insights into nutrient dynamics and phosphorus budgets in the lower Great Lakes basin and agriculture dominated environments more broadly can be gained by assessing event nutrient losses with respect to flow conditions and patterns in concentration-discharge relationships.

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 accurately predict nutrient loading from streams are needed to inform appropriate nutrient management decisions. Generalized 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 constituent. 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 accounting for the partitioning of surface and subsurface flow paths and hysteresis between streamflow and water quality parameters that are frequently observed in a wide range of environments.