RESUMEN
Nutrient contamination from point and non-point sources can lead to harmful consequences, such as algal blooms. Point and non-point nutrient loading estimation is determined using modeling approaches and often require an abundance of variables and observations for calibration. Small rural streams that lack water use designations often lack available data to utilize current modeling strategies. This study proposes the use of a 3-phase hybrid stepwise statistical modeling approach using generalized linear mixed models (GLMM) and a reference stream. Two streams in Central Texas were sampled for 13 months between February 2020 and February 2021, one being impacted by a wastewater treatment plant (WWTP). Dissolved phosphorus (PO4-P), ammonia (NH3-N), nitrite/nitrate (NO2 + NO3-N), total nitrogen (TN), and total phosphorus (TP) were sampled in both streams for each month. Non-point sources of contamination, such as land use/land cover and geomorphology composition, were quantified for both sub-basin drainage areas. Phase I models predicted nutrient concentrations in the reference stream using non-point source variables along with discharge and temporal variables. Best fit models were carried forward to phase II and leveraged a point-source variable, which is a naïve estimate of effluent nutrient concentration in the absence of assimilation. Phase II model coefficients highlight the significance of point-source contamination in predicting nutrient concentration, but overall lacked the ability to make future predictions under new hydrologic regimes from WWTP intensification. Phase III models included deterministically calculating an uptake variable using the relationship between discharge and wetted widths, predicting background non-point concentrations by leveraging phase I models, and calculating future nutrient loadings from WWTP intensification. This approach predicted significant increases in nutrient concentrations under planned WWTP intensification scenarios and decreased uptake efficiencies under the new hydrologic regimes.