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Abstract Agricultural land cover in the U.S. Midwest is a major source of nutrient pollution that has led to impairment of stream water quality. This study examines the impact of a forested state park on nutrient concentrations within an agriculturally dominated watershed. Water samples were collected over a 2‐year study period from eight stream sampling sites along four creeks and processed for total nitrogen (TN), nitrate (), total phosphorus (TP), and orthophosphate (). Hydrology, channel morphology, and remotely sensed land cover and vegetation data were also collected and analyzed within the study area. Results indicate that water quality responses to a forested state park vary between TN, , TP, and , and water quality variables are uniquely influenced by watershed and stream characteristics. The greatest water quality benefits most frequently occurred within the two smallest study streams with the greatest residence times and proportion of watershed areas within the forested state park. Overall, the greatest improvements to water quality occurred during periods of low stream discharge and when riparian vegetation was greenest. The results of this study suggest that conservation of forested areas within agriculturally dominated watersheds can provide water quality improvements in the U.S. Midwest. Targeting watersheds that drain small streams with long residence times for conservation may be most beneficial to improving water quality. 

Abstract Elevated nutrient and suspended sediment concentrations often result in negative environmental impacts within freshwater environments. Studies that directly compare suspended sediment and bioavailable nutrients between predominantly agricultural and predominantly urban watersheds during baseflow conditions are largely lacking. The purpose of this study was to determine the impacts of land cover, stream discharge, and wastewater treatment plant (WWTP) discharge on nutrient and sediment concentrations, across a large land cover gradient in Southwest Ohio streams. Weekly baseflow samples were collected from eight streams over 1 year from November, 2016 through November, 2017. Total suspended sediment, nitrate, and phosphate concentrations were measured. Results indicate that agricultural land cover and WWTPs increase nitrate and phosphate concentrations in the study area. Total suspended sediment and nitrate concentrations increased with discharge, and discharge was a relatively weak predictor of phosphate concentrations. Seasonal water quality trends varied by parameter and land use also had unique impacts on seasonal water quality trends. Results suggest that to improve water quality in the study area, efforts should focus on improving WWTP effluent treatment and agricultural land management. 

Abstract Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. In lakes, pelagic gross primary productivity (GPP) is strongly controlled by inputs of nutrients and dissolved organic matter. Although past studies have developed process models of this nutrient‐color paradigm (NCP), broad empirical tests of these models are scarce. We used data from 58 globally distributed, mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. The model includes three state variables–dissolved phosphorus, terrestrial dissolved organic carbon (DOC), and phytoplankton biomass–and generates realistic predictions for equilibrium rates of pelagic GPP. We calibrated our model using a Bayesian data assimilation technique on a subset of lakes where DOC and total phosphorus (TP) loads were known. We then asked how well the calibrated model performed with a larger set of lakes. Revised parameter estimates from the updated model aligned well with existing literature values. Observed GPP varied nonlinearly with both inflow DOC and TP concentrations in a manner consistent with increasing light limitation as DOC inputs increased and decreasing nutrient limitation as TP inputs increased. Furthermore, across these diverse lake ecosystems, model predictions of GPP were highly correlated with observed values derived from high‐frequency sensor data. The GPP predictions using the updated parameters improved upon previous estimates, expanding the utility of a process model with simplified assumptions for water column mixing. Our analysis provides a model structure that may be broadly useful for understanding current and future patterns in lake primary production.