Quantifying Summer Internal Phosphorus Loading in Large Lakes across the United States DOI
Smitom Swapna Borah, Natalie Nelson, Owen W. Duckworth

et al.

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: May 20, 2025

Internal phosphorus loading (IPL) can be a significant (P) source in freshwater systems, often causing water-quality improvement delays. Despite its importance, IPL estimates are missing for many systems due to several large-scale measuring and modeling challenges. In this study, we develop framework estimate summer anoxic sediment release rates (SRRs) P 5899 large lakes reservoirs (surface area > 1.0 km2; mixing depth < maximum depth) across the contiguous US (CONUS). Our combines random forest models bottom-water temperature (BT) surface-water total (TP) with mixed-effects regression model SRR, it includes uncertainty propagation these models. results indicate that mean SRR ranges from 1 37 mg/m2/day CONUS lakes, 31% of waterbodies having 10 mg/m2/day. Areas high generally associated predicted TP, which is particularly common agricultural areas. Uncertainties predictions largely attributable forest-based inputs predictive error regression. relatively dry summers, likely higher than external 26% watersheds. Overall, our reveal where critical factor watershed nutrient management.

Language: Английский

Quantifying Summer Internal Phosphorus Loading in Large Lakes across the United States DOI
Smitom Swapna Borah, Natalie Nelson, Owen W. Duckworth

et al.

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: May 20, 2025

Internal phosphorus loading (IPL) can be a significant (P) source in freshwater systems, often causing water-quality improvement delays. Despite its importance, IPL estimates are missing for many systems due to several large-scale measuring and modeling challenges. In this study, we develop framework estimate summer anoxic sediment release rates (SRRs) P 5899 large lakes reservoirs (surface area > 1.0 km2; mixing depth < maximum depth) across the contiguous US (CONUS). Our combines random forest models bottom-water temperature (BT) surface-water total (TP) with mixed-effects regression model SRR, it includes uncertainty propagation these models. results indicate that mean SRR ranges from 1 37 mg/m2/day CONUS lakes, 31% of waterbodies having 10 mg/m2/day. Areas high generally associated predicted TP, which is particularly common agricultural areas. Uncertainties predictions largely attributable forest-based inputs predictive error regression. relatively dry summers, likely higher than external 26% watersheds. Overall, our reveal where critical factor watershed nutrient management.

Language: Английский

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