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

и другие.

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Май 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.

Язык: Английский

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

и другие.

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Май 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.

Язык: Английский

Процитировано

0