Quantifying Internal Phosphorus Loading in Lakes and Reservoirs across the United States

Phosphorus (P) is a critical control on eutrophication in many lakes and reservoirs around the world. It can enter the water bodies externally from point and non-point sources as well as internally from the lake bottom sediments. Accounting for these external and internal loads in watershed P budgets can lead to more for effective water quality management. However, while several studies have been conducted to quantify external loads, quantification of internal loads is less common and more uncertain. Several statistical models have been developed to quantify the internal load but the existing models have poor performance across large spatial scales. In this study, we develop a regression model within Bayesian hierarchical framework to estimate the internal loads in different lakes and reservoirs across the United States. The new model is based on sediment core and benthic chamber studies in over 60 sites spread over North America. It uses total P concentration and temperature in overlying water as predictor variables to explain about 40% of the variance in the observed flux data. The effect of total P concentration and temperature is multiplicative, with the two predictors contributing almost equally to model performance. The results are used to assess variability in internal loads and their relative contribution to watershed P budgets across different regions of the country. The model can also be used to provide initial estimates of internal loading for individual water bodies, which can be further refined through site-specific measurements and modeling efforts.