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Phosphorus inputs from anthropogenic activities are subject to hydrologic (riverine) export, causing water quality problems in downstream lakes and coastal systems. Nutrient budgets have been developed to quantify the amount of nutrients imported to and exported from various watersheds. However, at large spatial scales, estimates of hydrologic phosphorus export are usually unavailable. This study develops a Bayesian hierarchical model to estimate annual phosphorus export across the contiguous United States, considering agricultural inputs, urban inputs, and geogenic sources under varying precipitation conditions. The Bayesian framework allows for a systematic updating of prior information on export rates using an extensive calibration data set of riverine loadings. Furthermore, the hierarchical approach allows for spatial variation in export rates across major watersheds and ecoregions. Applying the model, we map hotspots of phosphorus loss across the United States and characterize the primary factors driving these losses. Results emphasize the importance of precipitation in determining hydrologic export rates for various anthropogenic inputs, especially agriculture. Our findings also emphasize the importance of phosphorus from geogenic sources in overall river export. 

Abstract National nutrient inventories provide surplus phosphorus (P) estimates derived from county‐scale mass balance calculations using P inputs from manure and fertilizer sales and P outputs from crop yield data. Although bioavailable P and surplus P are often correlated at the field scale, few studies have investigated the relationship between measured soil P concentrations of large‐scale soil testing programs and inventory‐based surplus P estimates. In this study, we assessed the relationship between national surplus P data from the NuGIS dataset and laboratory‐measured soil test phosphorus (STP) at the county scale for Arkansas, North Carolina, and Oklahoma. For optimal periods of surplus P aggregation, surplus P was positively correlated with STP based on both Pearson (Arkansas:r = 0.65, North Carolina:r = 0.45, Oklahoma:r = 0.52) and Spearman correlation coefficients (Arkansas:ρ = 0.57, North Carolina:ρ = 0.28, and Oklahoma:ρ = 0.66). Based on Pearson correlations, the optimal surplus P aggregation periods were 10, 30, and 4 years for AR, NC, and OK, respectively. On average, STP was more strongly correlated with surplus P than with individual P inventory components (fertilizer, manure, and crop removal), except in North Carolina. In Arkansas and North Carolina, manure P was positively correlated with STP, and fertilizer P was negatively correlated with STP. Altogether, results suggest that surplus P moderately correlates with STP concentrations, but aggregation period and location‐specific factors influence the strength of the relationship.