Most phosphorus (P) modeling studies of water quality have focused on surface runoff loses. However, a growing number of experimental studies have shown that P losses can occur in drainage water from artificially drained fields. In this review, we assess the applicability of nine models to predict this type of P loss. A model of P movement in artificially drained systems will likely need to account for the partitioning of water and P into runoff, macropore flow, and matrix flow. Within the soil profile, sorption and desorption of dissolved P and filtering of particulate P will be important. Eight models are reviewed (ADAPT, APEX, DRAINMOD, HSPF, HYDRUS, ICECREAMDB, PLEASE, and SWAT) along with P Indexes. Few of the models are designed to address P loss in drainage waters. Although the SWAT model has been used extensively for modeling P loss in runoff and includes tile drain flow, P losses are not simulated in tile drain flow. ADAPT, HSPF, and most P Indexes do not simulate flow to tiles or drains. DRAINMOD simulates drains but does not simulate P. The ICECREAMDB model from Sweden is an exception in that it is designed specifically for P losses in drainage water. This model seems to be a promising, parsimonious approach in simulating critical processes, but it needs to be tested. Field experiments using a nested, paired research design are needed to improve P models for artificially drained fields. Regardless of the model used, it is imperative that uncertainty in model predictions be assessed.

Phosphorus (P) Indices in the southern United States frequently produce different recommendations for similar conditions. We compared risk ratings from 12 southern states (Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, and Texas) using data collected from benchmark sites in the South (Arkansas, Georgia, Mississippi, North Carolina, Oklahoma, and Texas). Phosphorus Index ratings were developed using both measured erosion losses from each benchmark site and Revised Universal Soil Loss Equation 2 predictions; mostly, there was no difference in P Index outcome. The derived loss ratings were then compared with measured P loads at the benchmark sites by using equivalent USDA-NRCS P Index ratings and three water quality models (Annual P Loss Estimator [APLE], Agricultural Policy Environmental eXtender [APEX], and Texas Best Management Practice Evaluation Tool [TBET]). Phosphorus indices were finally compared against each other using USDA-NRCS loss ratings model estimate correspondence with USDA-NRCS loss ratings. Correspondence was 61% for APEX, 48% for APLE, and 52% for TBET, with overall P index correspondence at 55%. Additive P Indices (Alabama and Texas) had the lowest USDA-NRCS loss rating correspondence (31%), while the multiplicative (Arkansas, Florida, Louisiana, Mississippi, South Carolina, and Tennessee) and component (Georgia, Kentucky, and North Carolina) indices had similar USDA-NRCS loss rating correspondence-60 and 64%, respectively. Analysis using Kendall's modified Tau suggested that correlations between measured and calculated P-loss ratings were similar or better for most P Indices than the models. (Dubrovsky and Hamilton, 2010). Recent harmful algal blooms in Lake Erie caused Toledo to shut down its drinking water supply for several days, refocusing the link between nutrient enrichment (particularly phosphorus [P]) and water quality impairment (Stow et al., 2015), with many of these nutrients being agriculturally derived. To control agricultural nutrient loading to surface waters, multiple control strategies are necessary at the source and during transport into the receiving water resources. The USDA-NRCS refers to this as "avoid, control, and trap."Since the late 1990s, the USDA and USEPA jointly required all states to adopt a unified nutrient management policy through the NRCS Code 590 Standard (USDA and USEPA, 1999). States were required to establish a soil-test P threshold based on crop requirements (above which P applications were restricted), to establish an alternative soil test P threshold using water quality criteria, or to develop a P Index to identify fields at risk for P losses. Forty-eight states and some territories, including Puerto Rico, chose to use P Indices (Sharpley et al., 2003), a concept originally developed by USDA-NRCS for assigning relative risk of P loss to agricultural fields (Lemunyon and Gilbert, 1993). California and Connecticut use soil-test P crop response (Sharpley et al., 2003).To...

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A wide range of mathematical models are available for predicting phosphorus (P) losses from agricultural fields, ranging from simple, empirically based annual time-step models to more complex, process-based daily time-step models. In this study, we compare field-scale P-loss predictions between the Annual P Loss Estimator (APLE), an empirically based annual time-step model, and the Texas Best Management Practice Evaluation Tool (TBET), a process-based daily time-step model based on the Soil and Water Assessment Tool. We first compared predictions of fieldscale P loss from both models using field and land management data collected from 11 research sites throughout the southern United States. We then compared predictions of P loss from both models with measured P-loss data from these sites. We observed a strong and statistically significant (p < 0.001) correlation in both dissolved (r = 0.92) and particulate (r = 0.87) P loss between the two models; however, APLE predicted, on average, 44% greater dissolved P loss, whereas TBET predicted, on average, 105% greater particulate P loss for the conditions simulated in our study. When we compared model predictions with measured P-loss data, neither model consistently outperformed the other, indicating that more complex models do not necessarily produce better predictions of field-scale P loss. Our results also highlight limitations with both models and the need for continued efforts to improve their accuracy.Comparing an Annual and a Daily Time-Step Model for Predicting Field-Scale Phosphorus Loss Carl H. Bolster,* Adam Forsberg, Aaron Mittelstet, David E. Radcliffe, Daniel Storm, John Ramirez-Avila, Andrew N. Sharpley, and Deanna Osmond A pplication of phosphorus (P) to agricultural lands can lead to increased offsite transport of P via surface runoff, erosion, and/or subsurface leaching to groundwater. Delivery of this P to P-sensitive water bodies can lead to water quality deterioration, primarily by accelerating the natural eutrophication process. Notable examples where excess P loading is contributing to water quality degradation include the Baltic Sea, Chesapeake Bay, the Florida Everglades, the Gulf of Mexico, and Lake Erie (Richardson et al., 2007;Chesapeake Bay Program, 2009;Dale et al., 2010;Andersson et al., 2014;Schoumans et al., 2014). In response to concerns over P losses from agricultural fields, research has focused on improving our understanding of the processes controlling P movement through the landscape (Radcliffe and Cabrera, 2007). This in turn has led to the development, improvement, and testing of models for predicting P fate and transport in the environment. When properly developed and used, these models can be useful tools for evaluating different management strategies for reducing P loss from agricultural fields (Sharpley et al., 2003;Radcliffe et al., 2009).Models for describing P movement through the landscape range in complexity depending on the theoretical rigor of the governing equations, the number of processes included in the mo...

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