Comparing an Annual and a Daily Time‐Step Model for Predicting Field‐Scale Phosphorus Loss

Bolster, Carl H.; Forsberg, Adam; Mittelstet, Aaron R.; Radcliffe, D. E.; Storm, Daniel E.; Ramírez-Ávila, John J.; Sharpley, Andrew N.; Osmond, Deanna L.

doi:10.2134/jeq2016.04.0159

Cited by 14 publications

(22 citation statements)

References 45 publications

(67 reference statements)

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“…Overall, APLE, the simpler model, performed about as well as the more complex and calibrated TBET model, although both models had limited accuracy for predicting field‐scale P losses in the South (Table 3). Bolster et al (2017) provided a detailed comparison analysis of the benchmark data and modeled P loads from APLE and TBET. Using a slightly more expanded dataset for the fields evaluated in our study, predictions of DP loss by APLE (model efficiency [ E ] = 0.52, percent bias [PBIAS] = −9.8%) were slightly better than those with TBET ( E = 0.42; PBIAS = 40%) (Bolster et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

“…Bolster et al (2017) provided a detailed comparison analysis of the benchmark data and modeled P loads from APLE and TBET. Using a slightly more expanded dataset for the fields evaluated in our study, predictions of DP loss by APLE (model efficiency [ E ] = 0.52, percent bias [PBIAS] = −9.8%) were slightly better than those with TBET ( E = 0.42; PBIAS = 40%) (Bolster et al, 2017). For predictions of TP, model efficiencies for both models were negative, indicating that the models provided worse predictions of TP loss than simply taking the average of the measured values.…”

Section: Resultsmentioning

confidence: 99%

“…Soil characteristics (Table 1), as well as land use information (Table 2), were assembled for these benchmark locations. A detailed description of these six benchmark sites can be found in Bolster et al (2017).…”

Section: Methodsmentioning

confidence: 99%

“…All models estimate edge‐of‐field P and sediment loss. More detailed descriptions of APLE can be found in Bolster et al (2017), of TBET in Forsberg et al (2017), and of APEX in Ramirez‐Avila et al (unpublished data, 2016).…”

Section: Methodsmentioning

confidence: 99%

“…Water quality modeling was conducted with TBET for Arkansas, Georgia, and North Carolina datasets (Forsberg et al, 2017) and for Oklahoma and Texas datasets (White et al, 2012). The APLE model was applied to all data sites (Bolster et al, 2017), and APEX was employed with datasets from Arkansas, Georgia, Mississippi, and North Carolina (Ramirez‐Avila et al, unpublished data, 2016). Model type was matched to benchmark datasets based on a combination of prior work, such as the case of TBET use with the Oklahoma and Texas data, or time step differences (annual vs. daily) between the collected data and model requirements of APEX, which precluded the use of Oklahoma and Texas as benchmark sites, as the data did not match model requirements.…”

Section: Methodsmentioning

confidence: 99%

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Southern Phosphorus Indices, Water Quality Data, and Modeling (APEX, APLE, and TBET) Results: A Comparison

et al. 2017

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Southern Phosphorus Indices, Water Quality Data, and Modeling (APEX, APLE, and TBET) Results: A Comparison

et al. 2017

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Modeling of phosphorus loss from field to watershed: A review

et al. 2020

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Phosphorus (P) losses from nonpoint sources into surface water resources through surface runoff and tile drainage play a significant role in eutrophication. Accordingly, the number of studies involving the modeling of agricultural P losses, the uncertainties of such models, and the best management practices (BMPs) supported by the modeling of hypothetical P loss reduction scenarios has increased significantly around the world. Many improvements have been made to these models: separate manure P pools, variable source areas allowing the determination of critical source areas of P loss, analyses of modeling uncertainties, and understanding of legacy P. However, several elements are still missing or have yet to be sufficiently addressed: the incorporation of preferential flow into models, the modification of P sorption-desorption processes considering recent research data (e.g., pedotransfer functions for labile, active, or stable P, along with P sorption coefficients), BMP parameterization, and scale-up issues, as well as stakeholder-scientist and experimentalist-modeler interactions. The accuracy of P loss modeling can be improved by (a) incorporating dynamic P sorptiondesorption processes and new P subroutines for direct P loss from manure, fertilizer, and dung, (b) modeling preferential flow, connectivity between field and adjacent water bodies, and P in-stream processes, (c) including an assessment of model uncertainty, (d) integrating field and watershed models for BMP calibration and scaling field results up to larger areas, and (e) building a holistic interaction between stakeholders, experimentalists, and modelers.

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Sensitivity and uncertainty analysis for predicted soil test phosphorus using the Annual Phosphorus Loss Estimator model

Bolster

Wessel

Vadas³

et al. 2022

J of Env Quality

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In this study we conducted a sensitivity and uncertainty analysis using the Annual P Loss Estimator (APLE) model focusing on model predictions of soil test phosphorus (STP). We calculated and evaluated the sensitivity coefficients of predicted STP and changes in STP using 1‐ and 10‐yr simulations with and without P application. We also compared two methods for estimating prediction uncertainties: first‐order variance approximation (FOVA) and Monte Carlo simulation (MCS). Finally, we compared uncertainties in APLE‐predicted STP with uncertainties in measured STP collected from multiple sites in Maryland under different manuring and cropping treatments. Results from our sensitivity analysis showed that predicted STP and changes in STP for 1‐yr simulations without P inputs were most sensitive to initial STP, whereas model STP predictions were most sensitive to manure and fertilizer application rates when sensitivity analyses included P inputs. For the 10‐yr simulations without P application inputs, the range in sensitivity coefficients for crop uptake and precipitation were much greater than for the 1‐yr simulations. Prediction uncertainties from FOVA were comparable to those from MCS for model input uncertainties up to 50%. Using FOVA to calculate APLE STP prediction uncertainties using the Maryland data set, the mean measured STP for nearly all site years fell within the 95% confidence intervals of the STP prediction uncertainties. Our results provide users of APLE insight into what model inputs require the most careful measurement when using the model to predict changes in STP under conditions of P drawdown (i.e., no P application) or P buildup.

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Comparing an Annual and a Daily Time‐Step Model for Predicting Field‐Scale Phosphorus Loss

Cited by 14 publications

References 45 publications

Southern Phosphorus Indices, Water Quality Data, and Modeling (APEX, APLE, and TBET) Results: A Comparison

Southern Phosphorus Indices, Water Quality Data, and Modeling (APEX, APLE, and TBET) Results: A Comparison

Modeling of phosphorus loss from field to watershed: A review

Sensitivity and uncertainty analysis for predicted soil test phosphorus using the Annual Phosphorus Loss Estimator model

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