Comparing an annual and daily time-step model for predicting field-scale phosphorus loss

Authors: Bolster, Carl; FORSBERG, ADAM - University Of Georgia; MITTELSTET, AARON - Oklahoma State University; RADCLIFFE, DAVID - University Of Georgia; STORM, DANIEL - Oklahoma State University; RAMIREZ-AVILA, JOHN - Mississippi State University; OSMOND, DEANNA - North Carolina State University

Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/23/2016
Publication Date: 1/19/2017
Citation: Bolster, C.H., Forsberg, A., Mittelstet, A., Radcliffe, D., Storm, D., Ramirez-Avila, J., Osmond, D. 2017. Comparing an annual and daily time-step model for predicting field-scale phosphorus loss. Journal of Environmental Quality. doi:10.2134/jeq2016.04.0159.

Interpretive Summary: Several models with varying degrees of complexity are available for describing P movement through the landscape. In this study, we compare results from two models varying in their degree of complexity: the Annual P Loss Estimator and the Texas Best management practice Evaluation Tool. The Annual P Loss Estimator is a spreadsheet-based annual time-step model developed for predicting P loss from agricultural fields. The Texas Best management practice Evaluation Tool is a daily time step model that applies the Soil and Water Assessment Tool at the field scale. Two important differences exist between these two models for describing P loss at the field scale: the time step used and how incidental P losses from surface applied fertilizer and manure are simulated. APLE is an annual time step model and thus is unable to simulate the effects of event-based P losses. In the version of SWAT currently used by TBET, direct interactions between runoff and surface-applied manure and fertilizer P are not simulated; rather, all surface applied P is assumed to be incorporated into the top 10 mm of soil and instantaneously partitioned between the different P pools. We first compared predictions of field-scale P loss for both models using as model inputs field and land management data collected from multiple research sites throughout the South and Southwest. We then compared model predictions from both models with measured P loss data from these sites. Our results indicate that simple annual time-step models do not necessarily produce poorer predictions of P loss at the field scale than more complex physically-based models and thus have a role to play in nutrient management planning.

Technical Abstract: Numerous models exist for describing phosphorus (P) losses from agricultural fields. The complexity of these models varies considerably ranging from simple empirically-based annual time-step models to more complex process-based daily time step models. While better accuracy is often assumed with more complex, process-based models, comparisons of empirically-based models with process-based models for predicting P loss at the field scale are rare. In this study, we compare results from two models varying in their degree of complexity: the Annual P Loss Estimator (APLE) and the Texas Best management practice Evaluation Tool (TBET). APLE is an empirically-based annual time-step model whereas TBET is a process-based daily time-step model that applies the Soil and Water Assessment Tool at the field scale. We first compared predictions of field-scale P loss for both models using as model inputs field and land management data collected from multiple research sites throughout the South and Southwest US. We then compared predictions of P loss from both models with measured P loss data from these sites. We found that APLE generally predicted greater dissolved P losses than TBET whereas TBET generally predicted greater particulate P losses than APLE for the conditions simulated in our study. When we compared model predictions with measured P loss data, neither model consistently outperformed the other. Our results indicate that simple annual time-step models do not necessarily produce poorer predictions of P loss at the field scale than more complex physically-based models and thus have a role to play in nutrient management planning.