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Comparing Annual and Daily Time-Step Models for Predicting Field-Scale P Loss

Bolster, C. H., Forsberg, A., Mittelstet, A., Radcliffe, D. E., Storm, D., Ramirez-Avila, J. J., & Osmond, D. L. (2015). Comparing Annual and Daily Time-Step Models for Predicting Field-Scale P Loss. 2015 Annual Meeting ASA CSA SSSA. Minneapolis.

Abstract

Several models exist, each with varying degrees of complexity, which are available for describing P movement through the landscape. The complexity of these models is dependent on the amount of data required by the model, the number of model parameters needed to be estimated, the theoretical rigor of the governing equations, the processes described by the model, and the temporal and spatial scales of the model. 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 a spreadsheet-based annual time-step model developed for predicting P loss from agricultural fields. TBET is a daily time step model that applies the Soil and Water Assessment Tool (SWAT) 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 (AR, GA, MS, NC, OK, and TX). We then compared model predictions from both models with measured P loss data from these sites.


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