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Title:   Author  item
                                            Bolster, Carl
  Submitted to: SERA-IEG 17 Bulletin
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 6/30/2015
                                    Publication  Date: 11/19/2015
                                    Citation: Bolster, C.H. 2015. The need for model uncertainty analysis. SERA-IEG 17 Bulletin. Pgs. 14-15.Interpretive Summary:
                                Technical Abstract: Phosphorous (P) loss models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. All P loss models, however, have an inherent amount of uncertainty associated with them. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model, an empirically-based spreadsheet model developed to describe annual, field-scale P loss when surface runoff is the dominant P loss pathway. In particular we evaluated the effects of uncertainties associated with model parameter errors and compared them with uncertainties associated with model inputs. Specifically, we estimated the parameter uncertainties associated with the regression equations used to calculate total soil P from measurements of soil clay content, organic matter, and labile P; the P enrichment ratio determined from erosion rates; concentration of P in runoff calculated from labile soil P; and partitioning of P between runoff and infiltration for applied manures and fertilizers based on runoff ratio. Our analysis included calculating both confidence and prediction intervals. We then calculated predictions of P loss using the APLE model while including uncertainties in both model parameters and inputs and compared the relative magnitude of these sources of uncertainty to the overall uncertainty associated with predictions of P loss. Results from this study highlight the importance of including reasonable estimates of model parameter uncertainties when using models to predict P loss. Our results also demonstrate how the estimation of model parameter uncertainty can identify model limitations.


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