Submitted to: Pest Management Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/3/2003
Publication Date: 3/20/2004
Citation: Ma, Q.L., Wauchope, R.D., Ma, L., Rojas, K.W., Malone, R.W., Ahuja, L.R. 2004. Test of the root zone water quality model (rzwqm) for predicting runoff of atrazine, alachlor and fenamiphos species from conventional-tillage corn mesoplots. Pest Management Science 60:267-276(2004). Interpretive Summary: Ten years ago a team of eight ARS and University of Georgia scientists conducted the most intensive rainfall simulation experiment ever conducted, to measure the nonpoint pollution of streams by pesticides and nutrient chemicals from a typical corn field. Twenty-four heavy rains were simulated on two fields for two years and the runoff from the fields was measured and sampled and analyzed. The main purpose of the experiment was to provide a full data set to compare with computer simulation models predictions: an attempt was made to observe, measure and record in the field everything that was known to influence chemical runoff from crops. These data have all been input into the most sophisticated pesticide runoff simulation model available, the ARS Root Zone Water Quality Model or RZWQM and we compared RZWQM's predictions of pesticide runof with the fields results. The results were very good. RZWQM, with little tuning, or calibration of parameters to make the predictions fit the results, predicted total annual losses of pesticides from 65 to 126% of observed amounts. This is exceptionally good; these types of models are considered to be doing well if they hit within a factor of 2.
Technical Abstract: The Root Zone Water Quality Model (RZWQM) is a comprehensive, integrated physical, biological, and chemical process model that simulates plant growth and movement of water, nutrients and pesticides in a representative area of an agricultural system. We tested the ability of RZWQM to predict surface runoff losses of atrazine, alachlor, fenamiphos and two fenamiphos oxidative degradates as compared with results from a 2-year mesoplot rainfall simualtion experiment. Model parameter inputs included site-specific soil properties and weather, but default values were used for most other parameters including pesticide properties. No attempts were made to calibrate the model except for an adjustment of pesticide persistence in near-surface soil, and a calibration for soil crust/seal hydraulic conductivity. RZWQM predicted runoff water volumes very well, giving predicted/observed ratios of 1.2 + 0.5 for all events. Predicted pesticide concentrations and loads from critical events--those occurring 24 h after pesticide application--were generally within a factor of 2, but atrazine losses from these events tended to be underestimated--probably a formulation effect--and fenamiphos losses tended to be overestimated due to rapid oxidation of the parent. The ratios of predicted to measured pesticide concentrations in all runoff events varied between 0.2 and 147, with an average of 7. Large over-predictions of pesticide runoff occurred in runoff events later in the season when both concentrations and loads were small. The normalized root mean square error (NRMSE) for pesticide runoff concentration predictions varied between 42% and 122%, with an average of 84%. Pesticide runoff loads were predicted with a similar accuracy. These results indicate that the soil-water mising model used in RZWQM is an excellent and robust predictor of pesticide entrainment and runoff.