title: Uncertainty assessment of the model RICEWQ in northern Italy.
abstract: Model predictions are often seriously affected by uncertainties arising from many sources. Ignoring the uncertainty associated with model predictions may result in misleading interpretations when the model is used by a decision-maker for risk assessment. In this paper, an analysis of uncertainty was performed to estimate the uncertainty of model predictions and to screen out crucial variables using a Monte Carlo stochastic approach and a number of statistical methods, including ANOVA and stepwise multiple regression. The model studied was RICEWQ (Version 1.6.1), which was used to forecast pesticide fate in paddy fields. The results demonstrated that the paddy runoff concentration predicted by RICEWQ was in agreement with field measurements and the model can be applied to simulate pesticide fate at field scale. Model uncertainty was acceptable, runoff predictions conformed to a log-normal distribution with a short right tail, and predictions were reliable at field scale due to the narrow spread of uncertainty distribution. The main contribution of input variables to model uncertainty resulted from spatial (sediment-water partition coefficient and mixing depth to allow direct partitioning to bed) and management (time and rate of application) parameters, and weather conditions. Therefore, these crucial parameters should be carefully parameterized or precisely determined in each site-specific paddy field before the application of the model, since small errors of these parameters may induce large uncertainty of model outputs.
authors.affiliation: Istituto di Chimica Agraria ed Ambientale, Università Cattolica del Sacro Cuore, 29100 Piacenza, Italy.
authors.forename: Zewei
authors.initials: Z
authors.lastname: Miao
authors.forename: Marco
authors.initials: M
authors.lastname: Trevisan
authors.forename: Ettore
authors.initials: E
authors.lastname: Capri
authors.forename: Laura
authors.initials: L
authors.lastname: Padovani
authors.forename: Attilio A M
authors.initials: AA
authors.lastname: Del Re
mesh_terms: D005544:Forecasting; D007558:Italy; D008962:Models, Theoretical; D009010:Monte Carlo Method; D012275:Oryza; D010575:Pesticides / Q000032:analysis*; D018570:Risk Assessment; D012989:Soil Pollutants / Q000032:analysis*; D014873:Water Pollutants / Q000032:analysis*
chemical_list: D010575:Pesticides; D012989:Soil Pollutants; D014873:Water Pollutants