Hong Tao, Purucker S Thomas
Oak Ridge Institute of Science and Education, Ecosystems Research Division, 960 College Station Road, Athens, GA, USA.
US Environmental Protection Agency, Ecosystems Research Division, 960 College Station Road, Athens, GA, USA.
Environ Model Softw. 2018;105:24-38. doi: 10.1016/j.envsoft.2018.03.018.
Environmental fate and transport processes are influenced by many factors. Simulation models that mimic these processes often have complex implementations, which can lead to over-parameterization. Sensitivity analyses are subsequently used to identify critical parameters whose uncertainties can be further reduced or better described and prediction variability minimized. In this study, a variance decomposition based global sensitivity analysis technique (Sobol' method) is conducted based on estimated concentrations in vertical soil compartments using the Pesticide Root Zone Model (PRZM). Daily simulations are performed that explore the input parameter space. Estimated concentrations are compared to data collected over the course of a growing season from an experimental site in Georgia. Our results suggest that model sensitivity is conditional and should be examined at appropriate spatial and temporal resolution to avoid omitting important parameters. This approach can yield a better understanding about the interplay between sensitivity/uncertainty and model dynamics in non-monotonic, non-linear systems.
环境归宿和迁移过程受到多种因素的影响。模拟这些过程的模型通常具有复杂的实现方式,这可能导致过度参数化。随后使用敏感性分析来识别关键参数,其不确定性可以进一步降低或得到更好的描述,并将预测变异性降至最低。在本研究中,基于使用农药根区模型(PRZM)估算的垂直土壤层中的浓度,进行了基于方差分解的全局敏感性分析技术(索博尔方法)。进行每日模拟以探索输入参数空间。将估算浓度与从佐治亚州一个试验场在生长季节期间收集的数据进行比较。我们的结果表明,模型敏感性是有条件的,应在适当的空间和时间分辨率下进行检查,以避免遗漏重要参数。这种方法可以更好地理解非单调、非线性系统中敏感性/不确定性与模型动态之间的相互作用。