Sensitivity analysis is an important component for modelling water resource and environmental processes. Analysis of Variance (ANOVA), has been widely used for global sensitivity analysis for various models. However, the applicability of ANOVA is restricted by this biased variance estimator. To address this issue, the subsampling based ANOVA method are developed in this study, in which multiple subsampling(single-, multiple- and full-subsampling) techniques are proposed to diminish the effect of the biased variance estimator of ANOVA. Two case studies including one simplified regression model and one hydrological model are used to illustrate the applicability of the proposed approaches. Results indicate that: (1) the subsampling procedures effectively diminish the biases resulting from traditional ANOVA method; (2) among the proposed subsampling approaches, the full-subsampling ANOVA has the most robust performance; (3) compared with Sobol’s method, the subsampling ANOVA methods can significantly reduce the calculation requirements while achieve similar sensitivity characterization for model parameters. This study serves as a first basis for the application of subsampling ANOVA methods to sensitivity analysis for water resource and environmental models.

敏感性分析是对水资源和环境过程进行建模的重要组成部分。方差分析（ANOVA）已广泛用于各种模型的全局灵敏度分析。但是，方差分析的适用性受到该偏差方差估计器的限制。为了解决这个问题，本研究开发了基于二次抽样的方差分析方法，其中提出了多种二次抽样（单次抽样，多次二次抽样和全次抽样）技术以减少ANOVA偏差方差估计器的影响。通过两个案例研究（包括一个简化的回归模型和一个水文模型）来说明所提出方法的适用性。结果表明：（1）二次抽样程序有效地减少了传统方差分析方法产生的偏差；（2）在建议的二次采样方法中，全二次采样方差分析具有最强的性能；（3）与Sobol方法相比，二次采样方差分析方法可以显着降低计算要求，同时对模型参数实现相似的灵敏度表征。该研究为将二次抽样方差分析方法应用于水资源和环境模型的敏感性分析提供了基础。