Hydrological models are powerful tools to understand soil–vegetation–atmosphere transport (SVAT) processes. These processes are partially controlled by soil water retention and hydraulic conductivity, in process-based models described by analytical functions (K-θ-h) that define the dependence between pressure head (h), soil water content (θ) and hydraulic conductivity (K). Given the nonlinearity of these process-based SVAT models, stochastic analysis is an interesting tool to get insight in the pattern of model outputs given the uncertainty in the K–θ–h functions. We developed a stochastic framework to evaluate outputs of the SWAP hydrological model according to the uncertainty in the van Genuchten-Mualem K–θ–h analytical functions (VGM). A multivariate Gaussian joint distribution analysis stochastically sampled VGM parameters using two statistical methods, one considering and one disregarding parameter correlations. We evaluated the relations of VGM parameters and simulation outputs (i) for an internal drainage scenario to predict pressure head at flux-based field capacity and (ii) for a cropped scenario to predict water balance components. We also assessed the relative importance of each parameter using linear regression and random forest methods. The hydrological stochastic simulations showed that the developed algorithm allowed to perform multiple SWAP model runs in a quick and unassisted manner on a common personal computer. Although the degree of VGM uncertainty of the studied soils did not affect the SWAP simulation results considerably, model output uncertainty increased when uncorrelated stochastic realizations of VGM parameters were used. The relative propagation of parameter uncertainty showed to be dependent on the simulated scenario (boundary conditions) and on the soil itself. For the correlated VGM stochastic realizations, both criteria used to analyze the parameter importance detected the VGM parameter l as one of the most relevant in determining water balance components variation. This is an interesting outcome, as this parameter is hardly ever determined, and commonly assumed to a predefined value.

水文模型是了解土壤－植被－大气传输（SVAT）过程的有力工具。在分析功能（K- θ- h）描述的基于过程的模型中，这些过程部分由土壤保水率和水力传导率控制，这些模型定义了压头（h），土壤含水量（θ）和水力传导率（K）。考虑到这些基于过程的SVAT模型的非线性，鉴于K –θ– h的不确定性，随机分析是一种有趣的工具，可以洞悉模型输出的模式功能。我们根据van Genuchten-Mualem K –θ– h中的不确定性，开发了一个随机框架来评估SWAP水文模型的输出分析功能（VGM）。多元高斯联合分布分析使用两种统计方法随机采样VGM参数，一种考虑和一种忽略参数相关性。我们评估了VGM参数与模拟输出之间的关系（i）用于内部排水的情景，以预测基于通量的场容量下的压头，（ii）用于裁剪的情景，以预测水平衡的组成部分。我们还使用线性回归和随机森林法评估了每个参数的相对重要性。水文随机模拟表明，开发的算法允许在普通的个人计算机上以快速，无辅助的方式执行多个SWAP模型运行。尽管研究土壤的VGM不确定度对SWAP模拟结果影响不大，当使用VGM参数的不相关随机实现时，模型输出不确定性增加。结果表明，参数不确定性的相对传播取决于模拟情况（边界条件）和土壤本身。对于相关的VGM随机实现，用于分析参数重要性的两个标准都检测到了VGM参数l是确定水平衡要素变化中最相关的要素之一。这是一个有趣的结果，因为几乎从未确定过此参数，通常将其假定为预定义值。