Soil water movement has direct effects on environment, agriculture and hydrology. Simulation of soil water movement requires accurate determination of model parameters as well as initial and boundary conditions. However, it is difficult to obtain the accurate initial soil moisture or matric potential profile at the beginning of simulation time, making it necessary to run the simulation model from the arbitrary initial condition until the uncertainty of the initial condition?(UIC) diminishes, which is often known as “warming up”. In this paper, we compare two commonly used methods for quantifying the UIC (one is based on running a single simulation recursively across multiple hydrological years, and the other is based on Monte Carlo simulations with realization of various initial conditions) and identify the warm-up time?twu (minimum time required to eliminate the UIC by warming up the model) required with different soil textures, meteorological conditions and soil profile lengths. Then we analyze the effects of different initial conditions on parameter estimation within two data assimilation frameworks (i.e., ensemble Kalman filter and iterative ensemble smoother) and assess several existing model initializing methods that use available data to retrieve the initial soil moisture profile. Our results reveal that Monte Carlo simulations and the recursive simulation over many years can both demonstrate the temporal behavior of the UIC, and a common threshold is recommended to determine?twu. Moreover, the relationship between twu?for variably saturated flow modeling and the model settings (soil textures, meteorological conditions and soil profile length) is quantitatively identified. In addition, we propose a warm-up period before assimilating data in order to obtain a better performance for parameter and state estimation.
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机译：土壤水运动对环境，农业和水文产生直接影响。土壤水运动模拟需要准确确定模型参数以及初始和边界条件。然而，在模拟时间开始，难以获得准确的初始土壤湿度或马门潜在曲线，使得必须从任意初始条件运行模拟模型，直到初始条件的不确定性？（UIC）减少，这通常被称为“热身”。在本文中，我们比较了两个用于量化UIC的常用方法（一个是基于在多个水文岁年递归地运行单个模拟，而另一个是基于蒙特卡罗模拟，实现各种初始条件，并识别温暖 - up time？twu（通过预热模型消除UIC所需的最短时间）需要不同的土壤纹理，气象条件和土壤轮廓长度。然后，我们分析了不同初始条件对两个数据同化框架中的参数估计的影响（即，集合Kalman滤波器和迭代集合更顺畅），并评估使用可用数据来检索初始土壤湿度曲线的几种现有模型初始化方法。我们的结果表明，蒙特卡罗模拟和多年来递归模拟都可以证明UIC的时间行为，建议使用常见的阈值来确定TWU。此外，TWU之间的关系？对于可变饱和的流动建模和模型设置（土壤纹理，气象条件和土壤轮廓长度）是定量的。此外，我们提出了在吸收数据之前的预热时段，以便获得更好的参数和状态估计性能。
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