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A Lagrangian model for soil water dynamics during rainfall-driven conditions

机译:降雨驱动条件下土壤水分动力学的拉格朗日模型

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Within this study we propose a stochastic approach to simulate soil water dynamics in the unsaturated zone by using a non-linear, space domain random walk of water particles. Soil water is represented by particles of constant mass, which travel according to the It? form of the Fokker–Planck equation. The model concept builds on established soil physics by estimating the drift velocity and the diffusion term based on the soil water characteristics. A naive random walk, which assumes all water particles to move at the same drift velocity and diffusivity, overestimated depletion of soil moisture gradients compared to a Richards solver. This is because soil water and hence the corresponding water particles in smaller pore size fractions are, due to the non-linear decrease in soil hydraulic conductivity with decreasing soil moisture, much less mobile. After accounting for this subscale variability in particle mobility, the particle model and a Richards solver performed highly similarly during simulated wetting and drying circles in three distinctly different soils. Both models were in very good accordance during rainfall-driven conditions, regardless of the intensity and type of the rainfall forcing and the shape of the initial state. Within subsequent drying cycles the particle model was typically slightly slower in depleting soil moisture gradients than the Richards model. brbr Within a real-world benchmark, the particle model and the Richards solver showed the same deficiencies in matching observed reactions of topsoil moisture to a natural rainfall event. The particle model performance, however, clearly improved after a straightforward implementation of rapid non-equilibrium infiltration, which treats event water as different types of particles, which travel initially in the largest pore fraction at maximum velocity and experience a slow diffusive mixing with the pre-event water particles. The proposed Lagrangian approach is hence a promising, easy-to-implement alternative to the Richards equation for simulating rainfall-driven soil moisture dynamics, which offers straightforward opportunities to account for preferential, non-equilibrium flow.
机译:在这项研究中,我们提出了一种随机方法,通过使用非线性,空间域的水颗粒随机游动来模拟非饱和区中的土壤水分动力学。土壤水以质量恒定的颗粒表示,哪些颗粒随It迁移?福克-普朗克方程的形式。模型概念基于已建立的土壤物理学,通过根据土壤水分特征估算漂移速度和扩散项。与Richards求解器相比,假定所有水粒子以相同的漂移速度和扩散率运动的幼稚随机游走,高估了土壤水分梯度的消耗。这是因为由于土壤水力传导率随土壤水分的减少而非线性下降,土壤水以及相应的较小孔径分数的水颗粒的流动性大大降低。考虑到颗粒迁移率的这种亚尺度变化性之后,在三种截然不同的土壤中,模拟的润湿和干燥循环期间,颗粒模型和Richards求解器的性能非常相似。不管降雨强迫的强度和类型以及初始状态的形状如何,两种模型在降雨驱动条件下都非常吻合。在随后的干燥循环中,颗粒模型在减少土壤水分梯度方面通常会比Richards模型慢一些。 在一个实际基准中,粒子模型和Richards求解器在将表层土壤水分与自然降雨事件的观测反应匹配方面显示出相同的缺陷。但是,直接实施快速非平衡渗透后,颗粒模型的性能明显得到改善,该过程将事件水视为不同类型的颗粒,它们最初以最大速度在最大孔隙率中传播,并与预分离器缓慢扩散混合。事件水颗粒。因此,拟议的拉格朗日方法是一种有前途的,易于实现的替代方案,可以代替Richards方程来模拟降雨驱动的土壤水分动力学,从而为解释优先的非平衡流提供了直接的机会。

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