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Soil water dynamics play an active role in ecological and hydrologic processes. Soil water exhibits a stochastic nature because of the large temporal variations in precipitation and evapotranspiration. Objectives of this study were to analyse the probabilistic nature of soil water under three vegetation types, to simulate their probability density functions (PDFs) using a stochastic model, and to determine the most sustainable vegetation types. Soil water data were collected over a 3-year period with a bi-weekly frequency at plots in the Loess Plateau, China, under grass (Bothriochloa ischaemum L., BOI), shrub, sea-buckthorn (Hippophae rhamnoides L., SEB) and tree, Chinese pine (Pinus tabulaeformis Carr, CHP). The data were compared with values simulated using the Laio stochastic model. The results showed that the mean relative soil water contents differed in the order: BOI > CHP > SEB. Soil water was related to the daily rainfall and evapotranspiration. Under the same climate, topography and soil conditions, the soil water PDF was sensitive to a critical water content at which plants begin closing stomata and the mean maximum daily evapotranspiration rate. Based on the shape of the PDFs and their statistical moments, the Laio stochastic model accurately simulated the soil water PDFs under all three vegetation types in the semi-humid area of the Loess Plateau. The soil water PDFs for three vegetation types were simulated with four leaf area index scenarios; BOI and CHP were the most sustainable vegetation types compared with SEB in terms of maintaining soil water availability and soil erosion control.

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