Prediction of soil moisture scarcity using sequential Gaussian simulation in an arid region of China

Soil moisture plays a vital role in maintaining the sustainability of dryland ecosystems. Accurately predicting soil moisture scarcity (SMS) has an important interest of guidance to soil and water conservation. In this study, we gathered a time series of soil moisture measurements throughout the growing season (from April to October) in an area of approximately 100 km(2) in a desert oasis of northwestern China. Sequential Gaussian simulation was applied to investigate the spatial variability and scarcity of soil moisture across multiple land use types. Soil moisture exhibited considerable spatial heterogeneity with different magnitudes of spatial dependence at different times. Two hundred simulated realizations depicted the possible spatial variations of soil moisture in the geographic space. SMS was characterized as the natural event that occurred when the spatial probability of soil moisture not exceeding 0.15 cm(3) cm(-3) was greater than a critical threshold. With the increasing of probability thresholds, the proportion of SMS locations in each land use decreased at different rates. Given the spatial probability threshold of 0.6,13-3.8% of the cultivated land, 2.6-5.2% of the forest land, 3.2-4.6% of the grassland, and 2.7-7.4% of the shrub land were of SMS during the measuring period. The newly cultivated land and the ecotone of desert and oasis were the major regions SMS occurred. Some soil moisture conservation measures such as precision irrigation should be taken to prevent the probable land degradation and agricultural disasters in these areas. The prediction of SMS using stochastic simulation contributes to improving soil water management in the oasis and provides a methodology reference for similar studies in risk analysis. (C) 2017 Elsevier B.V. All rights reserved.