Given the cumbersome determination method of Soil Water Characteristic Curve (SWCC), the collapsible loess (silty clay loam) in Lanzhou was taken as the research object to explore the method of symmetrical prediction of SWCC in the low suction section on the inflection point, and determine the optimal suction section and the inflection point. The results showed that in the range of 0–7,000 cm suction, the spatial variation coefficient (CV) of soil saturation of each bulk density increased with the increase of suction. Soil saturation showed weak spatial variability when suction <800 cm, and moderate spatial variability when suction ≥800 cm. Using a bulk density of 1.58 g/cm3 as an example, taking the bulk density of 1.58 g/cm3 as an example, the SWCC determined by the symmetry of the bending point was compared with the measured data of 0–300, 0–500, 0–800 and 0–1,000 cm suction sections. It was found that the measured soil saturation of SWCC determined by the data of 0–800 cm suction section was the highest consistent with the predicted value. The measured and predicted saturation points of the SWCC were most consistent with suction segments of 0–800 cm. SWCC data of different textures and bulk density were used to verify the prediction method of SWCC at low suction section and inflection point of 0–800 cm. It was found that the average absolute error and root mean square error of statistical indicators were close to 0, and the correlation coefficient was greater than 0.9915. The actual and predicted values of each soil parameter were linearly correlated. This method of predicting SWCCs with low suction and inflection points ensures both a high degree of curve fitting and the accuracy of characteristic soil parameters, providing a simple method for the prediction of SWCCs and guidance for managing soil water in loessial areas.
In the low suction range, soil saturation increases with suction and CV increases.
The low suction inflection point was used to predict the soil moisture characteristic curve, which effectively reduced the deviation of CV.
SWCC prediction provides guidance for soil moisture management in loess area.