Soil moisture simulation and prediction in semi-arid regions are important for agricultural production, soil conservation andclimate change. However, considerable heterogeneity in the spatial distribution of soil moisture, and poor ability of distributedhydrological models to estimate it, severely impact the use of soil moisture models in research and practical applications. Inthis study, a newly-developed technique of coupled （WA-ANN） wavelet analysis （WA） and artificial neural network （ANN）was applied for a multi-layer soil moisture simulation in the Pailugou catchment of the Qilian Mountains, Gansu Province,China. Datasets included seven meteorological factors： air and land surface temperatures, relative humidity, global radiation,atmospheric pressure, wind speed, precipitation, and soil water content at 20, 40, 60, 80, 120 and 160 cm. To investigate theeffectiveness of WA-ANN, ANN was applied by itself to conduct a comparison. Three main findings of this study were： （1）ANN and WA-ANN provided a statistically reliable and robust prediction of soil moisture in both the root zone and deepestsoil layer studied （NSE 〉0.85, NSE means Nash-Sutcliffe Efficiency coefficient）; （2） when input meteorological factors weretransformed using maximum signal to noise ratio （SNR） and one-dimensional auto de-noising algorithm （heursure） in WA,the coupling technique improved the performance of ANN especially for soil moisture at 160 cm depth; （3） the results ofmulti-layer soil moisture prediction indicated that there may be different sources of water at different soil layers, and this canbe used as an indicator of the maximum impact depth of meteorological factors on the soil water content at this study site. Weconclude that our results show that appropriate simulation methodology can provide optimal simulation with a minimumdistortion of the raw-time series; the new method used here is applicable to soil sciences and management applications.
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