Temporal dynamics of subsurface soil water content estimated from surface measurement using wavelet transform

Abstract

Temporal variation of water in the vadose zone is important to understand processes such as solute transport and nutrient cycling. Measurements of soil water content (SWC) in the subsurface are less common than those near the surface and predictions using numerical models are limited by data availability. Wavelet decomposition of surface measurements of SWC could improve modeling of subsurface SWC by segregating features at different temporal scales and projecting them to the subsurface. The objectives of this work were to: 1) predict subsurface SWC using surface SWC and a combination of wavelet analysis and linear regression, and 2) investigate relationships between soil properties and the movement of soil water at various temporal scales, s. Climate data and SWC at various depths were collected from eight sites in the Atlantic Coastal Plain of the USA. Soil water retention and hydraulic conductivity (k) functions of each horizon were optimized by comparing measured and predicted (using HYDRUS-1D) soil water contents. Each time series of SWC was decomposed into 50 scale components using the Mexican Hat wavelet and later reduced to 5 group components with minimal impact on the characteristics of the signal. Changes in the values of each group component with depth were represented with transfer coefficients that could be estimated with predictors derived from particle size distributions and optimized soil hydraulic functions. Prediction depth and saturated k were the two most important predictors for s < 256 h, while k at −10 kPa was the best predictor for 256 h < s < 724 h, and the median value of particle size diameters for s > 724 h. Subsurface soil water content can be reasonably predicted with the proposed approach, particularly when vertical movement of soil water is unrestricted.