Climate-driven soil suction variation using a natural-order Fourier series approach

In unsaturated soil practice, the near-ground-surface moisture flow is commonly evaluated using the 1-dimensional (1D) suction diffusion equation. This study presents the application of a Natural-Order Fourier Series (NOFS) approach for representing monthly climate-driven soil suction variations near the ground surface, which is a boundary condition that is generally difficult to adequately model. The NOFS incorporates an algorithmic selection criterion to optimize the order of the Fourier series to improve the capture of the seasonal shifts and extreme climate periods, while maintaining acceptable computation efficiency. The climate-soil suction interaction is expressed using published empirical relationships between monthly rainfall, temperature, and soil index properties. An example and validation study of the proposed NOFS selection approach is presented using measured soil properties and historical weather data at a study site in Denver, Colorado USA. Key findings from performance and stability studies of five locations in the United States associated with differing climate regions are discussed. Limitations and recommendations for implementation are also included. The proposed NOFS approach for capturing climate-driven changes in suction near the ground surface can be efficiently implemented in unsaturated soil numerical analyses that are governed by moisture-dependent mechanical soil behavior and can help improve the computation time, stability, and performance associated with stochastic simulations.