Characterization of Saturated Hydraulic Conductivity in an Agricultural Field Using Karhunen-Loève Expansion with MCMC

Process-based soil hydrologic models require input of saturated hydraulic conductivity (Ksat). However, model users often have limited access to measured data and thus use published or estimated values for many site-specific hydrologic and environmental applications. We proposed an algorithm that uses the Karhunen-Loéve Expansion (KLE) in conjunction with Markov Chain Monte Carlo (MCMC) technique which employs measured soil moisture values to characterize the saturated hydraulic conductivity of an agricultural field at a fine resolution. The study domain is situated in the Walnut Creek watershed, Iowa with soybean crop (in 2005) and well defined top (atmospheric) and bottom (ground water) boundary conditions. The KLE algorithm parameterize and generates Ksat fields with random correlation length that is used in the SWMS_3D model for predicting soil moisture dynamics for two different scenarios: 1) the van Genuchten soil hydraulic parameters (except Ksat) are constant and are based on the soil type of the grid block within the domain; 2) Ksat is correlated with the van Genuchten parameter α, as Ksat α2. The predicted soil moisture for both scenarios are evaluated with the measured soil moisture in the MCMC algorithm for acceptance (or rejection) of the Ksat fields. The accepted Ksat fields are evaluated and validated against the laboratory measured Ksat at specific locations and the comparison shows reasonable agreement. The KLE- MCMC algorithm was further tested in the same study domain for another year (2002) having different vegetation (corn) and local forcings. The algorithm shows potential to characterize the saturated hydraulic conductivity fields at fine-scale using inexpensive and more regularly measured soil moisture measurements. Further studies are required to incorporate variability in different hydro-climatic regions and diverse topography to extend the application of this algorithm.