Modelling available water capacity of topsoil in a Bayesian paradigm. (October 2019) - Achieng, Kevin O.
Abstract: Available water capacity (AWC) is a fundamental factor in energy-water nexus. Particularly, AWC influences climate dynamics, hydrological processes, and water management. Measuring AWC is often laborious, time consuming, and sometimes impossible. Pedotransfer functions (PTFs) are therefore used as alternatives to model AWC. Classical statistical-based pedotransfer functions are widely used to model AWC. However, these classical statistical models do not incorporate prior knowledge of the existing AWC data. Therefore, the objective of this study is two-fold: Firstly, to develop AWC PTFs for topsoil using five physical soil properties; and secondly, to evaluate relative importance of the physical soil properties in the Bayesian-based AWC model. Results show that Bayesian PTFs plausibly simulate AWC with percent bias (PBIAS) of <1%; root mean square error (RMSE) of 0.021 to 0.027 cm/cm; and degree of agreement (d1) of 0.6–0.79. Silt had the greatest influence on the AWC, both inside and outside Bayesian framework. Highlights: New pedotransfer models are developed in a Bayesian framework for topsoil AWC. Silt has the greatest influence on AWC – both inside and outside Bayesian framework. The relative importance of sand and organic matter on AWC PTFs are region-dependent.