Muhammad Naveed1, Per Moldrup2, Markus Tuller3, Ty Ferre4, Ken Kawamoto5, Toshiko Komatsu5 and Lis Wollesen de Jonge1, (1)Agroecology, Aarhus University, Tjele, Denmark (2)Department of Biotechnology, Chemistry and Environmental Engineering, Aalborg University, Aalborg, Denmark (3)SWES Department, University of Arizona, Tucson, AZ (4)Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ (5)Saitama University, Saitama, , JAPAN
Modelling water distribution and flow in partially saturated soils requires knowledge of the soil-water characteristic (SWC). However, measurement of the SWC is challenging and time-consuming, and in some cases not feasible. This study introduces two predictive models (Xw-model and Xw*-model) for the SWC, derived from readily available soil properties such as texture and bulk density. A total of 46 soils from different horizons at 15 locations across Denmark were used for models evaluation. The Xw-model predicts the volumetric water content as a function of volumetric fines content (organic matter and clay). It performed reasonably well for the dry-end (above a pF value of 2.0; pF = log(|h|), where h is the matric potential in cm), but did not do as well closer to saturated conditions. The Xw*-model gives the volumetric water content as a function of volumetric content of particle size fractions (organic matter, clay, silt, fine and coarse sand), variably included in the model depending on the pF value. The volumetric content of a particular soil particle size fraction was included in the model if it was assumed to contribute to the pore size fraction still occupied with water at the given pF value. Hereby, the Xw*-model implicitly assumes that a given particle size fraction creates an analogue pore size fraction and, also, is based on the validity of the well-known capillary law equation relating equivalent drained pore size to the soil-water matric potential. The Xw*-model was found to be quite robust, and it performed exceptionally well for all tested pF values ranging from 0.4 to 4.2. For prediction of the continuous SWC, it is recommended that the van Genuchten model is parameterized based on the SWC data points predicted by the Xw*-model.