© 2014, (publisher). All rights reserved. Management and irrigation of plants increasingly relies on accurate mathematical models for the movement of water within unsaturated soils. Current models often use values for water content and soil parameters that are averaged over the soil profile. However, many applications require models to more accurately represent the soil–plant–atmosphere continuum, in particular, water movement and saturation within specific parts of the soil profile. In this paper a mathematical model for water uptake by a plant root system from unsaturated soil is presented. The model provides an estimate of the water content level within the soil at different depths, and the uptake of water by the root system. The model was validated using field data, which include hourly water content values at five different soil depths under a grass/herb cover over 1 year, to obtain a fully calibrated system for plant water uptake with respect to climate conditions. When compared quantitatively to a simple water balance model, the proposed model achieves a better fit to the experimental data due to its ability to vary water content with depth. To accurately model the water content in the soil profile, the soil water retention curve and saturated hydraulic conductivity needed to vary with depth.
|Original language||English (US)|
|Number of pages||14|
|State||Published - Jun 2014|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-C1-013-04
Acknowledgements: The authors would like to thank the Engineering and Physical Sciences Research Council (EPSRC) for funding J. Smethurst (grant numbers GR/R72341/01 and EP/F063482/01), the Biotechnology and Biological Sciences Research Council (BBSRC) for funding S. Payvandi, The Royal Society University Research Fellowship for funding T. Roose, award no. KUK-C1-013-04 of the King Abdullah University of Science and Technology (KAUST) for funding K. Zygalakis, EPSRC and the Centre for Operational Research, Management Science and Information Systems (CORMSIS) for funding J. Fliege, and EPSRC Complexity DTC for funding J. Heppell (EP/G03690X/1). The authors acknowledge the use of the IRIDIS high-performance computing facility, and associated support services at the University of Southampton, in the completion of this work.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.