Using a hybrid model to predict solute transfer from initially saturated soil into surface runoff with controlled drainage water View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-06

AUTHORS

Juxiu Tong, Bill X. Hu, Jinzhong Yang, Yan Zhu

ABSTRACT

The mixing layer theory is not suitable for predicting solute transfer from initially saturated soil to surface runoff water under controlled drainage conditions. By coupling the mixing layer theory model with the numerical model Hydrus-1D, a hybrid solute transfer model has been proposed to predict soil solute transfer from an initially saturated soil into surface water, under controlled drainage water conditions. The model can also consider the increasing ponding water conditions on soil surface before surface runoff. The data of solute concentration in surface runoff and drainage water from a sand experiment is used as the reference experiment. The parameters for the water flow and solute transfer model and mixing layer depth under controlled drainage water condition are identified. Based on these identified parameters, the model is applied to another initially saturated sand experiment with constant and time-increasing mixing layer depth after surface runoff, under the controlled drainage water condition with lower drainage height at the bottom. The simulation results agree well with the observed data. Study results suggest that the hybrid model can accurately simulate the solute transfer from initially saturated soil into surface runoff under controlled drainage water condition. And it has been found that the prediction with increasing mixing layer depth is better than that with the constant one in the experiment with lower drainage condition. Since lower drainage condition and deeper ponded water depth result in later runoff start time, more solute sources in the mixing layer are needed for the surface water, and larger change rate results in the increasing mixing layer depth. More... »

PAGES

12444-12455

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11356-016-6452-4

DOI

http://dx.doi.org/10.1007/s11356-016-6452-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015500152

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26983916


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