Estimating Soil Hydraulic Parameters during Ponding Infiltration Using a Hybrid Algorithm

Accurate inversion of soil hydraulic parameters based on the van Genuchten-Mualem model has received much attention in soil science research. Herein, a hybrid algorithm method using particle swarm optimization and vector-evaluated genetic algorithm was used to invert the parameters theta(s), alpha, n, and K-s, with the objective functions of infiltration rate, cumulative infiltration, and soil water content. Then, numerical experiments were conducted on four typical soils at three initial water content levels (20, 40, and 60% effective saturation) to verify the accuracy of the inverse method. The results showed that the inversed soil water retention and conductivity curves were approximately the same as the real curves, with the root mean square errors of 0.00101-0.00192 cm(3)center dot cm(-3), 0.00800-0.02519 cm(3)center dot cm(-3), respectively, and both the Nash-Sutcliffe coefficients were approximately 1.0. Additionally, laboratory experiments were also performed to compare with the inversed parameters for verification, within small root mean squared errors and approximately 1.0 Nash-Sutcliffe coefficients. Furthermore, the method can also achieve acceptably accurate parameter inversion even with substantial measurement errors included in the cumulative infiltration, initial water content, and final water content. Thus, the method is effective and robust and found to be practical in field experiments.

Automated summary

Accurate inversion of soil hydraulic parameters based on the van Genuchten-Mualem model has been achieved through a hybrid algorithm method using particle swarm optimization and vector-evaluated genetic algorithm. The inverse method was verified through numerical experiments and laboratory experiments, showing good accuracy and robustness. The method is found to be practical in field experiments, even with substantial measurement errors.