Simultaneous Prediction of Saturated Hydraulic Conductivity and Drainable Porosity Using the Inverse Problem Technique and Numerical Solution of the Boussinesq Equation around Subsurface Drainage

Document Type : Original Article

Authors

1 Graduated Student of M.Sc. Department of Irrigation., Sari Agricultural Sciences and Natural Resources University., Sari., Iran

2 Associate Professor, Water Engineering Department, University of Sari Agricultural Sciences and Natural Resources., Sari., Iran

3 PhD student, Department of Irrigation., Sari Agricultural Sciences and Natural Resources University., Sari., Iran

Abstract

Saturated hydraulic conductivity and effective porosity are important parameters in groundwater modeling, water movement in the soil and solute transport. This study aimed to evaluate the accuracy of prediction of these two parameters simultaneously using numerical solution of one-dimensional Boussinesq equations governing the unsteady and saturation flow. In the proposed inverse method the genetic algorithm method for optimization and from control volume method for numerical solution of the governing equation are used. In order to collecting the required data a physical model with the length of 4 meters, a width of 2 meters and a height of 1.8 meters was used and the height of water table was read by 20 observation wells embedded in the model in different times. The value of saturated hydraulic conductivity (k) and effective porosity (µ) was measured directly. The result showed that genetic algorithm is a powerful tool for optimization of inverse method because in this study saturated hydraulic conductivity and effective porosity were evaluated with a high accuracy. Result showed that the height of water table was predicted with reasonable accuracy by inverse method, so that statistical parameters RMSE, MAE, ME, EF are 22, 18, 53 mm and 94 percent, respectively. The result implies that over time caused the used method has high accuracy so the ability to predict in the end times reverse the first time.

Keywords


علیزاده،ا. 1382. زهکشی اراضی (طرح و برنامه‌ریزی سیستم­های زهکشی در کشاورزی). دانشگاه فردوسی مشهد. 460 صفحه.
Amor,V.M and Droogers,P. 2002. Inverse modelling in estimating soil hydraulic functions: A genetic algorithm approach. Hydrology and Earth System Sciences. 6.1: 49-65.
Fathi,P., Samani,j and Kouchakzadeh,M. 2006. Prediction of soil hydraulic parameters by inverse method. Journal of Water and Soil Science. 1: 1-9.
Hore,F.R. 1959. Pizometer methods in ontario. Agricultural Engineering, pp. 272-278.
Mahbod,M and Zand-Parsa,S. 2010. Prediction of soil hydraulic parameters by inverse method using genetic algorithm optimization under field conditions. Archives of Agronomy and Soil Science. 56:13-28.
Majdalani,S., Angulo-Jaramillo,R and Di Pietro,L. 2008. Estimating preferential water flow parameters using a binary genetic algorithm inverse method. Environmental Modelling and Software. 23: 950–956.
Mao,D., Yeh,T., Wan,L., Hsu,K., Lee,C and Wen,J. 2013. Necessary conditions for inverse modeling of flow through variably saturated porous media. Advances in Water Resources. 52:50-61.
Minasny,B and Field,D.J. 2005. Estimating soil hydraulic properties and their uncertainty: the use of stochastic simulation in the inverse modelling of the evaporation method. Geoderma. 126: 277–290.
Pandey,R.S., Bhattacharya,A.K., Singh,O.P and Gupta,S.K. 1992. Drawdown solutions with variable drainable porosity. Journal of Irrigation and Drainage Engineering. 118: 382–396.
Ritter,A., Hupet,F., Munoz-Carpena,R., Lambot,S and Vanclooster,M. 2003. Using inverse methods for estimating soil hydraulic properties from field data as an alternative to direct methods. Agricultural Water Management. 59: 77–96.
Samani,J.M.V., Fathi,P and Homaee,M. 2007. Simultaneous Prediction of Saturated Hydraulic Conductivity and Drainable Porosity Using the Inverse Problem Technique. Journal of Irrigation and Drainage Engineering. 133: 110–115.
Schelle,H., Durner,W., Iden,S.C and Fank,J. 2013. Simultaneous estimation of soil hydraulic and root distribution parameters from lysimeter data by Inverse modeling. procedia environmental sciences. 19:564–573.