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An Inverse Modeling Approach to Calibrate Parameters for a Drainage Model with Two Optimization Algorithms on Homogeneous/Heterogeneous Soil

  • Amir Sedaghatdoost
  • Hamed EbrahimianEmail author
  • Abdolmajid Liaghat
Article
  • 46 Downloads

Abstract

Due to the time and spatial limitations of subsurface drainage pilots, simulation models have been extensively applied for evaluating these systems. Since the accuracy of simulation models depends enormously on the accuracy of model parameters, this study aims to develop an inverse modeling approach for estimating most influential soil hydraulic and solute transport parameters in a subsurface drainage system in an arid and semi-arid region. The SWAP model in conjunction with a genetic algorithm and PEST optimization tool was used to find optimum parameters by minimizing the differences between observed and simulated values of drainage discharge, watertable depth, and drainage salinity. Results revealed that the best simulation of drainage outputs was obtained by parameters which were estimated minimizing an objective function that included all three datasets via a genetic algorithm. Although assuming the soil as a homogeneous and heterogeneous medium had quite similar results from objective functions with one or two datasets, homogeneous assumption worked better in the objective function with three datasets. The inverse modelling approach with GA resulted in a better performance as compared to the PEST optimization tool, particularly in objective functions with two or three datasets.

Keywords

Indirect methods Soil properties Simulation Drainage Khuzestan 

Notes

Compliance with Ethical Standards

Conflict of Interest Statement

None.

References

  1. Carroll DL (1996) Chemical laser modeling with genetic algorithms. Am Inst Aeronaut Astronaut J 34(2):338–346CrossRefGoogle Scholar
  2. Dane JH, Hruska S (1983) In-situ determination of soil hydraulic properties during drainage. Soil Sci Soc Am J 47(4):619–624CrossRefGoogle Scholar
  3. Dane JH, Topp GC (2002) Part 4. Physical Methods. Methods of Soil Analysis. Soil Science Society of America, WisconsinGoogle Scholar
  4. Doherty J (2005) PEST: model independent parameter estimation, fifth edition of user manual. Watermark Numerical Computing, BrisbaneGoogle Scholar
  5. Droogers P, Immerzeel WW, Lorite IJ (2010) Estimating actual irrigation application by remotely sensed evapotranspiration observations. Agric Water Manag 97(9):1351–1359CrossRefGoogle Scholar
  6. Durner W, Jansen U, Iden SC (2008) Effective hydraulic properties of layered soils at the lysimeter scale determined by inverse modelling. Eur J Soil Sci 59(1):114–124Google Scholar
  7. Ebrahimian H, Parsinejad M, Liaghat A, Akram M (2011) Field research on the performance of a rice husk envelope in a subsurface drainage system (case study Behshahr Iran). Irrig Drain 60(2):216–228CrossRefGoogle Scholar
  8. Ebrahimian H, Liaghat A, Parsinejad M, Playán E, Abbasi F, Navabian M, Lattore B (2013) Optimum design of alternate and conventional furrow fertigation to minimize nitrate loss. J Irrig Drain Eng 139(11):911–921CrossRefGoogle Scholar
  9. El-Sadek A, Feyen J, Berlamont J (2001) Comparison of models for computing drainage discharge. J Irrig Drain Eng 127(6):363–369CrossRefGoogle Scholar
  10. Farmaha BS (2014) Evaluating Animo model for predicting nitrogen leaching in rice and wheat. Arid Land Res Manag 28(1):25–35CrossRefGoogle Scholar
  11. Hopmans JW, Šimunek J, Bristow KL (2002) Indirect estimation of soil thermal properties and water flux using heat pulse probe measurements: geometry and dispersion effects. Water Resour Res 38(1):71–714CrossRefGoogle Scholar
  12. Ines AV, Droogers P (2002) Inverse modelling in estimating soil hydraulic functions: a genetic algorithm approach. Hydrol Earth Syst Sci Discuss 6(1):49–66CrossRefGoogle Scholar
  13. Ines AV, Mohanty BP (2008) Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithm: 1. Conceptual modeling. Water Resour Res 44(6)Google Scholar
  14. Irmak A, Kamble B (2009) Evapotranspiration data assimilation with genetic algorithms and SWAP model for on-demand irrigation. Irrig Sci 28(1):101–112CrossRefGoogle Scholar
  15. Jhorar RK, Van Dam JC, Bastiaanssen WGM, Feddes RA (2004) Calibration of effective soil hydraulic parameters of heterogeneous soil profiles. J Hydrol 285(1):233–247CrossRefGoogle Scholar
  16. Kroes JG, Van Dam JC, Groenendijk P, Hendriks RFA, Jacobs CMJ (2008) SWAP version 3.2. Theory description and user manual. Netherlands: AlterraGoogle Scholar
  17. Lai J, Ren L (2016) Estimation of effective hydraulic parameters in heterogeneous soils at field scale. Geoderma 264:28–41CrossRefGoogle Scholar
  18. Mertens J, Kahl G, Gottesbüren B, Vanderborght J (2009) Inverse modeling of pesticide leaching in lysimeters: local versus global and sequential single-objective versus multiobjective approaches. Vadose Zone J 8(3):793–804CrossRefGoogle Scholar
  19. Mishra S, Parker JC (1989) Parameter estimation for coupled unsaturated flow and transport. Water Resour Res 25(3):385–396Google Scholar
  20. Negm L, Youssef M, Skaggs R, Chescheir G, Kladivko E (2014) DRAINMOD-DSSAT simulation of the hydrology, nitrogen dynamics, and plant growth of a drained corn field in Indiana. J Irrig Drain Eng 140(8):04014026CrossRefGoogle Scholar
  21. Noory H, Van Der Zee SEATM, Liaghat AM, Parsinejad M, Van Dam JC (2011) Distributed agro-hydrological modeling with SWAP to improve water and salt management of the Voshmgir irrigation and drainage network in northern Iran. Agric Water Manag 98(6):1062–1070CrossRefGoogle Scholar
  22. Praveen K, Alameda J, Bajcsy P, Folk M, Markus M (2006) Hydro informatics data integrative approaches in computation, analysis, and modeling. Florida: CRC PressGoogle Scholar
  23. Qureshi AS, Ahmad W, Ahmad AFA (2013) Optimum groundwater table depth and irrigation schedules for controlling soil salinity in Central Iraq. Irrig Drain 62(4):414–424Google Scholar
  24. Reshma T, Reddy KV, Pratap D, Ahmedi M, Agilan V (2015) Optimization of calibration parameters for an event based watershed model using a genetic algorithm. Water Resour Manag 29(13):4589–4606CrossRefGoogle Scholar
  25. Ritter A, Hupet F, Muñoz-Carpena R, Lambot S, Vanclooster M (2003) Using inverse methods for estimating soil hydraulic properties from field data as an alternative to direct methods. Agric Water Manag 59(2):77–96CrossRefGoogle Scholar
  26. Ritter A, Muñoz-Carpena R, Regalado CM, Vanclooster M, Lambot S (2004) Analysis of alternative measurement strategies for the inverse optimization of the hydraulic properties of a volcanic soil. J Hydrol 295(1):124–139CrossRefGoogle Scholar
  27. Ritzema HP (1994) Drainage principles and applications. Netherlands: International Institute for Land Reclamation and Improvement (ILRI)Google Scholar
  28. Sarwar A, Bastiaanssen WGM, Boers TM, Van Dam JC (2000) Devaluating drainage design parameters for the fourth drainage project, Pakistan by using SWAP model: part I–calibration. Irrig Drain Sys Eng 14 (4):257–280Google Scholar
  29. Sedaghatdoost A, Ebrahimian H (2015) Calibration of infiltration, roughness and longitudinal dispersivity coefficients in furrow fertigation using inverse modelling with a genetic algorithm. Biosyst Eng 136:129–139CrossRefGoogle Scholar
  30. Sedaghatdoost A, Ebrahimian H, Liaghat AM (2018) Estimating soil hydraulic and solute transport parameters in subsurface drainage systems using an inverse modelling approach. Irrig Drain 67(2):82–90CrossRefGoogle Scholar
  31. Shin Y, Mohanty BP, Ines AV (2012) Soil hydraulic properties in one-dimensional layered soil profile using layer-specific soil moisture assimilation scheme. Water Resour Res 48(6)Google Scholar
  32. Šimůnek J, Genuchten MV (1996) Estimating unsaturated soil hydraulic properties from tension disc infiltrometer data by numerical inversion. Water Resour Res 32(9):2683–2696CrossRefGoogle Scholar
  33. Skaggs RW, Youssef MA, Chescheir GM (2012) DRAINMOD: model use, calibration, and validation. Trans ASABE 55(4):1509–1522CrossRefGoogle Scholar
  34. Smedema LK, Vlotman WF, Rycroft DW (2004) Modern land drainage: Planning, design and management of agricultural drainage systems. Florida: CRC PressGoogle Scholar
  35. Sonnleitner MA, Abbaspour KC, Schulin R (2003) Hydraulic and transport properties of the plant–soil system estimated by inverse modelling. Eur J Soil Sci 54(1):127–138CrossRefGoogle Scholar
  36. Van Dam JC, Groenendijk P, Hendriks RF, Kroes JG (2008) Advances of modeling water flow in variably saturated soils with SWAP. Vadose Zone J 7(2):640–653CrossRefGoogle Scholar
  37. Vazifedoust M, Van Dam JC, Feddes RA, Feizi M (2008) Increasing water productivity of irrigated crops under limited water supply at field scale. Agric Water Manag 95(2):89–102CrossRefGoogle Scholar
  38. Verma AK, Gupta SK, Isaac RK (2010) Long-term use of saline drainage waters for irrigation in subsurface drained lands: simulation modelling with SWAP. J Agric Eng Res 47(3):15–23Google Scholar
  39. Vrugt JA, Stauffer PH, Wöhling T, Robinson BA, Vesselinov VV (2008) Inverse modeling of subsurface flow and transport properties: a review with new developments. Vadose Zone J 7(2):843–864CrossRefGoogle Scholar
  40. Wang X, Mosley CT, Frankenberger JR, Kladivko EJ (2006) Subsurface drain flow and crop yield predictions for different drain spacings using DRAINMOD. Agric Water Manag 79(2):113–136CrossRefGoogle Scholar
  41. Wohling T, Vrugt JA, Barkle GF (2008) Comparison of three multiobjective optimization algorithms for inverse modeling of vadose zone hydraulic properties. Soil Sci Soc Am J 72(2):305–319Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Amir Sedaghatdoost
    • 1
  • Hamed Ebrahimian
    • 2
    Email author
  • Abdolmajid Liaghat
    • 3
  1. 1.Department of Biological and Agricultural EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural ResourcesUniversity of TehranKarajIran
  3. 3.Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural ResourcesUniversity of TehranTehranIran

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