Skip to main content

Advertisement

SpringerLink
Log in

Assessing an efficient hybrid of Monte Carlo technique (GSA-GLUE) in Uncertainty and Sensitivity Analysis of vanGenuchten Soil Moisture Characteristics Curve

  • Original Paper
  • Published:
Computational Geosciences Aims and scope Submit manuscript

A Correction to this article was published on 05 February 2021

This article has been updated

Abstract

Studying model uncertainty and identifying the parameter uncertainty in the modeling of water flow through the soil is useful to improve water and soil management. This research aimed to assess the uncertainty of the parameters of soil water retention curve (SWRC) models using an efficient hybrid of the Monte Carlo technique e.g. generalized likelihood uncertainty estimation (GLUE). GLUE estimates the parameters of vanGenuchten, vanGenuchten-Mualem, and vanGenuchten-Burdine models for four soil classes. Also, to evaluate the relative importance of the model parameters, generalized sensitivity analysis (GSA) was performed. The results of the uncertainty analysis showed that among the studied models, the vanGenuchten-Mualem model with the indices of S = 0.05, T = 0.4, d-factor = 0.25 and, PCI = 100 was considered as the most accurate model with the least uncertainty. Also, the results of GSA were demonstrated that alpha and n parameters were sensitive parameters in the models. Consequently, identifying the uncertainty of the SWRC model structure and its parameters, relevant models with higher accuracy can be used in the study of soil water processes, and better water resource allocation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Change history

  • 04 December 2020

    There are incorrect spelling and missing word in the article text that needs to be corrected.

  • 05 February 2021

    A Correction to this paper has been published: https://doi.org/10.1007/s10596-020-10024-z

References

  1. Abbasi, F.: Advanced soil physics. TehranUniv (In Persian), (2010)

  2. Abbaspour, K.C., Johnson, C.A., vanGenuchten, M.T.: Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J. 3(4), 1340–1352 (2004)

    Article  Google Scholar 

  3. Beven, K.: 12 Equifinality and Uncertainty in Geomorphological Modelling. In The Scientific Nature of Geomorphology: Proceedings of the 27th Binghamton Symposium in Geomorphology, Held 27–29 September J. W.S., 27: 289 (1996)

  4. Beven, K.: Rainfall-Runoff Modelling: the Primer. John Willey & Sons, Ltd (2001)

  5. Beven, K.: A manifesto for the equifinality thesis. J. Hydrol. 320(1–2), 18–36 (2006)

    Article  Google Scholar 

  6. Beven, K., Binley, A.: The future of distributed models: model calibration and uncertainty prediction. Hydrol. Process. 6, 279–298 (1992)

    Article  Google Scholar 

  7. Brandyk, A., Iczko, A., Majewski, G., Kleniewska, M., Krukowski, M.: Uncertainty of Deardorff’s soil moisture model based on continuous TDR measurements for sandy loam soil. J. Hydrol. Hydromech. 64(1), 23–29 (2016)

  8. Brooks, R., Corey, T.: HYDRAU Uc Properties of Porous Media. Hydrology Papers, Colorado State University, (1964)

  9. Calamak, M., Melih Yanmaz, A., Kentel, E.: Probabilistic evaluation of the effects of uncertainty in transient seepage parameters. J. Geotech. Geoenviron. 143(9), (2017)

  10. Cea, L., Legout, C., Grangeon, T., Nord, G.: Impact of model simplifications on soil erosion predictions: application of the GLUE methodology to a distributed event-based model at the hillslope scale. Hydrol. Process. 30(7), 1096–1113 (2016)

    Article  Google Scholar 

  11. Chirico, G.B., Medina, H., Romano, N.: Uncertainty in predicting soil hydraulic properties at the hillslope scale with indirect methods. J. Hydrol. 334(3–4), 405–422 (2007)

    Article  Google Scholar 

  12. Deng, H., Ye, M., Schaap, M.G., Khaleel, R.: Quantification of uncertainty in pedotransfer function-based parameter estimation for unsaturated flow modeling. Water Resour. Res. 45(4), (2009)

  13. Fan, Y.R., Huang, G.H., Baetz, B.W., Li, Y.P., Huang, K., Li, Z., Xiong, L.H.: Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: a hybrid sequential data assimilation and probabilistic collocation method. Environ. Model. Softw. 86, 30–49 (2016)

    Article  Google Scholar 

  14. Fredlund, D.G., Xing, A.: Equations for the soil-water characteristic curve. Can. Geotech. J. 31(4), 521–532 (1994)

    Article  Google Scholar 

  15. Ghavidelfar, S., Shamseldin, A.Y., Melville, B.W.: Estimation of soil hydraulic properties and their uncertainty through the Beerkan infiltration experiment. Hydrol. Process. 29(17), 3699–3713 (2015)

    Article  Google Scholar 

  16. Harrison, K.W., Kumar, S.V., Peters-Lidard, C.D., Santanello, J.A.: Quantifying the change in soil moisture modeling uncertainty from remote sensing observations using Bayesian inference techniques. Water. Resour. Res. 48(11), (2012)

  17. He, R., Pang, B.: Sensitivity and uncertainty analysis of the Variable Infiltration Capacity model in the upstream of Heihe River basin. P.I.A.H.S., 368, 312–316 (2015)

  18. Jakeman, A. J., Ghassemi, F., Dietrich, C. R., Musgrove, T. J., Whitehead, P. G.: Calibration and reliability of an aquifer system model using generalized sensitivity analysis. Proceedings of the conference held in The Hague, (1990)

  19. Klute, A., Dirksen, C.: Water retention: laboratory methods. Methods of soil analysis: part 1—physical and mineralogical methods, (methodsofsoilan1).Soil Science Society of America, American Society of Agronomy, (1986)

  20. Kosugi, K.I.: Lognormal distribution model for unsaturated soil hydraulic properties. Water. Resour. Res. 32(9), 2697–2703 (1996)

    Article  Google Scholar 

  21. Larsbo, M., Roulier, S., Stenemo, F., Kasteel, R., Jarvis, N.: An improved dual-permeability model of water flow and solute transport in the vadose zone. Vadose Zone J. 4(2), 398–406(2005)

  22. Liu, W., Luo, X., Huang, F., Fu, M.: Uncertainty of the soil–water characteristic curve and its effects on slope seepage and stability analysis under conditions of rainfall using the Markov Chain Monte Carlo Method. Water. J. 9(10) (2017)

  23. Lu, L., Jun, X., Chong-Yu, X., Jianjing, C., Rui, W.: Analysis of the sources of equifinality in hydrological models using GLUE methodology. Proc. of Symposium JS.4 at the Joint IAHS & IAH Convention, Hyderabad, India (2009)

  24. Mahboobi, A. A., Naderi, A. A.: Applied soil physics. (in Persian). Boali Sina University, Iran ( 2016)

  25. Minasny, B., Field, D.J.: Estimating soil hydraulic properties and their uncertainty: the use of stochastic simulation in the inverse modelling of the evaporation method. Geoderma. 126(3–4), 277–290 (2005)

    Article  Google Scholar 

  26. Mualem, Y.: A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 12(3), 513–522 (1976)

    Article  Google Scholar 

  27. Ratto, M., Tarantola, S., Saltelli, A.: Sensitivity analysis in model calibration: GSA-GLUE approach. Comput. Phys. Commun. 136(3), 212–224 (2001)

    Article  Google Scholar 

  28. Sabatine, S.M., Niemann, J.D., Greimann, B.P.: Evaluation of parameter and model uncertainty in simple applications of a 1D sediment transport model. J. Hydraul. Eng. 141(5), 04015002 (2015)

    Article  Google Scholar 

  29. Sarrazin, F., Pianosi, F., Wagener, T.: Global sensitivity analysis of environmental models: convergence and validation. Environ. Model. Softw. 79, 135–152 (2016)

    Article  Google Scholar 

  30. Shafiei, M., Ghahraman, B., Saghafian, B., Davary, K., Pande, S., Vazifedoust, M.: Uncertainty assessment of the agro-hydrological SWAP model application at field scale: a case study in a dry region. Agr. Water. Manage. 146, 324–334 (2014)

    Article  Google Scholar 

  31. Shen, Z.Y., Chen, L., Chen, T.: Analysis of parameter uncertainty in hydrological and sediment modeling using GLUE method: a case study of SWAT model applied to three gorges reservoir region, China. Hydrol. Earth Syst. Sci. 16(1), 121–132 (2012)

    Article  Google Scholar 

  32. Simunek, J., Jirka, Van Genuchten, M., Šejna, M.: Development and applications of the HYDRUS and STANMOD software packages and related codes. Vadose Zone J. 7, 587–600 (2008). https://doi.org/10.2136/vzj2007.0077

    Article  Google Scholar 

  33. Spear, R.C., Hornberger, G.M.: Eutrophication in Peel Inlet—II. Identification of critical uncertainties via generalized sensitivity analysis. Water. Res. 14(1), 43–49 (1980)

    Article  Google Scholar 

  34. Stauffer, P.H., Lu, Z.: Quantifying transport uncertainty in unsaturated rock using Monte Carlo sampling of retention curves. Vadose Zone J. 11(4), (2012)

  35. Sun, M., Zhang, X., Huo, Z., Feng, S., Huang, G., Mao, X.: Uncertainty and sensitivity assessments of an agricultural–hydrological model (RZWQM2) using the GLUE method. J. Hydrol. 534, 19–30 (2016)

    Article  Google Scholar 

  36. vanGenuchten, M.T.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils 1. Soil Sci. Soc. Am. 44(5), 892–898 (1980)

    Article  Google Scholar 

  37. Xiong, L., Wan, M., Wei, X., O'connor, K. M.: Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation/Indices pour évaluer les bornes de prévision de modèles hydrologiques et mise en œuvre pour une estimation d'incertitude par vraisemblance généralisée. H.S. J. 54(5), 852–871(2009)

  38. Yan, Y., Liu, J., Zhang, J., Li, X., Zhao, Y.: Quantifying soil hydraulic properties and their uncertainties by modified GLUE method. Int. Agrophys. 31(3), 433–445 (2017)

    Article  Google Scholar 

  39. Ye, M., Khaleel, R., Schaap, M.G., Zhu, J.: Simulation of field injection experiments in heterogeneous unsaturated media using cokriging and artificial neural network. Water. Resour. Res. 43(7), (2007)

  40. Zhu, J., Du, H.: Parameter uncertainty estimation by using the concept of ideal data in GLUE approach. J. Water. Resource. Prot. 9(01) (2017)

Download references

Author information

Authors and Affiliations

  1. Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

    Samaneh Etminan & Vahidreza Jalali

  2. Department of Rangeland and watershed management, Higher Education Complex of Shirvan, Birjand, Iran

    Vahidreza Jalali

  3. Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

    Majid Mahmoodabadi

  4. Associate Professor, Department of Water Engineering, Faculty of Agriculture, University of Birjand, Southern Khorasan, Iran

    Abbas Khashei siuki & Mohsen Pourreza Bilondi

Authors
  1. Samaneh Etminan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vahidreza Jalali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Majid Mahmoodabadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abbas Khashei siuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohsen Pourreza Bilondi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vahidreza Jalali.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Highlights

• The GLUE method is an appropriate method to evaluate parameters uncertainty of soil water retention curves.

• S and T indices have more performance than confidence interval criteria to evaluate the models uncertainty.

• the GSA method was able to determine the sensitivity of the model parameters in each studied soil texture class.

• “n” parameter is the most sensitive parameter in three models.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Etminan, S., Jalali, V., Mahmoodabadi, M. et al. Assessing an efficient hybrid of Monte Carlo technique (GSA-GLUE) in Uncertainty and Sensitivity Analysis of vanGenuchten Soil Moisture Characteristics Curve. Comput Geosci 25, 503–514 (2021). https://doi.org/10.1007/s10596-020-10019-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10596-020-10019-w

Keywords

Search

Navigation