Skip to main content
SpringerLink
Log in

On the assessment of ground water parameter uncertainty over an arid aquifer

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Uncertainty assessment of groundwater modeling is important for sustainable groundwater management and planning. The purpose of this study is to assess parameter uncertainty of groundwater modeling in the Birjand plain, Iran. This arid aquifer was modeled using MATLAB-based MODFLOW to avoid propagating uncertainty associated with hydraulic conductivity and recharge parameters. So, the aquifer was divided into 17 hydraulic conductivity homogenous zones; besides, 9 recharge zones were considered separately. Parameter uncertainty was evaluated using the Monte Carlo (MC) sampling technique, namely, the generalized likelihood uncertainty estimation (GLUE). The results indicated that the performance of the GLUE based on the inverse error variance likelihood function was satisfied, because it gave the higher bracketing of observations equal to 86 %. Parameter uncertainty is well defined in the zones where they are not influenced directly by an inflow or outflow stream while hydraulic conductivity parameters of these zones follow approximately a normal distribution. In addition, groundwater modeling leads to a uniform exponential distribution in the zones with inflow or outflow streams.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. Maximum Likelihood Bayesian Model Averaging

  2. Kashyap Information Criteria

  3. Bayesian Information Criteria

  4. Akaike Information Criterion

References

  • Beven K (2001) Rainfall-runoff modelling: the primer. Wiley-Blackwell, England

    Google Scholar 

  • Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320:18–36. doi:10.1016/j.jhydrol.2005.07.007

    Article  Google Scholar 

  • Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298. doi:10.1002/hyp.3360060305

    Article  Google Scholar 

  • Beven K, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in mechanistic modeling of complex environmental systems using the GLUE methodology. J Hydrol 249:11–29. doi:10.1016/S0022-1694(01)00421-8

    Article  Google Scholar 

  • Beven K, Smith PJ, Freer J (2008) So just why would a modeller choose to be incoherent? J Hydrol 354:15–32. doi:10.1016/j.jhydrol.2008.02.007

    Article  Google Scholar 

  • Buckley KM, Binley A, Beven K (1995) Calibration and predictive uncertainty estimation of groundwater quality models: application to the Twin Lake Tracer Test. In: Groundwater Quality Management: Proceedings of the GQM 3 Conference Held in Tallinn, Estonia, September 1993. IAHS Publ 220: 205–214

  • Feyen L, Beven KJ, De Smedt F, Freer J (2001) Stochastic capture zone delineation within the generalized likelihood uncertainty estimation methodology: conditioning on head observations. Water Resour Res 37(3):625–638. doi:10.1029/2000WR900351

    Article  Google Scholar 

  • Freer J, Beven K, Ambroise B (1996) Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resour Res 32(7):2161–2173. doi:10.1029/95WR03723

    Article  Google Scholar 

  • Ghoochanian E, Etebari B, Akbarpour A (2013) Integrating groundwater management with WEAP and MODFLOW models (Case study: Birjand Plain, east of Iran). MODFLOW and More 2013: Translating Science into Practice – Conference, June 2–5, 2013, Colorado

  • Hassan AE, Bekhit HM, Chapman JB (2008) Uncertainty assessment of a stochastic groundwater flow model using GLUE analysis. J Hydrol 362:89–109. doi:10.1016/j.jhydrol.2008.08.017

    Article  Google Scholar 

  • He J, Jones JWJ, Graham WWD, Dukes MMD (2010) Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agric Syst 103:256–264. doi:10.1016/j.agsy.2010.01.006

    Article  Google Scholar 

  • Humphrey S (2008) Astochastic approach to a groundwater flow model of southern honey lake valley in Lassen county, CA and Washoe county, NV. Thesis, University of Nevada

  • Ijiri Y, Saegusa H, Sawada A, Ono M, Watanabe K, Karasaki K, Doughty C, Shimo M, Fumimura K (2009) Evaluation of uncertainties originating from the different modeling approaches applied to analyze regional groundwater flow in the Tono area of Japan. J Contam Hydrol 103(3–4):168–181. doi:10.1016/j.jconhyd.2008.10.010

    Article  Google Scholar 

  • Izady A, Davary K, Alizadeh A, Ziaei AN, Alipoor A, Joodavi A, Brusseau ML (2014) A framework toward developing a groundwater conceptual model. Arab J Geosci 7:3611–3631. doi:10.1007/s12517-013-0971-9

    Article  Google Scholar 

  • Izady A, Davary K, Alizadeh A, Ziaei AN, Akhavan S, Alipoor A, Joodavi A, Brusseau ML (2015) Groundwater conceptualization and modeling distributed SWAT-based recharge for semi-arid agricultural Neishaboor plain, Iran. Hydrogeol J 23(1):47–68. doi:10.1007/s10040-014-1219-9

    Article  Google Scholar 

  • JiChun WU, XianKui Z (2013) Review of the uncertainty analysis of groundwater numerical simulation. Chin Sci Bull 25:3044–3052. doi:10.1007/s11434-013-5950-8

    Google Scholar 

  • Jin X, XU CY, Zhang Q, Sing VP (2010) Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. J Hydrol 383:147–155. doi:10.1016/j.jhydrol.2009.12.028

    Article  Google Scholar 

  • Keesman K, van Straten G (1989) Identification and prediction propagation of uncertainty in models with bounded noise. Int J Control 49(6):2259–2269. doi:10.1080/00207178908559771

    Article  Google Scholar 

  • Li X, Tsai F (2009) Bayesian model averaging for groundwater head prediction and uncertainty analysis using multimodel and multimethod. Water Resour Res 45:W09403. doi:10.1029/2008WR007488

    Google Scholar 

  • Li L, Xia J, Xu C-Y, Singh VP (2010) Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models. J Hydrol 390:210–221. doi:10.1016/j.jhydrol.2010.06.044

    Article  Google Scholar 

  • Makowski D, Wallach D, Tremblay M (2002) Using a Bayesian approach to parameter estimation: comparison of the GLUE and MCMC methods. Agronomie 22:191–203. doi:10.1051/agro:2002007

    Article  Google Scholar 

  • Mansouri B, Salehi J, Etebari B, Kardanmoghadam H (2012) Metal concentrations in the groundwater in Birjand Flood Plain, Iran. Bull Environ Contam Toxicol 89:138–142. doi:10.1007/s00128-012-0630-y

    Article  Google Scholar 

  • McDonald MG, Harbaugh AW (1988) A modular three-dimensional finite-difference ground-water flow model. U.S. Geological Survey, USA

  • McKay MD, Beckman RJ, Conover WJ (1979) Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2):239–245. doi:10.1080/00401706.1979.10489755

    Google Scholar 

  • Mirarabi A, Nakhaei M (2009) Groundwater level Fluctuation forcasting in birjand aquifer using artificial neural network. EGU General Assembly 2009, held 19–24 April, 2009 in Vienna, Austria

  • Moore C, Wöhling T, Doherty J (2010) Efficient regularization and uncertainty analysis using a global optimization methodology. Water Resour Res 46:W08527. doi:10.1029/2009WR008627

    Google Scholar 

  • Morse BS, Pohll G, Huntington J, Rodrigues-Castillo R (2003) Stochastic capture zone analysis of arsenic-contaminated well using the generalized likelihood uncertainty estimator (GLUE) methodology. Water Resour Res 39(6):1151. doi:10.1029/2002WR001470

    Google Scholar 

  • Mousavi SJ, Abbaspour KC, Kamali B, Amini M, Yang H (2012) Uncertainty-based automatic calibration of HEC-HMS model using sequential uncertainty fitting approach. J Hydroinform 14(2):286–309. doi:10.2166/hydro.2011.071

    Article  Google Scholar 

  • Olsthoorn T N (2013) mfLab: Environmet for MODFLOW suite groundwater modeling.http://code.google.come/p/mfLab. Accessed 19 Dec 2013

  • Pourtaghi ZS, Pourghasemi HR (2014) GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeol J 22(3):643–662. doi:10.1007/s10040-013-1089-6

    Article  Google Scholar 

  • Rahnama B, Naseri M, Zahraie B (2014) Identifying optimized structure and uncertainty analysis of monthly water balance model. IWRJ 8(14):77–86 (in Farsi)

    Google Scholar 

  • Refsgaard JC, Christensen S, Sonnenborg TO, Seifert D, Hojberg AL, Trodborg L (2012) Review of strategies for handling geological uncertainty in groundwater flow and transport modeling. Adv Water Resour 36:36–50. doi:10.1016/j.advwatres.2011.04.006

    Article  Google Scholar 

  • Rojas R, Feyen L, Dassargues A (2008) Conceptual model uncertainty in groundwater modeling: combining generalized likelihood uncertainty estimation and Bayesian model averaging. Water Resour Res 44:1–16. doi:10.1029/2008WR006908

    Google Scholar 

  • Rojas R, Kahunde S, Peeters L, Batelaan O, Feyen L, Dassargues A (2010) Application of a multimodel approach to account for conceptual model and scenario uncertainties in groundwater modeling. J Hydrol 394:416–435. doi:10.1016/j.jhydrol.2010.09.016

    Article  Google Scholar 

  • Romanowicz R, Beven KJ, Tawn J (1994) Evaluation of predictive uncertainty in non-linear hydrological models using a Bayesian approach. In: Barnettand V, Turkman KF (eds) Statistics for the environment: water related issues. Wiley, NewYork, pp 297–317

    Google Scholar 

  • Singh A, Mishra S, Ruskauff G (2010) Model averaging techniques for quantifying conceptual model uncertainty. Ground Water 48:701–715. doi:10.1111/j.1745-6584.2009.00642.x

    Article  Google Scholar 

  • Troldborg L, Refsgaard J, Jensen K, Engesgaard P (2007) The importance of alternative conceptual models for simulation of concentrations in a multi–aquifer system. Hydrogeol J 15(5):843–860. doi:10.1007/s10040-007-0192-y

    Article  Google Scholar 

  • Tsai FTC, Li X (2008) Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window. Water Resour Res 44:W09434. doi:10.1029/2007WR006576

    Google Scholar 

  • Vazquez RF, Beven K, Feyen J (2009) GLUE based assessment on the overall predictions of a MIKE SHE application. Water Resour Manag 23:1325–1349. doi:10.1007/s11269-008-9329-6

    Article  Google Scholar 

  • Wang X, He X, Williams JR, Izaurralde RC, Atwood JD (2005) Sensitivity and uncertainty analyses of crop yields and soil organic carbon simulated with EPIC. Trans ASAE 48(3):1041–1054. doi:10.13031/2013.18515

    Article  Google Scholar 

  • Wu JC, Lu L, Tang T (2011) Bayesian analysis for uncertainty and risk in a groundwater numerical model’s predictions. Hum Ecol Risk Assess 7:1310–1331. doi:10.1080/10807039.2011.618419

    Article  Google Scholar 

  • Ye M, Pohlmann KF, Chapman JB, Pohll GM, Reeves DM (2010) A model-averaging method for assessing groundwater conceptual model uncertainty. Ground Water 48:716–728. doi:10.1111/j.1745-6584.2009.00633

    Article  Google Scholar 

  • Yoon H, Hart DB, McKenna SA (2013) Parameter estimation and predictive uncertainty in stochastic inverse modeling of groundwater flow: comparing null-space Monte Carlo and multiple starting point methods. Water Resour Res 49:536–553. doi:10.1002/wrcr.20064

    Article  Google Scholar 

  • Zia H (2004) Hydrogeology and effect artificial recharge of ground water Birjand Plain. MSc Thesis, Tabriz University, Iran

Download references

Author information

Authors and Affiliations

  1. Department of Water Engineering, University of Birjand, Birjand, Iran

    BaharehSadat Hamraz, Mohsen Pourreza Bilondi & Sadegh Sadeghi Tabas

  2. Department of Civil Engineering, University of Birjand, Birjand, Iran

    Abolfazl Akbarpour

Authors
  1. BaharehSadat Hamraz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abolfazl Akbarpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohsen Pourreza Bilondi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sadegh Sadeghi Tabas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohsen Pourreza Bilondi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hamraz, B., Akbarpour, A., Pourreza Bilondi, M. et al. On the assessment of ground water parameter uncertainty over an arid aquifer. Arab J Geosci 8, 10759–10773 (2015). https://doi.org/10.1007/s12517-015-1935-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12517-015-1935-z

Keywords

Search

Navigation