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Modeling Earth Systems and Environment

, Volume 5, Issue 1, pp 119–131 | Cite as

Application of a link simulation optimization model utilizing quantification of hydrogeologic uncertainty to characterize unknown groundwater contaminant sources

  • Mahsa AmirabdollahianEmail author
  • Bithin Datta
  • Peter H. Beck
Original Article
  • 71 Downloads

Abstract

In existing groundwater contamination source characterization methodologies, simulation models estimate the contamination concentration in the study area. In order to obtain reliable solutions, it is essential to provide the simulation models with reliable hydrogeological properties. In real-life scenarios often high level of uncertainty and variability is associated with the hydrogeological properties. This study focuses on quantifying the hydrogeological parameter uncertainty to enhance the accuracy of identifying contamination release histories. Tracer experiment results at the Eastlakes Experimental Site, located in Botany Sands Aquifer, in New South Wales, Australia, are utilized to examine the performance and potential applicability of the methodology. In the selected study area, the hydrogeological heterogeneity in the microscopic scale, specifically the hydraulic conductivity, has substantial effect on the transport of pollutants. Among available tracer information, Bromide is studied as a conservative contaminant. Using possible realizations of the flow field, a coefficient of confidence (COC) is calculated for each field monitoring locations and times. Higher COC implies that the result of simulation models at that specific monitoring location and time is more reliable than other contaminant concentration data. Therefore, the optimization model should emphasise matching the corresponding estimated and observed contamination concentrations to accurately identify the contaminant release locations and histories. The linked simulation–optimisation method is utilised to optimally characterise the Bromide sources. Performance evaluation results demonstrate that the proposed methodology recovers pollution source characteristics more accurately compared to the methodology which does not consider the effect of hydrogeological parameter uncertainty.

Keywords

Hydrogeology Parameter uncertainty Source characterization Groundwater Tracer test Optimization 

Notes

Funding

B. Datta thanks CRC for Contamination Assessment and Remediation of Environment (CRC-CARE), University of New Castle, NSW, Australia for providing financial support for this research through Project: no. 5.6.0.3.09/10(2.6.03), CRC-CARE-Bithin Datta (JCU) which also funded the Ph.D. scholarship of the first author. M. Amirabdollahian also acknowledges the financial support by CRC-CARE, and James Cook University, Australia.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest. It is to be noted that NSW Department of Industry is not associated or endorsing any component of this research material or its results.

References

  1. Amirabdollahian M, Datta B (2013) Identification of contaminant source characteristics and monitoring network design in groundwater aquifers: an overview. J Environ Prot 4(5A):26–41CrossRefGoogle Scholar
  2. Amirabdollahian M, Datta B (2014) Identification of pollutant source characteristics under uncertainty in contaminated water resources systems using adaptive simulated annealing and fuzzy logic. Int J Geomate 6(1):757–762Google Scholar
  3. Aral MM, Guan J, Maslia ML (2001) Identification of contaminant source location and release history in aquifers. J Hydrol Eng 6(3):225–234CrossRefGoogle Scholar
  4. Beck PH (2000) Transport of conservative and reactive inorganic elements in the saturated part of a hetrogeneous sand aquifer, Botany Basin. University of New South Wales, SydneyGoogle Scholar
  5. Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320(1–2):18–36.  https://doi.org/10.1016/j.jhydrol.2005.07.007 CrossRefGoogle Scholar
  6. Clemo T (2003) Improved water table dynamics in block-centered finite-difference flow models. In: MODFLOW and more 2003: understanding through modeling, Golden, Colorado, USA 11–14 September 2003Google Scholar
  7. Datta B, Chakrabarty D, Dhar A (2009) Simultaneous identification of unknown groundwater pollution sources and estimation of aquifer parameters. J Hydrol 376(1–2):48–57CrossRefGoogle Scholar
  8. Dokou Z, Pinder GF (2009) Optimal search strategy for the definition of a DNAPL source. J Hydrol 376(3):542–556.  https://doi.org/10.1016/j.jhydrol.2009.07.062 CrossRefGoogle Scholar
  9. Dokou Z, Pinder GF (2011) Extension and field application of an integrated DNAPL source identification algorithm that utilizes stochastic modeling and a Kalman filter. J Hydrol 398(3):277–291.  https://doi.org/10.1016/j.jhydrol.2010.12.029 CrossRefGoogle Scholar
  10. Esfahani HK, Datta B (2016) Linked optimal reactive contaminant source characterization in contaminated mine sites: case study. Water Res Plan ASCE 142(12):04016061CrossRefGoogle Scholar
  11. Evans DJ (1993) A physical and hydrochemical characterisation of a sand aquifer in Sydney. University of New South Wales, Sydney (unpubl.) Google Scholar
  12. Freeze RA, James B, Massmann J, Sperling T, Smith L (1992) Hydrogeological decision analysis: 4. The concept of data worth and its use in the development of site investigation strategies. Ground Water 30(4):574–588.  https://doi.org/10.1111/j.1745-6584.1992.tb01534.x CrossRefGoogle Scholar
  13. Fu T, Chen H, Zhang W, Nie Y, Wang K (2015) Vertical distribution of soil saturated hydraulic conductivity and its influencing factors in a small karst catchment in Southwest China. Environ Monit Assess 187(3):1–13.  https://doi.org/10.1007/s10661-015-4320-1 CrossRefGoogle Scholar
  14. Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New YorkGoogle Scholar
  15. Guillaume JHA, Hunt RJ, Comunian A, Fu B, Blakers R (2016) Methods for exploring uncertainty in groundwater management predictions. In: Jakeman AJ, Barreteau O, Hunt RJ, Rinaudo JD, Ross A (eds) Integrated groundwater management. Springer, Cham.  https://doi.org/10.1007/978-3-319-23576-9_28 Google Scholar
  16. Hazrati-Yadkoori S, Datta B (2017) Adaptive surrogate model based optimization (ASMBO) for unkown groundwater contamination source characterizations using self-organizing maps. Water Res Prot 9(02):193–214CrossRefGoogle Scholar
  17. Ingber L (1996) Adaptive simulated annealing (ASA): lessons learned. Control Cybern 25(1):33–54Google Scholar
  18. Jankowski J, Beck P (2000) Aquifer heterogeneity: hydrogeological and hydrochemical properties of the Botany Sands Aquifer and their impact on contaminant transport. Aust J Earth Sci 47(1):45–64 (copyright © Geological Society of Australia, reprinted by permission of Taylor & Francis Ltd, http://www.tandfonline.com on behalf of Geological Society of Australia) CrossRefGoogle Scholar
  19. Kisi O, Parmar KS (2016) Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. J Hydrol 534:104–112CrossRefGoogle Scholar
  20. Kisi O, Parmar KS, Soni K, Demir V (2017) Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline and M5 model tree models. Air Qual Atmos Health 10:873–883CrossRefGoogle Scholar
  21. Mahar PS, Datta B (2001) Optimal identification of ground-water pollution sources and parameter estimation. Water Res Plan ASCE 127(1):20–29CrossRefGoogle Scholar
  22. Moo-Young H, Johnson B, Johnson A, Carson D, Lew C, Liu S et al (2004) Characterization of infiltration rates from landfills: supporting groundwater modeling efforts. Environ Monit Assess 96(1–3):283–311.  https://doi.org/10.1023/B:EMAS.0000031734.67778.d7 CrossRefGoogle Scholar
  23. Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37(1/2):17CrossRefGoogle Scholar
  24. Mugunthan P, Shoemaker CA (2004) Time varying optimization for monitoring multiple contaminants under uncertain hydrogeology. Bioremediat J 8(3–4):129–146.  https://doi.org/10.1080/10889860490887509 CrossRefGoogle Scholar
  25. O’Hagan A, Oakley JE (2004) Probability is perfect, but we can’t elicit it perfectly. J Reliab Eng Syst Saf 85(1–3):239–248.  https://doi.org/10.1016/j.ress.2004.03.014 CrossRefGoogle Scholar
  26. Oberkampf WL, Helton JC, Joslyn CA, Wojtkiewicz SF, Ferson S (2004) Challenge problems: uncertainty in system response given uncertain parameters. J Reliab Eng Syst Saf 85:11–19CrossRefGoogle Scholar
  27. Prakash O, Datta B (2015) Simulation–optimization of pollutant sources in contaminated aquifers by integration sequential-monitoring-network design and source identification: methodology and an application in Australia. Hydrogeol J 23(6):1089–1107CrossRefGoogle Scholar
  28. Ross TJ (2005) Fuzzy logic with engineering applications (vol. book, whole). Wiley, ChichesterGoogle Scholar
  29. Schlumberger Water Services (2011) Visual MODFLOW help. http://www.swstechnology.com/help/vmod/index.html?vm_ch5_run5.htm. Accessed 6 Feb 2018
  30. Singh RM, Datta B (2006) Identification of groundwater pollution sources using GA-based linked simulation optimization model. J Hydrol Eng 11(2):101–109CrossRefGoogle Scholar
  31. Sun NZ (1994) Inverse problems in groundwater modeling. Kluwer Academic, BostonGoogle Scholar
  32. Tiedeman C. Gorelick SM (1993) Analysis of uncertainty in optimal groundwater contaminant capture design. Water Resour Res 29(7):2139–2153.  https://doi.org/10.1029/93wr00546 CrossRefGoogle Scholar
  33. Wu JC, Zeng XK (2013) Review of the uncertainty analysis of groundwater numerical simulation. Chin Sci Bull 58(25):3044–3052.  https://doi.org/10.1007/s11434-013-5950-8 CrossRefGoogle Scholar
  34. Yeh HD, Chang TH, Lin YC (2007) Groundwater contaminant source identification by a hybrid heuristic approach. Water Resour Res 43(9):1–16.  https://doi.org/10.1029/2005wr004731 CrossRefGoogle Scholar
  35. Yu XW (1994) Study of physical and chemical properties of groundwater and surface water in the northern part of the Botany Basin, Sydney. University of New South wales, Sydney (unpubl) Google Scholar
  36. Zheng C, Wang PP (1999) A modular three-dimensional multispecies transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems. Documentation and User’s. CiteseerGoogle Scholar
  37. Zheng C, Hill MC, Hsieh PA (2001) MODFLOW-2000, the U.S. geological survey modular ground-water model-user guide to the LMT6 package, the linkage with MT3DMS for multi-species mass transport modeling. In: U. S. G. SURVEY (ed) Open file report 01-82. Denver, ColoradoGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Science and EngineeringJames Cook UniversityTownsvilleAustralia
  2. 2.NSW Department of IndustryParramattaAustralia
  3. 3.CRC for Contamination Assessment and Remediation of Environment (CRC-CARE)University of New CastleCallaghanAustralia
  4. 4.GHD Pty. Ltd.MelbourneAustralia

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