Environmental Earth Sciences

, 77:621 | Cite as

A surrogate-based simulation–optimization approach application to parameters’ identification for the HydroGeoSphere model

  • Yongkai An
  • Wenxi LuEmail author
  • Xueman Yan
Original Article


A systematic approach is needed to use water more productively, because water shortages limit socio-economic development in many parts of the world. The aim of this paper is to establish a surrogate-based simulation–optimization approach to identify parameter values for a fully integrated surface water and groundwater flow coupling simulation. A surface water and groundwater flow coupling simulation model was implemented using HydroGeoSphere (HGS) model and the parameter sensitivities in the model were analyzed using local sensitivity analysis method. The parameters that exerted a large influence on the output results of the HGS model were then selected as stochastic variables, and the stochastic variable data sets were generated using the latin hypercube sampling (LHS) method, which, thereby, were used as inputs in HGS model to obtain the corresponding outputs. On the basis of input and output data sets, a kriging surrogate model of the HGS model was then established and verified, and parameter values of HGS model were identified using a surrogate-based simulation–optimization approach. The results of this study show that parameters that exert a large influence on the simulation output results include hydraulic conductivity, porosity, the van genuchten parameter (\(\alpha\)), and channel manning coefficient. The established kriging surrogate model is an ideal alternative to the HGS model for simulating and predicting, while optimal parameter values can be identified effectively and accurately using the established approach. The results of this research reveal that huge computational loads can be mitigated while using the kriging surrogate as an alternative for a simulation model in the solution process of optimization model.


HGS Kriging Surrogate model Simulation–optimization Parameters’ identification 



This research was funded by the China Geological Survey (no. DD20160266), China National Natural Science Foundation (41372237), and Project 2017149 supported by Graduate Innovation Fund of Jilin University.


  1. An Y, Lu W, Cheng W (2015) Surrogate model application to the identification of optimal groundwater exploitation scheme based on regression kriging method—a case study of Western Jilin Province. Int J Environ Res Public Health 12(8):8897–8918CrossRefGoogle Scholar
  2. Brunner P, Simmons CT (2012) HydroGeoSphere: a fully integrated, physically based hydrological model. Groundwater 50(2):170–176CrossRefGoogle Scholar
  3. Castillo E, Hadi AS, Conejo A, Fernández-Canteli A (2004) A general method for local sensitivity analysis with application to regression models and other optimization problems. Technometrics 46(4):430–444CrossRefGoogle Scholar
  4. Cornelissen T, Diekkrüger B, Bogena H (2013) Using HydroGeoSphere in a forested catchment: how does spatial resolution influence the simulation of spatio-temporal soil moisture variability? Procedia Environ Sci 19:198–207CrossRefGoogle Scholar
  5. Gottardi G, Venutelli M (1993) A control-volume finite-element model for two-dimensional overland flow. Adv Water Resour 16(5):277–284CrossRefGoogle Scholar
  6. Graham DN, Refsgaard A (2001) MIKE SHE: a distributed, physically based modeling system for surface water/groundwater interactions. In: MODFLOW 2001 and other modeling odysseys—Conference proceedings, pp 321–327Google Scholar
  7. Gustafson P, Srinivasan C, Wasserman L (1996) Local sensitivity analysis. Bayesian Stat 5:197–210Google Scholar
  8. Huang Y, Zhou Z, Yu Z (2009) The application of hydrogeosphere in simulating flow and solute transport of dam site in Jinping Hydropower Station. Chin J Hydrodyn 24(2):242–249Google Scholar
  9. Kersaudy P, Sudret B, Varsier N, Picon O, Wiart J (2015) A new surrogate modeling technique combining Kriging and polynomial chaos expansions—application to uncertainty analysis in computational dosimetry. J Comput Phys 286:103–117CrossRefGoogle Scholar
  10. Kollet SJ, Maxwell RM (2006) Integrated surface–groundwater flow modeling: a free-surface overland flow boundary condition in a parallel groundwater flow model. Adv Water Resour 29(7):945–958CrossRefGoogle Scholar
  11. Kwon H, Yi S, Choi S (2014) Numerical investigation for erratic behavior of Kriging surrogate model. J Mech Sci Technol 28(9):3697–3707CrossRefGoogle Scholar
  12. Langevin C, Swain E, Wolfert M (2005) Simulation of integrated surface-water/ground-water flow and salinity for a coastal wetland and adjacent estuary. J Hydrol 314(1–4):212–234CrossRefGoogle Scholar
  13. Lu WX, Liu P, Xu W, Xin X (2011) Numerical simulation of groundwater and sensitivity analysis of parameters based on hydrogeosphere technology. Water Resour Power 6:022Google Scholar
  14. Panday S, Huyakorn PS (2004) A fully coupled physically-based spatially-distributed model for evaluating surface/subsurface flow. Adv Water Resour 27(4):361–382CrossRefGoogle Scholar
  15. Panday S, Huyakorn PS, Therrien R, Nichols RL (1993) Improved three-dimensional finite-element techniques for field simulation of variably saturated flow and transport. J Contam Hydrol 12(1–2):3–33CrossRefGoogle Scholar
  16. Rakovec O, Hill MC, Clark MP, Weerts AH, Teuling AJ, Uijlenhoet R (2014) Distributed evaluation of local sensitivity analysis (DELSA), with application to hydrologic models. Water Resour Res 50(1):409–426CrossRefGoogle Scholar
  17. 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
  18. Sudicky E, Park Y, Unger A, Jones J, Brookfield A et al (2006) Simulating complex flow and contaminant transport dynamics in an integrated surface-subsurface modelling framework. In: GSA annual meeting and exposition, Philadephia, vol 38. Geological Society of America Abstracts with Programs, Boulder, CO, p 258Google Scholar
  19. Tapoglou E, Karatzas GP, Trichakis IC, Varouchakis EA (2014) A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation. J Hydrol 519:3193–3203CrossRefGoogle Scholar
  20. Therrien R, McLaren RG, Sudicky EA, Panday SM (2010) HydroGeoSphere: a three-dimensional numerical model describing fully-integrated subsurface and surface flow and solute transport. Groundwater Simulations Group, University of Waterloo, WaterlooGoogle Scholar
  21. Therrien R, Sudicky EA, Park YJ, McLaren RG (2012) HydroGeoSphere: a three-dimensional numerical modelling describing fully-integrated subsurface and surface flow and transport. Aquanty Inc, WaterlooGoogle Scholar
  22. VanderKwaak JE (1999) Numerical simulation of flow and chemical transport in integrated surface-subsurface hydrologic systems. Dissertation, University of WaterlooGoogle Scholar
  23. VanderKwaak JE, Loague K (2001) Hydrologic-response simulations for the R-5 catchment with a comprehensive physics-based model. Water Resour Res 37(4):999–1013CrossRefGoogle Scholar
  24. Zhao Y, Lu W, Xiao C (2016) A Kriging surrogate model coupled in simulation–optimization approach for identifying release history of groundwater sources. J Contam Hydrol 185:51–60CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Groundwater Resources and Environment, Ministry of EducationJilin UniversityChangchunPeople’s Republic of China

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