Modeling and uncertainty analysis of seawater intrusion in coastal aquifers using a surrogate model: a case study in Longkou, China

  • Tiansheng Miao
  • Wenxi LuEmail author
  • Jin Lin
  • Jiayuan Guo
  • Tianliang Liu
Original Paper


Seawater intrusion is a complex problem involving groundwater with variable density. It has an important impact on the development and utilization of groundwater resources in coastal areas. In previous studies, numerical simulations of seawater intrusion were performed by assigning fixed values to model parameters, thereby not taking into account the influence of stochastic variability of hydrogeological parameters on model predictions. In this study, we developed a three-dimensional mathematical model of seawater intrusion by modeling vertical solute transport due to density changes resulting from seawater intrusion. We then applied a Monte Carlo–based method to analyze uncertainty in model simulations of seawater intrusion. Because the Monte Carlo method requires the simulation model to be repeatedly run, we used the Kriging method to build a surrogate model that significantly reduced the computational load compared to the simulation model while ensuring the desired accuracy. Then we used a local sensitivity analysis method to select the two parameters with the greatest influence on model output. We treated the two selected parameters as random variables, and results show that (1) the three-dimensional, variable-density seawater intrusion model can effectively simulate and predict the distribution of seawater intrusion in the study area; (2) the local sensitivity analysis can accurately identify the hydrogeological parameters that most influence model output; (3) the uncertainty analysis based on the surrogate model reduces computing time substantially and provides a realistic assessment of the effect of hydrogeological parameter variability on seawater intrusion numerical simulation results.


Seawater intrusion Three-dimensional Variable-density groundwater simulation model Surrogate model Uncertainty analysis 



The authors thank the editor and anonymous reviewers for their insightful comments and suggestions.

Funding information

This work was supported by the Development Program of China (NO. 2016YFC0402800).


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Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  • Tiansheng Miao
    • 1
    • 2
    • 3
  • Wenxi Lu
    • 1
    • 2
    • 3
    Email author
  • Jin Lin
    • 4
  • Jiayuan Guo
    • 1
    • 2
    • 3
  • Tianliang Liu
    • 1
  1. 1.College of Environment and ResourcesJilin UniversityChangchunChina
  2. 2.Key Laboratory of Groundwater Resources and Environment, Ministry of EducationJilin UniversityChangchunChina
  3. 3.Jilin Provincial Key Laboratory of Water Resources and EnvironmentJilin UniversityChangchunChina
  4. 4.Nanjing Hydraulic Research InstituteNanjingChina

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