Uncertainty analysis for precipitation and sea-level rise of a variable-density groundwater simulation model based on surrogate models

Han, Zheng; Lu, Wenxi; Lin, Jin

Uncertainty analysis for precipitation and sea-level rise of a variable-density groundwater simulation model based on surrogate models

2020

Han, Zheng | Lu, Wenxi | Lin, Jin

Effective coastal aquifer management typically relies on numerical models to analyze the seawater intrusion (SI) process. Before using groundwater simulation models to predict the extent of SI in the future, preparing input data is an extremely necessary and important step. For precipitation and sea-level rise (SLR), which are two of the most influential factors for SI, it is difficult to precisely forecast their variations. Current studies of using numerical models to predict future SI often overlook the uncertainty of these two factors. This can result in compromised predictions of SI. In this study, a three-dimensional variable-density groundwater simulation model was established for a coastal area in Longkou, China. Then, the Monte Carlo method was applied to perform uncertainty analysis for the input data of precipitation and SLR of the SI model. In order to reduce the huge computational load brought by repeated invocation of the SI model during the process of Monte Carlo simulation, a surrogate model based on a multi-gene genetic programming (MGGP) method was developed to replace the SI simulation model for calculation. A comparison between the MGGP surrogate model and the Kriging surrogate model was carried out, and the results show that the MGGP surrogate model has a distinct advantage over the Kriging surrogate model in approximating the excitation-response relationship of the variable-density groundwater simulation model. Through statistical analysis of Monte Carlo simulation results, an object and reasonable risk assessment of SI for the study area was obtained. This study suggests that it is essential to take the uncertainty of precipitation and SLR into account when modeling and predicting the extent of SI.

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