Sustainable groundwater resource management necessitates dependable and precise predictions of groundwater head fields under fluctuating climatic conditions. The substitution of original simulation models with efficient surrogates presents a challenge in simultaneously accounting for correlations among multiple time series outputs and maintaining overall prediction accuracy. This study develops a novel surrogate modelling approach, DSCNN-GRU, incorporating a deep separable convolutional neural network (DSCNN) and a gated recurrent unit (GRU), to efficiently capture temporal and spatial variations in groundwater head fields from transient groundwater flow models using input hydraulic conductivity field data. The applicability and performance of the proposed method are evaluated for predicting groundwater head fields in a practical research area under three scenarios with different hydraulic conductivity fields. The performance of the DSCNN-GRU model is compared to the traditional convolutional neural network (CNN), CNN-LSTM, and DSCNN-LSTM models to further test its applicability. The numerical study demonstrates that optimizing hyperparameters can result in reasonably accurate performance of the proposed model, and the “simplest” DSCNN-GRU outperforms CNN, CNN-LSTM, and DSCNN-LSTM in both prediction accuracy and time-to-solution.

Accurate joint estimation of heterogeneous hydraulic conductivity fields and time-varying contaminant source parameters in groundwater systems constitutes a challenging high-dimensional inverse problem, particularly under sparse observational conditions and high computational demands. To alleviate this limitation, this study proposes an autoregressive depthwise convolutional neural network (AR-DWCNN) as a lightweight surrogate model for coupled groundwater flow and contaminant transport simulations. The proposed model employs depthwise separable convolutions and dense connectivity within an encoder–decoder framework to capture nonlinear flow and spatiotemporal transport dynamics while reducing model complexity and computational demand relative to conventional convolutional architectures. The AR-DWCNN is further integrated with an enhanced Iterative Local Updating Ensemble Smoother incorporating Levenberg–Marquardt regularization, enabling efficient joint inversion of high-dimensional hydraulic conductivity fields and multi-period contaminant source strengths. Numerical experiments conducted on a synthetic two-dimensional heterogeneous aquifer demonstrate that the surrogate-assisted inversion framework achieves posterior estimates that closely match those obtained using the numerical forward model, while significantly improving computational efficiency. These results indicate that the AR-DWCNN-based inversion method provides an effective and scalable solution for high-dimensional groundwater contaminant transport inverse problems, offering practical potential for uncertainty quantification and remediation design in complex subsurface systems.