CMIP6-Driven Groundwater-Level Projections and Climate Risk Mapping for South Korea Using a Hybrid Deep Learning Framework

Groundwater variability is recognized as a critical constraint on long-term water resource sustainability in South Korea under nonstationary climate forcing. This study developed a groundwater-level (GWL) projection framework based on CMIP6 simulations, validated against national monitoring data, screened for physical realism of underlying climate projections, and translated into station-scale climate risk metrics. GWL observations from 199 stations of the National Groundwater Monitoring Network (2009–2025) were related to CMIP6 precipitation and soil-moisture forcings using a hybrid deep learning architecture, the hybrid attention-based convolutional neural network–long short-term memory (HACL) model, combining multiscale temporal convolution, bidirectional memory, and self-attention. Of ten candidate GCMs, four (ACCESS, GISS, INM, and NorESM) met pre-defined performance criteria (R2 > 0.70, NSE ≥ 0.70, KGE > 0.50), forming a filtered ensemble more internally consistent than the unfiltered multi-model mean. This ensemble projected national-average GWL increases of 16.60 m (SSP245), 15.92 m (SSP370), and 17.85 m (SSP585), the largest in the western alluvial lowlands—substantially exceeding historical observed rates and indicating sensitivity signals warranting further investigation rather than direct station-level forecasts. The results were incorporated into the Climate Groundwater Risk Index (CGRI) integrating the magnitude of change, ensemble spread, observed variability, and vulnerability, offering a replicable approach for monsoon-affected regions. Independent out-of-sample evaluation (2022–2025) confirmed robust generalization (R2 = 0.965, NSE = 0.965, KGE = 0.974, RMSE = 14.26 m). Ablation benchmarking showed HACL substantially outperformed standalone BiLSTM (NSE = 0.861), 1D-CNN (NSE = 0.810), and linear regression (NSE = 0.628). Predictor sensitivity analysis revealed that year as a continuous covariate accounts for ~85% of the projected 16–18 m signal; constrained strictly to physical forcing, projected increases are +2.42 m (SSP245), +2.15 m (SSP370), and +3.78 m (SSP585) by 2081–2100, aligning with historical trends (~2–3 m) and restoring station-level hydrogeological sensitivity.