Attention-Based Insights into Surface–Groundwater Coupling: Transformer Forecasting of 10-Day Groundwater Levels in the Zhuoshui River Basin

Under a changing climate, shifts in the spatiotemporal patterns of rainfall can markedly influence catchment-scale hydrological processes and alter interactions between surface water and groundwater systems. Groundwater level is a key indicator of basin hydrological status, jointly controlled by rainfall infiltration, river recharge, pumping, and aquifer properties. Yet, at longer horizons, the nonlinear coupled relationships among rainfall, river discharge, and groundwater levels remain challenging to model and forecast, especially at the regional (multi-site) scale. The Zhuoshui River Basin, a critical water-supply and agricultural region in Taiwan, provides a representative setting to investigate these surface water–groundwater interactions.
Here we develop a long-horizon groundwater-level forecasting model at the 10-day (dekadal) scale based on the Transformer architecture for approximately 22 monitoring wells across the basin. The model is trained and optimised using historical hydrometeorological time series, with inputs including rainfall, river discharge, groundwater levels, and other key hydrological drivers. By leveraging the Transformer's attention mechanism, the proposed approach captures long-range dependencies in multivariate sequences and enables attribution analyses of dominant drivers influencing groundwater responses across lead times.
The model achieves strong predictive skill over the multi-site system (test RMSE = 0.23 m; R² = 0.95), demonstrating its capability to reproduce basin-wide groundwater dynamics at the dekadal scale. Attention weight analyses reveal how rainfall and river-flow signals propagate into groundwater variability across different time lags and spatial locations, deepening understanding of surface water–groundwater coupling mechanisms in the basin.
The developed forecasting framework provides actionable information for integrated water resources management under changing climatic and anthropogenic pressures, including early warnings for groundwater depletion risks, optimized conjunctive use strategies, and informed agricultural irrigation planning. By explicitly modeling multivariate hydrological interactions through attention mechanisms, this approach advances both scientific understanding and operational capabilities for regional groundwater management. The methodology is transferable to other groundwater-dependent regions facing similar forecasting and management challenges.

Keywords: climate change; surface water–groundwater interactions; groundwater-level forecasting; Transformer