Data-driven forecasting and uncertainty quantification of head and salinity dynamics in coastal aquifers

Abstract

Coastal aquifers are increasingly vulnerable to seawater intrusion, climate change, and intensive groundwater extraction, while available monitoring data are often limited in both quality and quantity. These constraints restrict the application of physics-based models and complicate reliable risk assessment and management.This study develops and validates a data-driven framework for short-term forecasting of piezometric level and salinity dynamics in coastal aquifers, with explicit consideration of model uncertainty. Five regression and machine-learning methods were applied to datasets from two Mediterranean coastal aquifers: the Señorío aquifer in Marbella, southern Spain, where electrical conductivity and piezometric level were modeled, and the Sierra de Mijas aquifer in Torremolinos, eastern Spain, where piezometric level was modeled. Forecasts were produced for 3-, 6-, and 12-month horizons using limited historical records, and model performance was evaluated by metrics capturing accuracy, trend reproduction, and probabilistic characteristics. Results show that the ARDL model performs robustly for groundwater time series with small oscillations, whereas non-linear approaches, including recurrent neural networks, are more effective in capturing oscillatory behavior and short-term fluctuation. The Gaussian Process framework yields reliable prediction intervals and introduces a normalized measure of relative model uncertainty that supports model comparison and risk-informed interpretation of forecasts. Despite limited training data, all models achieved effective one-year forecasting performance. The inferred correlations between piezometric level, electrical conductivity, rainfall, recharge, and extraction are consistent with established physical understanding of coastal aquifer systems. Overall, the proposed framework provides a practical tool for supporting groundwater risk assessment and adaptive management in data-scarce coastal aquifers, with potential applications in early-warning systems and operational decision-making.

Bibliographic Information