Abstract. Accurate seasonal forecasting of water table elevation is critical for effective water resource management in unconfined aquifers, particularly under climate variability and anthropogenic pressures. This study presents a novel methodology for predicting water table elevation on seasonal timescales by coupling reanalysis and seasonal forecast data of soil moisture with a calibrated nonlinear transfer model. The approach leverages ERA5 reanalysis and SEAS5 seasonal forecasts to estimate flux toward the aquifer and forecast water table elevation. A case study in the Umbria Region of central Italy demonstrates the model's ability to simulate and predict monthly water table fluctuations. Two modeling strategies are compared: a static calibration approach (OPT1) and a dynamic calibration approach (OPT2), where model parameters are updated by considering different time periods. Both options yielded skillful forecasts across lead times of 1 to 6 months, with OPT2 showing slightly improved stability in forecast performance metrics. Results confirm the feasibility of incorporating seasonal climate forecasts into operational groundwater prediction frameworks. As expected, forecast accuracy is limited by the skill of precipitation predictions, especially during autumn and winter. The proposed framework lays the groundwork for anticipatory aquifer management and early warning systems under evolving hydroclimatic conditions.
Saraceni et al. (Mon,) studied this question.