Monitoring changes and time-dependent preprocessing can bias groundwater forecast evaluation. We test Telangana depth forecasts trained on historical IndiaDataPortal records and evaluated on later Central Ground Water Board telemetry; no telemetry series supplies a training window. A Temporal Fusion Transformer (TFT) is compared with recurrent, statistical, and tree-based pipelines at 1, 3, 6, and 12 months, excluding imputed targets and using repeated seeds, paired tests, and input ablations. For six months out, the five-seed ensemble of the TFT attains RMSE of 4.763 m compared to 5.027 m for a long short-term memory network (Holm adjusted p = 0.0064), with the gap varying across horizons. The removal of rainfall and satellite storage inputs decreases the RMSE of the three-seed ensemble by 0.152 m for six months and 0.235 m for twelve months. Rolling calibration increases 80% coverage from 50.9% to 80.2% but expands mean width from 4.263 to 7.612 m. Historical depth filters detect just two out of eleven crossings amongst 162 wells.

Groundwater Forecasting,Temporal Fusion,Transformer,Crosssource evaluation,Quantile Regression,Uncertainity Cali,Telangana