Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany - S. Kunz; A. Schulz; M. Wetzel; M. Nölscher; T. Chiaburu; F. Biessmann; S. Broda (Hydrology and Earth System Sciences)
Reliable predictions of groundwater levels are crucial for sustainable groundwater resource management, which needs to balance diverse water needs and to address potential ecological consequences of groundwater depletion. Machine learning (ML) approaches for time series forecasting have shown promising accuracy for groundwater level prediction and, furthermore, offer scalability advantages over traditional numerical methods when sufficient data are available. Global ML architectures enable predictions across numerous monitoring wells concurrently using a single model, allowing predictions over a broad range of hydrogeological and meteorological conditions and simplifying model management. In this contribution, groundwater levels for 5288 monitoring wells across Germany were forecasted up to 12 weeks using two state-of-the-art ML approaches, the Temporal Fusion Transformer (TFT) and the Neural Hierarchical Forecasting for Time Series (N-HiTS) algorithm. The models were provided with historical groundwater levels, meteorological features, and a wide range of static features describing hydrogeological and soil properties at the monitoring sites. To determine the conditions under which the model achieves good performance and whether it aligns with hydrogeological system understanding, the model's performance was evaluated spatially and correlations with both static input features and time series features from hydrograph data were examined.