From Large-scale Hydrological Models to Local Action: A Framework for Operational Groundwater Level Forecasting at the Point Scale

As climate change alters precipitation patterns, regional water governance and the agricultural sector face a critical challenge: transitioning from reactive crisis management to proactive water allocation. In recent years, regional authorities in Germany have increasingly been forced to issue water extraction bans to protect groundwater and surface water resources. However, such restrictions rely on water budget assessments derived from point-scale water level observations. While large-scale integrated hydrological models provide essential insights into long-term trends, their coarse spatial resolution often fails to accurately predict point-scale groundwater levels, creating a resolution gap for local decision-makers who require site-specific information for regulatory and operational purposes.

We present a Proof of Concept for an operational groundwater level forecasting framework designed to bridge this gap between large-scale modeling and local application. This approach focuses on the integration of existing, openly available data, combining historical local groundwater observations with large-scale recharge data from the integrated ParFlow hydrologic model. By applying a hybrid methodology—utilizing Fourier-based time-series analysis coupled with a simplified 2D groundwater table model —we demonstrate how large-scale model outputs can be downscaled into point-scale information.

Currently, the operational pipeline has been implemented for selected groundwater gauges in Saxony, featuring automated data ingestion and processing. We showcase the potential of this framework to provide agricultural stakeholders and water authorities with lead time needed for informed management decisions. Future developments will focus on expanding the gauge network, implementing a GIS-based interface for spatial visualization, and potentially integrating thresholds for groundwater extraction bans to increase regulatory predictability. By utilizing established scientific methods and data, this work provides a blueprint for transferring hydrological outputs into actionable information for stakeholders in regional water management and agriculture.