Estimating the components of the hydrological budget of the Alpine lakes using the GEOFrame modeling system

The Italian Alpine region faces significant challenges in water resource management due to competing demands from agriculture, hydropower production, and flood risk mitigation. Rising temperatures and ongoing glacier retreat pose unprecedented pressures, highlighting the need for an improved understanding of hydrological cycle components. This region hosts numerous Alpine lakes that play a key role in water storage, including the four largest – Lake Maggiore, Lake Como, Lake Iseo, and Lake Garda – which together provide up to 1.2 billion m³ of storage capacity over a catchment area exceeding 12000 km².

The aim of this work is to give an overview of the modelling framework and the calibration procedures that were performed on this area using the GEOFrame-NewAge hydrological modeling system, an open-source, modular platform based on Java components, designed to represent the complex physical processes of the hydrological cycle.

The upstream lake catchments were discretized into sub-basins and modelled using a semi-distributed approach, with processes evaluated at sub-basin centroids based on spatially averaged properties. A major focus was placed on harmonizing regional meteorological datasets, as preliminary analyses revealed a systematic underestimation of precipitation at high-elevation gauges. This required a re-evaluation of input data using historical sources and atlases, particularly for the Swiss catchment of Lake Maggiore. Evapotranspiration estimates were improved by introducing a semi-distributed net radiation scheme computed on a 1500 m grid, which enhanced model performance compared to centroid-based calculations.

Model calibration was challenging due to the dense network of water infrastructures that alter natural flow regimes and bypass many gauging stations. Calibration therefore relied on selected hydrometric stations and time periods minimally affected by anthropogenic influences, enabling a consistent basin-wide calibration. Using the Kling-Gupta Efficiency as the objective function, the model achieved excellent performance, with calibration and validation values often exceeding 0.8. Post-processing analyses also showed good agreement with long-term averages of key hydrological components.

By enabling the estimation of impacts on long-term water availability, this successfully calibrated model provides a powerful tool to quantify water scarcity, optimize reservoir management, and minimize conflicts among stakeholders, especially under a changing climate.