Hybrid Hydrological Modeling in the Forni Basin: Combining SWAT-GL and Machine Learning Techniques

Accurate streamflow predictions in glacial basins are critical for effective water resource management and flood risk mitigation, especially under changing climatic conditions. This study presents a hybrid modeling framework that integrates the SWAT-GL model, an extension of the Soil and Water Assessment Tool (SWAT) designed to include glacial hydrological processes, with advanced machine learning techniques (Random Forest, Support Vector Regression, and Multilayer Perceptron).

SWAT-GL was selected for its proven ability to simulate glacial hydrological processes at a daily scale. However, its application at an hourly scale is limited due to the reliance on the Green-Ampt infiltration method, which is less suitable for representing the unique soil characteristics typically observed in glacier-fed basins. To overcome this limitation, machine learning models were employed to refine the daily SWAT-GL outputs into hourly predictions. An ensemble model was developed to enhance accuracy, combining the complementary strengths of the individual machine learning approaches.

The model was calibrated using data from 2021 to 2023, with a one-year warmup period (2020), and validated with observed data from January 2024 to September 2024. Meteorological forecasts from ECMWF-IFS and MOLOCH models were incorporated, providing hourly data on precipitation, temperature, solar radiation, and wind speed. This approach enabled day-ahead operational forecasting, aligning model outputs with real-time management needs.

The ensemble model showed strong performance during training and testing, highlighting its robustness in refining daily SWAT-GL outputs into accurate hourly predictions.

The hybrid framework was applied to the Forni Basin, a glacier-fed system in the Italian Alps characterized by high variability in meltwater contributions and limited hydrological data. By addressing key challenges such as input uncertainties, limitations of process-based modeling at sub-daily scales, and scaling from daily to hourly forecasts, the model offers a robust tool for predicting streamflow in data-scarce, glacierized regions. This study highlights the potential of hybrid approaches to improve hydrological forecasting accuracy and scalability, contributing to the sustainable management of water resources in sensitive alpine environments.