![[2602.16579] AIFL: A Global Daily Streamflow Forecasting Model Using Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16579] AIFL: A Global Daily Streamflow Forecasting Model Using Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS
Summary
The paper presents AIFL, a deterministic LSTM model for global daily streamflow forecasting, trained on ERA5-Land and fine-tuned on IFS, achieving high predictive skill.
Why It Matters
Reliable streamflow forecasting is crucial for flood preparedness and water resource management. AIFL addresses performance gaps in transitioning from historical data to operational forecasts, offering a competitive and reproducible model for the hydrological community.
Key Takeaways
- AIFL is the first global streamflow forecasting model trained end-to-end within the CARAVAN ecosystem.
- The model achieves a median modified Kling-Gupta Efficiency (KGE') of 0.66 and a median Nash-Sutcliffe Efficiency (NSE) of 0.53.
- AIFL demonstrates exceptional reliability in detecting extreme events, enhancing operational robustness.
- The two-stage training strategy effectively bridges the gap between historical reanalysis and operational forecasts.
- AIFL maintains a transparent and reproducible forcing pipeline, contributing to the global hydrological community.
Computer Science > Machine Learning arXiv:2602.16579 (cs) [Submitted on 18 Feb 2026] Title:AIFL: A Global Daily Streamflow Forecasting Model Using Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS Authors:Maria Luisa Taccari, Kenza Tazi, Oisín M. Morrison, Andreas Grafberger, Juan Colonese, Corentin Carton de Wiart, Christel Prudhomme, Cinzia Mazzetti, Matthew Chantry, Florian Pappenberger View a PDF of the paper titled AIFL: A Global Daily Streamflow Forecasting Model Using Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS, by Maria Luisa Taccari and 9 other authors View PDF HTML (experimental) Abstract:Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products. This paper introduces AIFL (Artificial Intelligence for Floods), a deterministic LSTM-based model designed for global daily streamflow forecasting. Trained on 18,588 basins curated from the CARAVAN dataset, AIFL utilises a novel two-stage training strategy to bridge the reanalysis-to-forecast domain shift. The model is first pre-trained on 40 years of ERA5-Land reanalysis (1980-2019) to capture robust hydrological processes, then fine-tuned on operational Integrated Forecasting System (IFS) control forecasts (2016-2019) to adapt to the specific error structures and biases of operational numerical weat...