Fine Flood Forecasts: Calibrating global machine learning flood forecast models at the basin level through fine-tuning.

Machine learning (ML) models have transformed our ability to perform reasonably-accurate, large-scale river discharge modeling, opening new opportunities for global prediction in ungauged basins. These ML models are data-hungry, and results have conclusively shown that ML techniques do best when a single ML model is trained on all basins in the dataset. This is contrary to inuitions from the hydrological sciences, where individual basin calibration traditionally provides the best forecasts. 

 

We bridge this gap between intuitions from traditional ML and hydrology by pre-training a single global model on basins in the worldwide Caravan dataset (~6000 basins), and then fine-tune that model on individual basins. This is a well-known practice within ML, and for us serves the purpose of producing models aimed at high-quality local prediction problems while still capturing the advantages of large-sample training. We show that this leads to a significant skill improvement. 

 

We have also conducted analysis of geophysical and hydrological regimes that benefit most from fine-tuning. These results point to how flood forecasting and water management agencies and operators can expect to fine-tune large, pretrained models on their own local data, which may be proprietary and not part of large, global training datasets.

 

This work illustrates how local agencies like national hydromet agencies or flood forecasting agencies might be able to leverage machine learning based hydrological forecast models while also maximizing the value and information of local data by tailoring large, pretrained models to their own local context. This is an important step in allowing local agencies to take ownership of these global models, and directly incorporate local hydrological understanding to improve performance.