Machine Learning to augment global flood modelling in ungauged basins

Advancements in the fields of remote sensing, and high-performance computing have facilitated higher resolution and coverage of global flood risk maps. However, the distribution and availability of streamflow from in-situ gauges is not uniformly distributed, posing significant challenges. Machine learning (ML) offers a powerful framework to augment streamflow datasets by leveraging diverse data sources, such as remote sensing, climate reanalysis, and hydrological simulations. This study explores the application of ML techniques to generate synthetic streamflow data for ungauged basins, enhancing the coverage, and quality of global flood models for commercial applications. By integrating the principles of physical hydrology with data-driven approaches, we demonstrate that ML can effectively capture spatial and temporal dynamics of streamflow in regions with scarce observational data, or seasonal variation in flows. Key methods include supervised learning algorithms trained on gauged basins to predict streamflow to create a synthetic dataset of streamflow observations. Validation using global hydrological benchmarks indicates that the ML-augmented datasets significantly improve flood prediction accuracy, particularly in data-sparse regions.