Leveraging Deep Learning and Transfer Learning Methods for Hydrological Flash Drought Forecasting in Global River Basins

Exacerbated by climate change, flash droughts pose significant threats to global food and water security. Their rapid onset and severe ecological and economic impacts necessitate the development of improved prediction methods. Leveraging machine learning (ML) and transfer learning (TL), this study targets the development of hydrological flash drought forecasts using catchment-specific indices. By identifying crucial meteorological conditions and catchment attributes, it aims to bridge existing prediction gaps. Furthermore, it investigates the transferability of ML models trained in data-rich regions like the U.S. to predict flash droughts in data-poor regions, offering insights crucial for proactive water resource management and conflict prevention.