Predicting Flash Droughts Using Transformers: Understanding Surface and Root Zone 

Abstract

Flash droughts, characterized by their rapid onset and devastating agricultural and ecological impacts, pose a growing threat in a changing climate. Accurate and timely predictions are crucial for implementing mitigation strategies and minimizing their widespread consequences. This research presents a novel transformer-based forecasting system designed to predict soil moisture with a focus on detecting the early warning signs of flash droughts in North America. This study integrates the concepts of the two main soil moisture zones, surface and root zones, to provide a comprehensive understanding of drought dynamics. The research leverages the NLDAS (North American Land Data Assimilation System) simulation dataset, offering high-resolution spatiotemporal information crucial for accurate modeling. The transformer-based architecture is employed to capture complex temporal dependencies and non-linear relationships inherent in soil moisture variations. The architecture captures long-range dependencies and complex interrelations within the data, enabling accurate predictions of both surface and root zone moisture content. This approach enables the development of a robust forecasting model capable of capturing sudden and intense decreases in soil moisture characteristic of flash droughts. The system considers the relationship between surface and root zone soil moisture, acknowledging their distinct roles in impacting vegetation health, water availability, and overall ecosystem resilience. By incorporating this dual-zone perspective, the forecasting system enhances the accuracy of flash drought predictions, providing valuable insights for early intervention and adaptive management. Through rigorous evaluations and comparisons with existing forecasting methods, we assess the system's performance in capturing spatiotemporal variability and providing lead time for proactive mitigation strategies. Our findings shed light on the transformative potential of deep learning for flash drought prediction, highlighting the crucial role of understanding the interplay between surface and root zone moisture dynamics in this context.