Assessment and challenges in forecasting heatwaves as a precursor of flash droughts in southern South America

Abstract

Flash droughts (FDs) are a particular type of drought that develops rapidly and causes significant impacts, especially in the agricultural sector. While droughts in general are slow-evolving disasters with broad spatial extent and long duration, FDs are characterized by a fast depletion of soil moisture, triggered by a combination of precipitation deficits, heatwaves, and low atmospheric humidity. Despite their importance, knowledge about FDs remains limited—from their detection and characterization to the development of monitoring indicators and forecasting tools. Although the South American Drought Information System (SISSA) has made progress in monitoring, forecasting FDs and heat waves remains a major challenge due to the temporal scale at which they develop and their low frequency of occurrence, making certain machine learning approaches difficult to apply. This study aims to assess the predictability of heat wave and FD events through the use of the Extreme Forecast Index (EFI) from the ECMWF ensemble model and the Artificial Intelligence Forecast System (AIFS). Additionally, the study will analyze the application of univariate and multivariate logistic regressions to atmospheric and soil variables from the ERA5-Land reanalysis to estimate the probability of FD occurrence.