Flooding in Central Chile due to two extreme rain events: Insights from satellite remote sensors

Abstract

During the winter of 2023 in the Southern Hemisphere, Central Chile experienced two extreme rain events, impacting an area where over 70% of the country's population resides. The initial rain event transpired in late June, accumulating over 700 mm within five days. This led to significant damage to critical infrastructure, isolating towns and villages, particularly in the Mataquito basin. Subsequently, a second rain event in August affected the southern part of central Chile, specifically the Maule basin, mirroring the impacts observed in Mataquito. Given the infrastructure affectation and the need to mitigate risks it is imperative to identify flood-affected areas. This task is complicated considering the lack of in-situ measurements, but of increasing relevance considering the heightened likelihood of more intense rain events attributable to climate warming. Here, we employed a suite of satellite remote sensors, utilizing Google Earth Engine for analysis. Our approach integrated active and passive sensors, employing change detection algorithms such as Spectral Euclidean Distance (SED) and Spectral Angle Measurement (SAM) on Sentinel-2 images. Additionally, we employed supervised classification, utilizing the random forest algorithm to train the model for flood detection. To enhance accuracy, we calibrated our Sentinel-1 GRD results to the flood-identified areas in Sentinel-2 imagery. Our findings revealed a notable correlation between the identified flooded areas and in-situ measurements. Surprisingly, SAM demonstrated high accuracy in identifying flooding areas with elevated values, whereas SED presented a broader range of results that were often inconclusive, however, their use is limited by cloud presence. Despite these limitations, the calibration of active remote sensors using detected floods as ground truth proved valuable in establishing change thresholds for future rain events in similar regions.