Early Flood Warning Using Satellite-Derived Convective System and Precipitation Data -- A Retrospective Case Study of Central Vietnam

TL;DR

This study demonstrates how satellite data can be integrated to improve early flood warning systems by monitoring convective systems and heavy rainfall in central Vietnam, focusing on the October 2020 flood events.

Contribution

It introduces a retrospective approach combining multiple satellite datasets to enhance early flood detection and understanding of convective system behaviors.

Findings

Satellite data effectively identify and track convective clouds.

Integrated datasets improve understanding of flood-triggering meteorological conditions.

The approach aids in predicting flood magnitudes based on convective system characteristics.

Abstract

This paper addresses the challenges of an early flood warning caused by complex convective systems (CSs), by using Low-Earth Orbit and Geostationary satellite data. We focus on a sequence of extreme events that took place in central Vietnam during October 2020, with a specific emphasis on the events leading up to the floods, i.e., those occurring before October 10th, 2020. In this critical phase, several hydrometeorological indicators could be identified thanks to an increasingly advanced and dense observation network composed of Earth Observation satellites, in particular those enabling the characterization and monitoring of a CS, in terms of low-temperature clouds and heavy rainfall. Himawari-8 images, both individually and in time-series, allow identifying and tracking convective clouds. This is complemented by the observation of heavy/violent rainfall through GPM IMERG data, as well as the detection of strong winds using radiometers and scatterometers. Collectively, these datasets, along with the estimated intensity and duration of the event from each source, form a comprehensive dataset detailing the intricate behaviors of CSs. All of these factors are significant contributors to the magnitude of flooding and the short-term dynamics anticipated in the studied region.