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Soil Saturation Dictates Africa’s Flood Severity

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Soil Saturation Dictates Africa’s Flood Severity

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The most complete hydrological data set for the African continent reveals a surprise: Soil moisture, not heavy precipitation, best explains the timing of Africa’s most severe floods.

Floods across Africa (including Buliisa, Uganda) displaced hundreds of people in 2020 and 2021. Credit: KikonkoCC-BY-SA 4.0

In the summer of 2020, deadly floods ravaged Africa, affecting nearly a million people and killing hundreds. However, the physical causes of floods across the continent’s diverse climate and terrain are gravely understudied. Lacking a broad network of water gauges, researchers have focused primarily on specific countries or single bodies of water. “The large extension of ungauged areas [has prevented] significant studies [from being conducted both] quantitatively and qualitatively,” said Mohamed El Mehdi Saidi of Cadi Ayyad University in Morocco.

That has now changed, thanks to a 2-year project by an international team to curate the most complete hydrological data set for the African continent to date. This massive compilation combines on-the-ground and remote sensing measurements covering nearly 1,500 stream gauges and more than 11,000 flood events spanning at least 3 decades. The team’s analysis, the first continent-wide study of flood drivers in Africa, suggested that the largest yearly floods are more strongly linked to regions’ annual peaks in soil moisture than to annual peaks in precipitation.

Other research teams have conducted several continent-wide studies of flood drivers across the United StatesEurope, and Australia. Higher data coverage of stream flows and flooding patterns across these landmasses has led to a stronger understanding of when and why damaging floods occur. These continents, however, differ drastically from Africa climatically and geographically, leading scientists to suspect that the triggers of African floods are unique.

Africa’s largely arid climate, with the Sahara covering 25% of the landmass, is part of that equation. “You additionally have this ability to study a climate largely free of snow, which is a complicating factor when studying floods,” said infrastructure engineer Conrad Wasko of the University of Melbourne in Australia who was not involved in the study. With deadly floods becoming increasingly frequent in Africa as climate change worsens, hydrologists felt compelled to improve their data collection across the continent’s widely varying river basins.

The team’s African Database of Hydrometric Indices (ADHI), published in Earth System Science Data, includes hydrological parameters from watersheds across Africa spanning 33 years on average. Given the sparseness of data across the continent, the team took laborious steps to ensure that the records from different sources were of similar quality. “The most important thing was to manually and visually check each [measurement] independently,” said Yves Tramblay, a hydrologist at the French National Research Institute for Sustainable Development and lead author of the study.

For regions lacking in ground observations, the scientists incorporated Climate Hazards group Infrared Precipitation with Stations (CHIRPS), a series of remote sensing estimates from a hybrid satellite and ground data set, to obtain a homogeneous average of precipitation across all of Africa. They validated these measurements with gauged data when possible. The team’s thorough approach impressed Wasko: “Within engineering, we have a predisposition to collect [on-the-ground] data. New technologies, like remote sensing, are becoming essential to understanding hydrology in remote areas,” he said.

A New Flood Driver Takes the Stage

The ADHI data set allowed the team to compare the timing of several parameters relevant to floods. To determine which ones aligned most strongly with the largest floods each season, they isolated the dates when floods occurred and rigorously compared them to the timing of heavy rainfall and soil moisture conditions using directional statistics—a method that accounts for the direction data follow (in this case, the timing). The analysis revealed that high soil moisture levels showed a stronger correlation to the onset of flooding than to other parameters, most notably rainfall.

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