Starving the Mekong

Entry type: Single project

Country/area: United States

Publishing organisation: Reuters

Organisation size: Big

Publication date: 2022-12-15

Language: English

Authors: Simon Scarr, Clare Trainor, Manas Sharma, Adolfo Arranz, Kanupriya Kapoor, Phuong Nguyen


The Reuters graphics team publishes visual stories and data visualisations. The team typically cover all areas of the news, with content ranging from climate change to financial markets. Many of the pieces are conceptualised, researched, and produced by the graphics team.

Project description:

An exclusive data analysis shows how dams in the upper Mekong River are holding back up to 80% of sediment that should flow through the 4,900km-long waterway, starving the delta in Vietnam of nutrient-rich, land stabilizing soil essential for farming. This graphics story brings together data visualization and on-the ground reporting to show how this phenomenon is gradually but irreversibly reshaping the lives of millions of people whose livelihoods depend on the river.

Impact reached:

Scientists and environmental activists have for decades raised alarm over the main culprit behind diminishing sediment – upstream hydropower dams. Now, Reuters provides fresh evidence that the cascading sediment-trapping effect of these dams has far-reaching consequences downstream and is leaving millions with no choice but to adapt to the environmental changes forced on them.

The project was picked up and praised on social media by the scientific community as well as readers.

Techniques/technologies used:

The analysis relied on measurements of turbidity depicted in satellite images – the amount of light scattered by solid particles suspended in water – as a proxy for sediment levels. Sediment clouds the water as it is carried along by the current: the muddier the water, the higher the turbidity and the more sediment it is likely carrying. Thousands of images were analysed.

The satellite images for the Mekong analysis date back to the 1990s, which “allows us to calculate turbidity levels before many of the dams were built,” said EOMAP data analyst Philipp Bauer.

After discarding images obscured by cloud cover or pollution, the team was left with 1,500 images depicting the turbidity around two dams in China and two in Laos. Scientists not involved in conducting the analysis agreed that the findings made clear that the dams were a key culprit behind the delta’s sediment loss.

Much of the mapping and analysis was conducted in QGIS. Python was used to process large batches of data. Lottie library as well as javascript was used for animation and interactivity. Adobe’s creative suite was used for much of the final styling of graphics.

Context about the project:

The project began following a conversation with EOMAP about the technology they use to detect turbidity in water bodies. This led to a collaboration which explored multiple areas of the Mekong and took many months of research before any analysis and production began.

This was one of the most ambitious data exercises carried out on the Mekong. One challenge was refining the data and determining how to present the findings.

The team also made efforts to visit those who are living on the banks of the river and add strong traditional reporting to back up the data extracted from satellites

What can other journalists learn from this project?

There can often be innovative ways to acquire data if a public dataset does not exist. That lack of available data can make the final story even more powerful.

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