With hundreds of hydropower dams currently proposed for the Amazon basin – an ecologically sensitive area covering more than a third of South America – predicting their greenhouse emissions in advance could be critical for the region, and the planet.
A Cornell-led team including ecologists, computer scientists and researchers from South American organizations has developed a computational model that uses artificial intelligence to find the most promising configurations of dam sites amid a staggering number of possible combinations.
The researchers found that achieving low-carbon hydropower requires planning that considers the entire Amazon basin – and favors dams at higher elevations.
“If you develop these dams one at a time without planning strategically – which is how they’re usually developed – there is a very low chance that you’ll end up with an optimal solution,” said Rafael Almeida, a postdoctoral research fellow with the Atkinson Center for a Sustainable Future and co-lead author of “Reducing Greenhouse Gas Emissions of Amazon Hydropower with Strategic Dam Planning,” which published Sept. 19 in Nature Communications.
“Developing dams suboptimally can lead to really undesirable outcomes, with emission levels that are not compatible with future sustainable energy goals,” Almeida said.
When areas are flooded to build dams, decomposing plant matter produces methane, a more destructive greenhouse gas than carbon dioxide. Depending on location and other factors, the carbon emissions from dam construction can vary from lowest to highest by more than two orders of magnitude. Using the model, the researchers can identify the combination of dams that would produce the lowest amounts of greenhouse gases for a given energy output target.
The analysis found that dams built at high elevations tend to have lower greenhouse gas emissions per unit of power output than dams in the lowlands – partly because less land needs to be flooded in steeper environments.
Since methane doesn’t remain in the atmosphere as long as carbon dioxide, the researchers’ analysis considered both 20-year and 100-year time frames. The method, which can compute highly accurate solutions in a matter of minutes, could also be used to gauge environmental impacts of other power sources in other regions.
For the analysis, Gomes said, the researchers used a powerful “divide and conquer” computational approach.
“We needed to develop algorithms that are able to reduce this problem into subproblems and, in a smart way, put together the results to produce solutions with the lowest greenhouse gas emissions, for given energy targets,” she said.
There are currently around 150 hydropower dams and another 350 proposed for the Amazon basin, which encompasses parts of Brazil, Ecuador, Peru and Bolivia. This study is part of a larger effort to use computational tools to analyze the dams’ impact, to help South American governments and organizations make informed decisions that balance the benefits and disadvantages.
“Traditionally, optimization is done with respect to a single objective,” Gomes said. “But because of the big picture, we need to think about multiple criteria. While something may be good from an energy perspective, it could have tremendous consequences for other environmental goals. So this is where we come in, to do this massive computation that looks at the problem from a multiple-objective point of view.”
The research team brings together experts in fields ranging from hydrology to biodiversity to artificial intelligence, to examine how the dams will affect the entire ecosystem.
“We want to develop ways to think about whether some of these projects might be more or less impactful than others,” Flecker said. “For example, maybe a certain dam might interrupt the connections in the river network and block fish migrations. You have to consider the tradeoffs between generating hydropower and the environmental impact, so society can make the decisions it deems acceptable.”
Co-authors included Qinru Shi, a doctoral student with the Institute for Computational Sustainability, and Suresh Sethi of the New York Cooperative Fish and Wildlife Research Unit. Also contributing to the paper were researchers from Stanford University, the Stockholm Environment Institute Latin America, the Federal University of Juiz de Fora, the National Institute of Amazonian Research, Michigan State University, the Cary Institute of Ecosystem Studies, the Wildlife Conservation Society and the University of California, Santa Barbara.
The research was supported by the Atkinson Venture Fund, the Atkinson Postdoctoral Fellowship Program, the National Science Foundation, the Future of Life Institute and the Army Research Office, through its Defense University Research Instrumentation Program.
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