The global demand for artificial intelligence (AI) is fueling a rapid expansion of data infrastructure, an industry that is notoriously water-intensive.This growth creates a critical, yet understudied, nexus between digital expansion and hydrological systems, particularly in ecologically vulnerable regions.This study applies a spatially explicit framework to quantify the water footprint of AI data centres in Brazil, a nation heavily reliant on drought-sensitive hydropower.Our method integrates datasets on data centre locations, regional hydrological cycles, power generation sources, and watershed-level water stress indices to model both direct (cooling) and indirect (energy generation) water consumption.Our key finding is that the AI infrastructure cluster in the So Paulo metropolitan region, with an operational IT load of approximately 550 MW, has an estimated annual water footprint of 16.1 million cubic meters.A significant portion of this, over 46%, is indirect "virtual water" consumed through hydropower generation, establishing a direct feedback loop where data centre demand stresses water and energy systems already compromised by climate change.This paper concludes that the environmental cost of AI extends beyond carbon to include water, a cost disproportionately borne by biodiverse regions.We call for a paradigm shift in tech policy and corporate sustainability to include metrics of water neutrality and watershed resilience, in alignment with global sustainability goals.
Guangda Liang (Mon,) studied this question.