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Data centers are critical to today’s digital economy, but are also among the largest industrial consumers of freshwater. Beyond the sheer volume of water use, the environmental impact of data center water consumption varies significantly across locations and seasons, depending on local and regional water stress. However, prior research has largely focused on reducing total water use, overlooking that the same unit of water can have drastically different environmental consequences depending on when and where it is consumed. In this paper, we introduce a stress-adjusted water framework that quantifies the true sustainability impact of data center water consumption by incorporating both spatial and temporal water stress. Using the AWARE-US model, we capture county-level monthly variations in water availability and extend this framework to account for the off-site water footprint of electricity generation. Based on this stress-aware accounting, we analyze stress-adjusted water-computing strategies spanning both the software and infrastructure layers. Specifically, we study workload scheduling policies that jointly optimize water and carbon efficiency, evaluate the potential of rainwater harvesting as a supplemental water source, and investigate the feasibility of dry cooling as a water-free alternative to evaporative cooling. Our evaluation across major U.S. data center markets shows that stress-aware workload scheduling can reduce stress-adjusted water consumption by up to 25% while preserving performance and balancing carbon emissions. We further show that rainwater harvesting can offset up to 100% of on-site cooling water in regions with sufficient precipitation, while offering diminishing returns in arid locations. Finally, our analysis of dry cooling reveals that its effectiveness depends critically on energy efficiency and off-site water stress, highlighting important trade-offs between water savings, energy use, and carbon emissions. Together, these results demonstrate that incorporating water stress as a first-class sustainability metric enables more informed and effective data center design and operation
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A Dataset for Research on Water Sustainability
Freshwater scarcity is a global problem that requires collective efforts across all industry sectors. Nevertheless, a lack of access to operational water footprint data bars many applications from exploring optimization opportunities hidden within the temporal and spatial variations. To break this barrier into research in water sustainability, we build a dataset for operation direct water usage in the cooling systems and indirect water embedded in electricity generation. Our dataset consists of the hourly water efficiency of major U.S. cities and states from 2019 to 2023. We also offer cooling system models that capture the impact of weather on water efficiency. We present a preliminary analysis of our dataset and discuss three potential applications that can benefit from it. Our dataset is publicly available at Open Science Framework (OSF).
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- PAR ID:
- 10534109
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400704802
- Page Range / eLocation ID:
- 442 to 446
- Format(s):
- Medium: X
- Location:
- Singapore Singapore
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3632775.3661962
