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1. Balancing Bits and Drops: Stress-Adjusted Water Management for Data Centers

   https://doi.org/10.1145/3744255.3811726  

   Talukder, Zahidur; Rahim, Imtiaz Bin; Gupta, Pranjol Sen; Ren, Shaolei; Islam, Mohammad A (June 2026, ACM)

   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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2. A Dataset for Research on Water Sustainability

   https://doi.org/10.1145/3632775.3661962  

   Gupta, Pranjol Sen; Hossen, Md Rajib; Li, Pengfei; Ren, Shaolei; Islam, Mohammad A (May 2024, ACM)

   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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3. Data Center Environmental Burden Reduction Through On-Site Renewable Power Generation

   https://doi.org/10.1115/1.4065053  

   McMullen, M; Wemhoff, A P (May 2024, ASME Journal of Engineering for Sustainable Buildings and Cities)

   Abstract The energy demands from data centers contribute greatly to water scarcity footprint and carbon emissions. Understanding the use of on-site renewable power generation is an important step to gain insight into making data centers more sustainable. This novel study examines the impact of on-site solar or wind energy on data center water scarcity usage effectiveness (WSUE) and carbon usage effectiveness (CUE) at a U.S. county scale for a given data center size, water consumption level, and energy efficiency. The analysis uncovers combinations of specific metrics associated with grid-based carbon emissions and water scarcity footprint that enable predictions of the improvements anticipated when implementing on-site solar or wind energy. The implementation of on-site renewables has the most benefit in reducing carbon footprint in areas with high existing grid-based emissions such as the western side of the Appalachian Mountains (e.g., central and eastern Kentucky). The largest benefit in reducing water scarcity footprint is generally seen in counties with low water scarcity compared to adjacent areas (e.g., northern California). 

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4. The relationship between data centers and the climate is a systems challenge: a spatial analysis of United States data centers

   https://doi.org/10.1088/2515-7620/ae193a  

   Kollar, Abbey; Grady, Caitlin (November 2025, Environmental Research Communications)

   Abstract Data centers are a rapidly growing infrastructure that support digital and artificial intelligence needs. However, their expansion has raised concerns about impact based on electricity and water usage. Beyond quantifying the impact of the data center on the environment, this study presents an energy-climate operational risk analysis of over 2,400 data centers in the United States, focusing on location-specific water and heat risks and the dependency of emissions on local energy mixes. Findings highlight that current framing around the sustainability and reliability of data center operations could benefit from systems approaches for a more holistic understanding. We find that many data centers are in regions with significant water stress and are exposed to heat waves. These operational risks, along with the emissions variability due to regional grid differences, underscore the need for sustainable and reliable future planning of data centers. 

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5. Potential Environmental Impacts of Peanut Using Water Footprint Assessment: A Case Study in Georgia

   https://doi.org/10.3390/agronomy12040930  

   Deepa, Raveendranpillai; Anandhi, Aavudai; Bailey, Nathaniel O.; Grace, Johnny M.; Betiku, Omolola C.; Muchovej, James J. (April 2022, Agronomy)

   The recent decade has witnessed an increase in irrigated acreage in the southeast United States due to the shift in cropping patterns, climatic conditions, and water availability. Peanut, a major legume crop cultivated in Georgia, Southeast United States, has been a staple food in the American household. Regardless of its significant contribution to the global production of peanuts (fourth largest), studies related to local or regional scale water consumption in peanut production and its significant environmental impacts are scarce. Therefore, the present research contributes to the water footprint of peanut crops in eight counties of Georgia and its potential ecological impacts. The impact categories relative to water consumption (water depletion—green and blue water scarcity) and pesticide use (water degradation—potential freshwater ecotoxicity) using crop-specific characterization factors are estimated for the period 2007 to 2017 at the mid-point level. These impacts are transformed into damages to the area of protection in terms of ecosystem quality at the end-point level. This is the first county-wise quantification of the water footprint and its impact assessment using ISO 14046 framework in the southeast United States. The results suggest inter-county differences in water consumption of crops with higher blue water requirements than green and grey water. According to the water footprint analysis of the peanut crop conducted in this study, additional irrigation is recommended in eight Georgia counties. The mid-point level impact assessment owing to water consumption and pesticide application reveals that the potential freshwater ecotoxicity impacts at the planting and growing stages are higher for chemicals with high characterization factors regardless of lower pesticide application rates. Multiple regression analysis indicates blue water, yield, precipitation, maximum surface temperature, and growing degree days are the potential factors influencing freshwater ecotoxicity impacts. Accordingly, a possible impact pathway of freshwater ecotoxicity connecting the inventory flows and the ecosystem quality is defined. This analysis is helpful in the comparative environmental impact assessments for other major crops in Georgia and aids in water resource management decisions. The results from the study could be of great relevance to the southeast United States, as well as other regions with similar climatic zones and land use patterns. The assessment of water use impacts relative to resource availability can assist farmers in determining the timing and layout of crop planting. 

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