ABSTRACT:

Thermoelectric power was among the largest freshwater-withdrawing sectors in the U.S. as of the most recent complete U.S. Geological Survey national assessment (≈41% of withdrawals in 2015). Despite this scale of water use, existing datasets and raw U.S. Energy Information Administration (EIA) records often retain duplicates, inconsistencies, and operationally implausible values that hinder reproducible analysis. To address this, we introduce the U.S. Operationally Plausible Thermoelectric Water Use Dataset (USOPTD), a plant-resolved, quality-controlled dataset of monthly water withdrawal, consumption, and generation records for U.S. power plants from 2014-2024. USOPTD applies a novel operational-plausibility filtering method, utilizing calculated power-cooling ratios, to remove anomalous records and accurately capture real water use. Crucially, this methodology provides highly granular monthly water-use records and consumptive use coefficients for individual plants, revealing distributional variation that is obscured by aggregated median values. By resolving data fragmentation and enforcing thermodynamic constraints, USOPTD allows users to produce internally consistent estimates of thermoelectric water-use intensity across temporal, spatial, and generator-specific dimensions. This dataset provides the scientific community with an operationally plausible foundation for reliable water-energy nexus modeling and resource management.

ABSTRACT:

Thermoelectric power generation accounts for over 41% of total U.S. freshwater withdrawals, making understanding the determinants of power plants’ water withdrawals (WW) and consumption (WC) critical for reducing the sector's reliance on increasingly scarce water resources. However, reported data inconsistencies and incomplete analysis of potential determinants of thermoelectric water use hinder such efforts. We address these challenges by introducing a novel data filtering method and a richer assessment of water use determinants. First, we applied a power-cooling ratio as an operations-based data filter that removed operationally implausible records while retaining more original data, outperforming previous statistical filtering methods. Second, we found that different water use reporting methods (WURMs) provided statistically significantly different WW and WC values, revealing the importance of this previously unrecognized feature in reported water use records. Third, our data-driven approach showed that traditionally emphasized features — such as cooling technology and gross generation — are of primary importance but can be surpassed by other, often overlooked, features when modeling WW or WC individually. The plant configuration, cooling technology, and gross generation were the most important features of WW, whereas WURM, cooling technology, and reporting month were the most important for WC. These findings can improve thermoelectric power plant management, water use reporting accuracy, and water use modeling.