Geology topics

Julian A. Scott

Publications and source records attributed to Julian A. Scott.

3 recordsLinked to original sources

USGS2026

Colocating artificial intelligence data centers with energy infrastructure on Federal public lands—A science synthesis and spatial analysis to inform decision making

Executive Summary Artificial intelligence (AI) is rapidly transforming industries and economies, creating an urgent need to strategically plan for the energy and infrastructure required to support increasing AI use. U.S. Federal agencies and bureaus have been directed to explore ways to accelerate permitting, development, and deployment of energy resources and AI technologies, including encouraging the colocation of energy infrastructure and data centers. To inform these initiatives, this report synthesizes relevant scientific information and presents a spatial analysis of existing energy infrastructure and data centers on or near U.S. Federal public lands managed by the Bureau of Land Management (BLM). The purpose of this science synthesis and spatial analysis is to provide the BLM with foundational information for considering potential colocation of data centers with energy infrastructure on Federal public lands to support evidence-based decisions. Additionally, this report provides insight into current (2025) and potential future energy demands by providing projections of a range of potential future environmental conditions relevant to maintaining industry-recommended cooling temperature standards necessary for efficient data center operations. As a part of this effort, a rapid response literature review was conducted of the best available science on the topic of data center development and energy infrastructure in July–August 2025, supplemented by additional resources recommended by U.S. Federal agency and bureau subject matter experts (hereafter experts; including the U.S. Department of Energy National Laboratory of the Rockies) and peer reviewers. To better understand current conditions relevant to AI data center development, a spatial analysis was conducted across Alaska and 11 States in the Western United States, Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming, all of which contain extensive BLM-managed surface lands (hereafter referred to as “BLM lands”) that could be considered for the colocation of energy infrastructure and AI data centers. This effort identified BLM lands within 10 miles of existing transmission lines, consistent with methods used in previous BLM programmatic environmental impact statements. This report describes the types of data centers operating within the United States, which vary in ownership, size, technology, and proximity to end users. This report then outlines the primary considerations of data center development, including reliable energy supply, natural resources (such as water availability to support cooling requirements), and relevant policy and regulatory considerations. Energy supply considerations are pivotal for data center operation. Between 2014 and 2018, data centers in the United States accounted for nearly 2 percent of the Nation’s total electricity consumption, and data center energy consumption is projected to increase from 2 to 6.7–12 percent of total U.S. electricity use by 2028. These energy requirements necessitate careful consideration of energy supply when considering potentially suitable locations for data center development. Experts anticipate that an increase in renewable energy generation will likely support most potential future power demand needs, including for data centers, followed by increases in natural gas, nuclear, and geothermal energy production. Additional capacity in the form of battery storage will likely not generate electricity, but may improve the reliability and flexibility of supply, helping to ensure that growing data center loads can be met. However, the U.S. Department of Energy estimates that the United States will need, on average, 57 percent more energy transmission infrastructure by 2035 to account for the growing power demand introduced by development such as data centers. Cooling server equipment in data centers requires large amounts of electricity and water, and this demand can be exacerbated by hot and humid conditions. Energy efficient water-based cooling technologies may reduce electricity consumption onsite but require more water consumption. This additional water demand has the potential to increase water stress and competition with other users. As such, developing data centers will likely need a thorough assessment of current and potential future water availability, as well as consideration of how water demand may change across other sectors. Data center development involves policy and regulatory considerations, as projects must undergo environmental review and authorization processes that can take 18–24 months. Coordinating these environmental reviews and authorizations with other energy development projects, such as building new transmission lines, may cause additional delays. Recent efforts by the U.S. Department of Energy and U.S. Department of the Interior aim to expedite environmental reviews and authorizations and improve coordination across agencies. The spatial analysis identified 771 existing AI data centers and more than 3,300 power plants. The spatial analysis found that 6 percent of AI data centers and 22 percent of power plants in the Western United States were on or within 1 mile of BLM lands, and California had the largest number of facilities. Most existing AI data centers were near high-voltage transmission lines and close to power plants, supporting efficient energy delivery. More than 90,000,000 acres of BLM lands were within 10 miles of existing high-voltage transmission lines, representing 38 percent of BLM lands in the study area. Available transmission infrastructure and the overlap with BLM lands varied by State, and Alaska had limited overlap compared to the rest of the Western United States. To operate most efficiently, data center temperatures must be at or below 80.6 degrees Fahrenheit. This analysis of future temperature and precipitation projections indicated increasing cooling demands for data centers, particularly in Arizona, California, and Nevada, where rising temperatures are expected to increase energy and operational costs while potentially stressing current regional electrical grid infrastructure. This report highlights relevant energy supply, natural resources, and regulatory considerations for data center development on BLM lands. This report does not provide a comprehensive ecological, regulatory, land suitability, or permitting analysis. The factors described here are contextual considerations only and are not intended to identify, rank, quantify, or recommend optimal areas for data center colocation. This spatial analysis focused solely on energy considerations relevant to data centers and did not consider water availability, critical habitats, BLM National Conservation Lands, areas of cultural or historical significance, and other sensitive resources. These topics are recognized as critical but were not within the scope of this science synthesis and spatial analysis.

Scientific Investigations Report↗

USGS2016

The origin of shallow lakes in the Khorezm Province, Uzbekistan, and the history of pesticide use around these lakes

The economy of the Khorezm Province in Uzbekistan relies on the large-scale agricultural production of cotton. To sustain their staple crop, water from the Amu Darya is diverted for irrigation through canal systems constructed during the early to mid-twentieth century when this region was part of the Soviet Union. These diversions severely reduce river flow to the Aral Sea. The Province has >400 small shallow (<3 m deep) lakes that may have originated because of this intensive irrigation. Sediment cores were collected from 12 lakes to elucidate their origin because this knowledge is critical to understanding water use in Khorezm. Core chronological data indicate that the majority of the lakes investigated are less than 150 years old, which supports a recent origin of the lakes. The thickness of lacustrine sediments in the cores analyzed ranged from 20 to 60 cm in all but two of the lakes, indicating a relatively slow sedimentation rate and a relatively short-term history for the lakes. Hydrologic changes in the lakes are evident from loss on ignition and pollen analyses of a subset of the lake cores. The data indicate that the lakes have transitioned from a dry, saline, arid landscape during pre-lake conditions (low organic carbon content) and low pollen concentrations (in the basal sediments) to the current freshwater lakes (high organic content), with abundant freshwater pollen taxa over the last 50–70 years. Sediments at the base of the cores contain pollen taxa dominated by Chenopodiaceae and Tamarix , indicating that the vegetation growing nearby was tolerant to arid saline conditions. The near surface sediments of the cores are dominated by Typha/Sparganium , which indicate freshwater conditions. Increases in pollen of weeds and crop plants indicate an intensification of agricultural activities since the 1950s in the watersheds of the lakes analyzed. Pesticide profiles of DDT (dichlorodiphenyltrichloroethane) and its degradates and γ-HCH (gamma-hexachlorocyclohexane), which were used during the Soviet era, show peak concentrations in the top 10 cm of some of the cores, where estimated ages of the sediments (1950–1990) are associated with peak pesticide use during the Soviet era. These data indicate that the lakes are relatively young (mostly <150 years old) and that without irrigation and canal inputs from the Amu Darya, the lakes would not exist as freshwater lakes.

Khorezm Province↗

USGS2015

Alternative standardization approaches to improving streamflow reconstructions with ring-width indices of riparian trees

Old, multi-aged populations of riparian trees provide an opportunity to improve reconstructions of streamflow. Here, ring widths of 394 plains cottonwood (Populus deltoids, ssp. monilifera) trees in the North Unit of Theodore Roosevelt National Park, North Dakota, are used to reconstruct streamflow along the Little Missouri River (LMR), North Dakota, US. Different versions of the cottonwood chronology are developed by (1) age-curve standardization (ACS), using age-stratified samples and a single estimated curve of ring width against estimated ring age, and (2) time-curve standardization (TCS), using a subset of longer ring-width series individually detrended with cubic smoothing splines of width against year. The cottonwood chronologies are combined with the first principal component of four upland conifer chronologies developed by conventional methods to investigate the possible value of riparian tree-ring chronologies for streamflow reconstruction of the LMR. Regression modeling indicates that the statistical signal for flow is stronger in the riparian cottonwood than in the upland chronologies. The flow signal from cottonwood complements rather than repeats the signal from upland conifers and is especially strong in young trees (e.g. 5–35 years). Reconstructions using a combination of cottonwoods and upland conifers are found to explain more than 50% of the variance of LMR flow over a 1935–1990 calibration period and to yield reconstruction of flow to 1658. The low-frequency component of reconstructed flow is sensitive to the choice of standardization method for the cottonwood. In contrast to the TCS version, the ACS reconstruction features persistent low flows in the 19th century. Results demonstrate the value to streamflow reconstruction of riparian cottonwood and suggest that more studies are needed to exploit the low-frequency streamflow signal in densely sampled age-stratified stands of riparian trees.

Montana, North Dakota, Soutb Dakota, Wyoming↗