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Regional monitoring programs in the United States: Synthesis of four case studies from Pacific, Atlantic, and Gulf Coasts

Water quality monitoring is a cornerstone of environmental protection and ambient monitoring provides managers with the critical data they need to take informed action. Unlike site-specific monitoring that is at the heart of regulatory permit compliance, regional monitoring can provide an integrated, holistic view of the environment, allowing managers to obtain a more complete picture of natural variability and cumulative impacts, and more effectively prioritize management actions. By reviewing four long-standing regional monitoring programs that cover portions of all three coasts in the United States – Chesapeake Bay, Tampa Bay, Southern California Bight, and San Francisco Bay – important insights can be gleaned about the benefits that regional monitoring provides to managers. These insights include the underlying reasons that make regional monitoring programs successful, the challenges to maintain relevance and viability in the face of ever-changing technology, competing demands and shifting management priorities. The lessons learned can help other managers achieve similar successes as they seek to establish and reinvigorate their own monitoring programs.

California, Delaware, Florida, Maryland, New Jerse↗

Achievements and prospects of global broadband seismographic networks after 30 years of continuous geophysical observations

Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid- to late-twentieth century saw the creation of the World-Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced global geographic coverage as seismometer bandwidth increased greatly allowing for the recording of the Earth's principal seismic spectrum. The modern era of global observations and rapid data access began during the 1980s, and notably included the inception of the GEOSCOPE initiative (1982) and GSN (1988). Through continual improvements, GEOSCOPE and the GSN have realized near-real time recording of ground motion with state-of-art data quality, dynamic range, and timing precision to encompass 180 seismic stations, many in very remote locations. Data from GSNs are increasingly integrated with other geophysical data (e.g., space geodesy, infrasound and Interferometric Synthetic Aperture Radar). Globally distributed seismic data are critical to resolving crust, mantle, and core structure; illuminating features of the plate tectonic and mantle convection system; rapid characterization of earthquakes; identification of potential tsunamis; global nuclear test verification; and provide sensitive proxies for environmental changes. As the global geosciences community continues to advance our understanding of Earth structure and processes controlling elastic wave propagation, GSN infrastructure offers a springboard to realize increasingly multi-instrument geophysical observatories. Here, we review the historical, scientific, and monitoring heritage of GSNs, summarize key discoveries, and discuss future associated opportunities for Earth Science.

Reviews of Geophysics↗

Observing systems, modeling, and forecasting

Predicting harmful algal blooms (HABs) requires integrating physical, chemical, and biological data collected from observing networks and then assimilating these data into models, which are used to generate forecasts. In 2005, the Harmful Algal Research and Response: A National Environmental Science Strategy 2005-2015 (HARRNESS, 2005) made recommendations on how to improve HAB modeling and forecasting over the next decade. Key HARRNESS recommendations related to sensing, networking, and modeling HABs included: ● Support the development and validation of new and improved technologies for remote cell and toxin detection, and for modeling and forecasting, ● Improve coordination of monitoring/ and modeling efforts, both at national and regional levels, ● Improve the use of networking technologies for monitoring and modeling efforts, ● Conduct sustained time series measurements of the biotic, chemical, and physical environments impacted by HABs, ● Develop food web models on the ecosystem fate and effects of toxins, ● Develop and improve species-specific models that link to physical-biological models. Here we review HAB observing, modeling, and forecasting advances and technologies and recommend research and management priorities for the next decade and beyond. Our report encompasses sensing technologies, sensor networking and data management, models and forecasts, and the paths to operationalize forecasts. Continued improvements of deployable sensors are foundational to improving early warning indicators, models, and forecasts, which are only as good as the underlying data. Sensing technology has advanced considerably in the last decade; for example, more capable fluorometric pigment sensors can track changes in bloom biomass in real-time. Additionally, automated imaging/classifying systems to identify and quantify key harmful algal (HA) taxa are being routinely deployed. However, deployable toxin sensors are available for only some HAB toxins and continue to be identified as a critical need by researchers and managers. As more and improved sensors and technologies become available, the data quality associated with each sensor needs to be assessed. Data quality encompasses the reliability, accuracy, and uncertainty associated with sensor-generated data. These data need to be of known quality so that researchers, managers, and end-users can reliably determine if the information is appropriate for their intended applications. Many of the data quality recommendations from HARRNESS (2005) are still relevant and have been reiterated within the management community. Understanding and documenting data quality, and when applicable, standardizing best practices for sensor use, continue to be recommended.

Report↗

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↗

Fifteen years of WRTDS for advancing water-quality science: A critical review of methodological developments and global applications

Contamination by nutrients, major ions, and metals poses a major threat to global water sustainability. Understanding how these pollutants vary across time and space requires long-term monitoring and robust statistical approaches. Traditional methods, however, often struggle to account for streamflow variability, seasonality, and nonlinear responses. Introduced in 2010, the Weighted Regressions on Time, Discharge, and Season (WRTDS) method offers a flexible, data-driven framework that generates both observed and flow-normalized estimates of concentration and load. Over the past 15 years, WRTDS has become a state-of-the-art tool for water-quality science and management, with applications spanning a wide range of hydrologic, climatic, and policy contexts─including major watersheds across North America, Europe, Asia, Australia, and the Arctic. In this review of WRTDS, we document the method’s major advancements, examine its expanding geographic and thematic applications, and summarize its relevance to water-quality management programs and policies worldwide. We also discuss its performance relative to other regression and machine-learning approaches. Finally, we identify key priorities for future development to support the continued evolution of WRTDS as a trusted and practical tool for scientists and managers working to protect and sustain water resources.

Environmental Science and Technology↗

Coral disease and health workshop: Coral histopathology II, July 12-14, 2005

The health and continued existence of coral reef ecosystems are threatened by an increasing array of environmental and anthropogenic impacts. Coral disease is one of the prominent causes of increased mortality among reefs globally, particularly in the Caribbean. Although over 40 different coral diseases and syndromes have been reported worldwide, only a few etiological agents have been confirmed; most pathogens remain unknown and the dynamics of disease transmission, pathogenicity and mortality are not understood. Causal relationships have been documented for only a few of the coral diseases, while new syndromes continue to emerge. Extensive field observations by coral biologists have provided substantial documentation of a plethora of new pathologies, but our understanding, however, has been limited to descriptions of gross lesions with names reflecting these observations (e.g., black band, white band, dark spot). To determine etiology, we must equip coral diseases scientists with basic biomedical knowledge and specialized training in areas such as histology, cell biology and pathology. Only through combining descriptive science with mechanistic science and employing the synthesis epizootiology provides will we be able to gain insight into causation and become equipped to handle the pending crisis. One of the critical challenges faced by coral disease researchers is to establish a framework to systematically study coral pathologies drawing from the field of diagnostic medicine and pathology and using generally accepted nomenclature. This process began in April 2004, with a workshop titled Coral Disease and Health Workshop: Developing Diagnostic Criteria co-convened by the Coral Disease and Health Consortium (CDHC), a working group organized under the auspices of the U.S. Coral Reef Task Force, and the International Registry for Coral Pathology (IRCP). The workshop was hosted by the U.S. Geological Survey, National Wildlife Health Center (NWHC) in Madison, Wisconsin and was focused on gross morphology and disease signs observed in the field. A resounding recommendation from the histopathologists participating in the workshop was the urgent need to develop diagnostic criteria that are suitable to move from gross observations to morphological diagnoses based on evaluation of microscopic anatomy. As a continuation of building the foundation and framework for coral disease diagnostics, the CDHC convened the Coral Disease and Health Workshop: Coral Histopathology II in Charleston, South Carolina, July 11-14, 2005. The workshop was hosted by the Department of Pathology and Laboratory Medicine at the Medical University of South Carolina, Charleston, SC which provided expertise, facilities and equipment in support of the workshop. All of the histological slides and related photographs used in the discussions were prepared and supplied by the IRCP. This workshop brought together 15 experts in veterinary and medical pathology and coral biology from national and international research institutes and government laboratories. The mission was to devise a standardized approach to examining microscopic anatomy and pathology of corals and a standardized nomenclature to facilitate accurate descriptions of the microscopic morphology of corals and enhance communication among specialists investigating causes of coral death. 2 The participants of this workshop deliberated for 3 days to refine the nomenclature for gross and microscopic anatomy of corals and systematically described microscopic changes associated with selected coral diseases. The findings and recommendations from the deliberations will be submitted to the research community for peer review. The standardized nomenclature and descriptions produced at this workshop will ultimately be made available to the scientific community through a variety of media including the World Wide Web. An exciting highlight of this meeting was provided by Professor Robert Ogilvie (MUSC Department of Cell Biology and Anatomy) when he introduced participants to a new digital technology that is revolutionizing histology and histopathology in the medical field. The Virtual Slide technology creates digital images of histological tissue sections by computer scanning actual slides in high definition and storing the images for retrieval and viewing. Virtual slides now allow any investigator with access to a computer and the web to view, search, annotate and comment on the same tissue sections in real time. Medical and veterinary slide libraries across the country are being converted into virtual slides to enhance biomedical education, research and diagnosis. The coral health and disease researchers at this workshop deem virtual slides as a significant way to increase capabilities in coral histology and a means for pathology consultations on coral disease cases on a global scale.

NOAA Technical Memorandum↗