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USGS2021

Comparison of flea sampling methods and Yersinia pestis detection on prairie dog colonies

Scientists collect fleas (Siphonaptera) to survey for Yersinia pestis , the bacterial agent of plague. When studying fleas parasitizing prairie dogs ( Cynomys spp.), two primary methods are used: (1) combing fleas from live-trapped prairie dogs and (2) swabbing fleas from burrows with cloth swabs attached to metal cables. Ideally, burrow swabbing, the cheaper and easier method, would explain flea burdens on prairie dogs and provide reliable information on plague prevalence. In a linear regression analysis of data from 1-month intervals (June–August 2010–2011) on 13 colonies of black-tailed prairie dogs ( Cynomys ludovicianus , BTPDs) in New Mexico, flea abundance on swabs explained 0–26% of variation in BTPD flea burdens. In an analysis of data (May–August 2016) from six colonies of BTPDs in Montana, flea abundance on swabs explained 2% of variation in BTPD flea burdens. In an analysis of data from a short-term interval (July 23–27, 2019) on four colonies of BTPDs in Montana, flea abundance on swabs explained 0.1% of variation in BTPD flea burdens. In an analysis of data from 1-week intervals (August–October 2000) on four colonies of white-tailed prairie dogs ( Cynomys leucurus , WTPD) in Utah, swabbing data explained 0.1% of variation in WTPD flea burdens. Pools of fleas from two WTPD colonies were tested for Y. pestis by mouse inoculation and isolation; 65% from WTPDs tested positive, whereas 4% from burrows tested positive. Data herein also show that results from burrow swabbing can misrepresent flea species composition and phenology on prairie dogs. Burrow swabbing is useful for some purposes, but limitations should be acknowledged, and accumulated data should be interpreted with caution.

Montana, New Mexico, Utah↗

USGS2006

Towards sustainable management of Louisiana's coastal wetland forests: Problems, constraints, and a new beginning

Over 345,000 ha of forested swamps occur throughout the Mississippi River Deltaic Plain. Natural and anthropogenic changes in hydrology and geomorphology at local and landscape levels have reduced the productivity in many of these coastal wetland forests areas and have caused the complete loss of forest cover in some places. A summary and interpretation of the available science, suggestions for policy change, and a multidisciplinary (multi-responsibility) approach were needed to address these issues [in the context of private land]. In response, the Louisiana Governor's office formed a Coastal Wetland Forest Conservation and Use Science Working Group (SWG) and an associated Advisory Panel to provide the Governor with information and suggestions of strategies for environmental and economic utilization, conservation, and protection of Louisiana's coastal wetland forest ecosystem in the long-term. The process of engaging scientists, resource managers, and other stakeholders in this effort is described, and the recommendations of the SWG are presented relative to forestry practices and the potential for sustainable management of coastal wetland forests.

Louisiana↗

USGS2017

Recommended approaches to the scientific evaluation of ecotoxicological hazards and risks of endocrine-active substances

A SETAC Pellston Workshop ® “Environmental Hazard and Risk Assessment Approaches for Endocrine-Active Substances (EHRA)” was held in February 2016 in Pensacola, Florida, USA. The primary objective of the workshop was to provide advice, based on current scientific understanding, to regulators and policy makers; the aim being to make considered, informed decisions on whether to select an ecotoxicological hazard- or a risk-based approach for regulating a given endocrine-disrupting substance (EDS) under review. The workshop additionally considered recent developments in the identification of EDS. Case studies were undertaken on 6 endocrine-active substances (EAS—not necessarily proven EDS, but substances known to interact directly with the endocrine system) that are representative of a range of perturbations of the endocrine system and considered to be data rich in relevant information at multiple biological levels of organization for 1 or more ecologically relevant taxa. The substances selected were 17α-ethinylestradiol, perchlorate, propiconazole, 17β-trenbolone, tributyltin, and vinclozolin. The 6 case studies were not comprehensive safety evaluations but provided foundations for clarifying key issues and procedures that should be considered when assessing the ecotoxicological hazards and risks of EAS and EDS. The workshop also highlighted areas of scientific uncertainty, and made specific recommendations for research and methods-development to resolve some of the identified issues. The present paper provides broad guidance for scientists in regulatory authorities, industry, and academia on issues likely to arise during the ecotoxicological hazard and risk assessment of EAS and EDS. The primary conclusion of this paper, and of the SETAC Pellston Workshop on which it is based, is that if data on environmental exposure, effects on sensitive species and life-stages, delayed effects, and effects at low concentrations are robust, initiating environmental risk assessment of EDS is scientifically sound and sufficiently reliable and protective of the environment. In the absence of such data, assessment on the basis of hazard is scientifically justified until such time as relevant new information is available.

Integrated Environmental Assessment and Management↗

USGS2015

Risk management of El Chichón and Tacaná Volcanoes: Lessons learned from past volcanic crises: Chapter 8

Before 1985, Mexico lacked civil-protection agencies with a mission to prevent and respond to natural and human-caused disasters; thus, the government was unprepared for the sudden eruption of El Chichón Volcano in March–April 1982, which produced the deadliest volcanic disaster in the country’s recorded history (~2,000 fatalities). With the sobering lessons of El Chichón still fresh, scientists and governmental officials had a higher awareness of possible disastrous outcome when Tacaná Volcano began to exhibit unrest in late 1985. Seismic and geochemical studies were quickly initiated to monitor activity. At the same time, scientists worked actively with officials of the Federal and local agencies to develop the “Plan Operativo” (Operational Plan)—expressly designed to effectively communicate hazards information and reduce confusion and panic among the affected population. Even though the volcano-monitoring data obtained during the Tacaná crisis were limited, when used in conjunction with protocols of the Operational Plan, they proved useful in mitigating risk and easing public anxiety. While comprehensive monitoring is not yet available, both El Chichón and Tacaná volcanoes are currently monitored—seismically and geochemically—within the scientific and economic resources available. Numerous post-eruption studies have generated new insights into the volcanic systems that have been factored into subsequent volcano monitoring and hazards assessments. The State of Chiapas is now much better positioned to deal with any future unrest or eruptive activity at El Chichón or Tacaná, both of which at the moment are quiescent as of 2014. Perhaps more importantly, the protocols first tested in 1986 at Tacaná have served as the basis for the development of risk-management practices for hazards from other active and potentially active volcanoes in Mexico. These practices have been most notably employed since 1994 at Volcán Popocatépetl since a major eruption under unfavorable prevailing winds may constitute a substantial threat to densely populated metropolitan Mexico City. While the 1982 El Chichón disaster was a national tragedy, it greatly accelerated volcanic emergency preparedness and multidisciplinary scientific studies of eruptive processes and products, not only at El Chichón but also at other explosive volcanoes in Mexico and elsewhere in the world.

Chiapas↗

USGS1996

Loess studies in central United States: Evolution of concepts

Few words in the realm of earth science have caused more debate than "loess". It is a common term that was first used as a name of a silt deposit before it was defined in a scientific sense. Because this "loose" deposit is easily distinguished from other more coherent deposits, it was recognized as a matter of practical concern and later became the object of much scientific scrutiny. Loess was first recognized along the Rhine Valley in Germany in the 1830s and was first noted in the United States in 1846 along the lower Mississippi River where it later became the center of attention. The use of the name eventually spread around the world, but its use has not been consistently applied. Over the years some interpretations and stratigraphic correlations have been validated, but others have been hotly contested on conceptual grounds and semantic issues. The concept of loess evolved into a complex issue as loess and loess-like deposits were discovered in different parts of the US. The evolution of concepts in the central US developed in four indefinite stages: the eras of (1) discovery and development of hypotheses, (2) conditional acceptance of the eolian origin of loess, (3) "bandwagon" popularity of loess research, and (4) analytical inquiry on the nature of loess. Toward the end of the first era around 1900, the popular opinion on the meaning of the term loess shifted from a lithological sense of loose silt to a lithogenetic sense of eolian silt. However, the dual use of the term fostered a lingering skepticism during the second era that ended in 1944 with an explosion of interest that lasted for more than a decade. In 1944, R.J. Russell proposed and H.N. Fisk defended a new non-eolian, property-based, concept of loess. The eolian advocates reacted with surprise and enthusiasm. Each side used constrained arguments to show their view of the problem, but did not examine the fundamental problem, which was not in the proofs of their hypothesis, but in the definition of the term. Between 1944 and about 1950, the debates about loess reached a maximum level of complexity. The main semantic problem was submersed in peripheral arguments about physical properties and genetic interpretations. The scholarly treatment of the subject by Fisk and Russell stimulated quality responses from a diversity of earth scientists interested in academic and applied studies, particularly geo-history, pedology, soil mechanics and stratigraphy. The long-lasting popularity of loess studies during the bandwagon era lasted to about 1970. By that time, the analytical and technical interests had attracted the mainstream into the fourth era with a focus beyond the old arguments. Although Fisk and Russell found themselves defending an unpopular theory, they stimulated a scientific interest in the late Quaternary history of the Mississippi Valley that may never be exceeded.

Engineering Geology↗

USGS2021

A socio-ecological imperative for broadening participation in coastal and estuarine research and management

For most of the scientific disciplines associated with coastal and estuarine research, workforce representation does not match the demographics of communities we serve, especially for Black, Hispanic or Latino, and Indigenous peoples. This essay provides an overview of this inequity and identifies how a scientific society can catalyze representational, structural, and interactional diversity to achieve greater inclusion. Needed changes go beyond representational diversity and require an intentional commitment to build capacity through inclusivity and community engagement by supporting anti-racist policies and actions. We want to realize a sense of belonging on the part of scientists in society at large and enable research pursuits through a lens of social justice in service of coastal communities. Minimally, this framework offers an avenue for increased recruitment of individuals from more diverse racial and ethnic identities. More broadly, the mechanisms described here aim to create a culture in scientific societies in which social justice, driven by anti-racist actions, produces systemic change in how members of scientific societies approach, discuss, and address issues of inequity. We have written this essay for members of the coastal and marine science community who are interested in change. We aim to call in new voices, allies, and champions to this work.

Estuaries and Coasts↗

USGS1999

Contaminated-sediment database development and assessment in Boston Harbor

Bottom sediments have been regarded as memory banks for contaminant inputs to urban waterways. Bottom sediments accumulate by the addition of particles that enter the waterway from many sources (U.S. National Research Council, 1989). Sediments include solid contaminants, as well as contaminants from the water column that are adsorbed on organic matter or soil (rock) particles. Sediments from Boston Harbor and adjacent parts of Massachusetts Bay are now among the most intensively studied in the Nation in terms of chemical composition. Scientists at the U.S. Geological Survey (USGS), Woods Hole, Mass., undertook a pioneering effort to assemble a database from all available sources of information on chemicals in sediments of the Boston Harbor study area. The database was created to help understand the distribution of sediment contaminants and their sources, transport, and other processes and to support environmental management. The work involved collaboration with the U.S. Environmental Protection Agency (USEPA), the U.S. Army Corps of Engineers (USACOE), the Massachusetts Water Resources Authority, and other organizations. More than 100,000 sediment measurements from more than 3,000 samples were gleaned from 1,500 references. New approaches were developed to scientifically edit and validate data from heterogeneous sources. The compiled data included inorganic and organic chemical information and physical parameters like sediment texture (grain size), as well as documentation to minimize a userđs need to track down hard-to-find reports.

Massachusetts↗

USGS2002

Mount Mazama and Crater Lake: Growth and destruction of a Cascade volcano

For more than 100 years, scientists have sought to unravel the remarkable story of Crater Lake’s formation. Before Crater Lake came into existence, a cluster of volcanoes dominated the landscape. This cluster, called Mount Mazama (for the Portland, Oregon, climbing club the Mazamas), was destroyed during an enormous explosive eruption 7,700 years ago. So much molten rock was expelled that the summit area collapsed during the eruption to form a large volcanic depression, or caldera. Subsequent smaller eruptions occurred as water began to fill the caldera to eventually form the deepest lake in the United States. Decades of detailed scientific studies of Mount Mazama and new maps of the floor of Crater Lake reveal stunning details of the volcano’s eruptive history and identify potential hazards from future eruptions and earthquakes.

Oregon↗

USGS2022

West Virginia and Landsat

Many may recall “Take Me Home, Country Roads,” made famous by John Denver, leads with the lyric “almost heaven, West Virginia, Blue Ridge Mountains, Shenandoah River.” The descriptors are apt. Nicknamed the “Mountain State,” West Virginia inspires thoughts of coal mining or logging in the Appalachian Mountains and valleys, or the leaping trout in the winding waters of New River Gorge National Park & Reserve . West Virginia is second only to Wyoming in coal production nationwide. Its mines produced more than 67 million tons of coal in 2020. Logging pumped about $3.4 billion into the State’s economy in 2019; tourist spending added another $4.6 billion. Those industries are key for West Virginia, but agriculture and fisheries also play a role in the State's economic fortunes. Peaches and apples are major drivers of food production, as are beef and poultry. Trout, meanwhile, are caught and sold commercially, and are stocked throughout the State's rivers for local and visiting anglers. Scientists, land managers, and others use imagery from the U.S. Geological Survey Landsat satellite program's deep historical archive to better understand and manage West Virginia’s storied forests, fields, mountains, and foothills.

West Virginia↗

USGS1997

Geospatial Technology Strategic Plan 1997-2000

Executive Summary -- Geospatial technology applications have been identified in many U.S. Geological Survey Biological Resources Division (BRD) proposals for grants awarded through internal and partnership programs. Because geospatial data and tools have become more sophisticated, accessible, and easy to use, BRD scientists frequently are using these tools and capabilities to enhance a broad spectrum of research activities. Bruce Babbitt, Secretary of the Interior, has acknowledged--and lauded--the important role of geospatial technology in natural resources management. In his keynote address to more than 5,500 people representing 87 countries at the Environmental Systems Research Institute Annual Conference (May 21, 1996), Secretary Babbitt stated, '. . .GIS [geographic information systems], if properly used, can provide a lot more than sets of data. Used effectively, it can help stakeholders to bring consensus out of conflict. And it can, by providing information, empower the participants to find new solutions to their problems.' This Geospatial Technology Strategic Plan addresses the use and application of geographic information systems, remote sensing, satellite positioning systems, image processing, and telemetry; describes methods of meeting national plans relating to geospatial data development, management, and serving; and provides guidance for sharing expertise and information. Goals are identified along with guidelines that focus on data sharing, training, and technology transfer. To measure success, critical performance indicators are included. The ability of the BRD to use and apply geospatial technology across all disciplines will greatly depend upon its success in transferring the technology to field biologists and researchers. The Geospatial Technology Strategic Planning Development Team coordinated and produced this document in the spirit of this premise. Individual Center and Program managers have the responsibility to implement the Strategic Plan by working within the policy and guidelines stated herein.

Information and Technology Report↗

USGS2009

Interpreting the tectonic evolution of Pacific Rim margins using plate kinematics and slab window volcanism

The possibility that slab windows might form in the wake of ridge subduction was first conceptualized in the late 1970s as earth scientists explored the implications of plate tectonic theory in three dimensions. Acceptance of slab-window occurrence, however, has been a long time coming (McCrory and Wilson, this issue). With records of both current and past slab-window transients now firmly established through detailed studies of volcanism and tectonism tied to kinematic plate reconstructions, we are able to exploit these spatially and temporally restricted events as paleogeographic markers. Slab-window research encompasses and integrates a broad range of disciplines including earthquake seismology, plate kinematics, volcanic geochemistry, lithospheric dynamics, seismic tomography, and structural geology. In this special issue we use this powerful new tool to constrain and interpret the Cenozoic evolution of Pacific Rim subduction margins from Alaska, to Patagonia, to Antarctica, to Tonga.

Tectonophysics↗

USGS2013

Partnering for science: proceedings of the USGS Workshop on Citizen Science

What U.S. Geological Survey (USGS) programs use citizen science? How can projects be best designed while meeting policy requirements? What are the most effective volunteer recruitment methods? What data should be collected to ensure validation and how should data be stored? What standard protocols are most easily used by volunteers? Can data from multiple projects be integrated to support new research or existing science questions? To help answer these and other questions, the USGS Community of Data Integration (CDI) supported the development of the Citizen Science Working Group (CSWG) in August 2011 and funded the working group’s proposal to hold a USGS Citizen Science Workshop in fiscal year 2012. The stated goals for our workshop were: raise awareness of programs and projects in the USGS that incorporate citizen science, create a community of practice for the sharing of knowledge and experiences, provide a forum to discuss the challenges of—and opportunities for—incorporating citizen science into USGS projects, and educate and support scientists and managers whose projects may benefit from public participation in science.To meet these goals, the workshop brought together 50 attendees (see appendix A for participant details) representing the USGS, partners, and external citizen science practitioners from diverse backgrounds (including scientists, managers, project coordinators, and technical developers, for example) to discuss these topics at the Denver Federal Center in Colorado on September 11–12, 2012. Over two and a half days, attendees participated in four major plenary sessions (Citizen Science Policy and Challenges, Engaging the Public in Scientific Research, Data Collection and Management, and Technology and Tools) comprised of 25 invited presentations and followed by structured discussions for each session designed to address both prepared and ad hoc "big questions." A number of important community support and infrastructure needs were identified from the sessions and discussions, and a subteam was formed to draft a strategic vision statement to guide and prioritize future USGS efforts to support the citizen science community. Attendees also brainstormed proposal ideas for the fiscal year 2013 CDI request for proposals: one possible venue to support the execution of the vision.

Open-File Report↗

USGS2015

Machine learning for predicting soil classes in three semi-arid landscapes

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil–landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area.

New Mexico, Utah, Wyoming↗

USGS2015

Summer of Applied Geophysical Experience (SAGE): Training for our future geoscientists

Energy and natural resources are crucial to the sustainability of worldwide economies, security, and overall well-being. However, the future workforce in the energy and natural-resources sector is at risk, and meeting the challenges of this dwindling workforce requires well-educated geoscientists in exploration and applied geophysics and related geoscience and technology disciplines. Programs such as geophysical field courses that are supported by SEG and industry, in partnership with academic institutions and government laboratories, are important approaches to maintaining and enhancing expertise in exploration geophysics. One example of a geophysical field course devoted to educating our future workforce is the Summer of Applied Geophysical Experience (SAGE), a four-week program based in Santa Fe, New Mexico, designed to actively engage students in all phases of applied geophysical research. SAGE is a unique educational experience that combines teaching and research as a partnership among universities, industry, government agencies, and professional societies. SAGE teaches the principles and applications of refraction and reflection seismology, magnetics, gravity, GPS, heat flow, several electromagnetic (EM) methods, and ground-penetrating radar (GPR) in a field-based, hands-on setting. More than 850 students and qualified professionals have attended SAGE, many of whom have gone on to become leaders in academia, industry, and government. SAGE students are exposed to the exciting challenges that face earth scientists today, and they develop skills that are necessary to address the world's growing energy demands. Examples of SAGE research projects include mapping archaeological sites and tectonic structure and investigating water and geothermal resources in the Rio Grande rift.

Leading Edge (Tulsa, OK)↗

USGS2016

Mapping presence and predicting phenological status of invasive buffelgrass in southern Arizona using MODIS, climate and citizen science observation data

The increasing spread and abundance of an invasive perennial grass, buffelgrass ( Pennisetum ciliare ), represents a critical threat to the native vegetation communities of the Sonoran desert in southern Arizona, USA, where buffelgrass eradication is a high priority for resource managers. Herbicidal treatment of buffelgrass is most effective when the vegetation is actively growing, but the remoteness of infestations and the erratic timing and length of the species’ growth periods confound effective treatment. The goal of our research is to promote buffelgrass management by using remote sensing data to detect where the invasive plants are located and when they are photosynthetically active. We integrated citizen scientist observations of buffelgrass phenology in the Tucson, Arizona area with PRISM precipitation data, eight-day composites of 250-m Moderate-resolution Imaging Spectroradiometer (MODIS) satellite imagery, and aerially-mapped polygons of buffelgrass presence to understand dynamics and relationships between precipitation and the timing and amount of buffelgrass greenness from 2011 to 2013. Our results show that buffelgrass responds quickly to antecedent rainfall: in pixels containing buffelgrass, higher correlations (R 2 > 0.5) typically occur after two cumulative eight-day periods of rain, whereas in pixels dominated by native vegetation, four prior 8-day periods are required to reach that threshold. Using the new suite of phenometrics introduced here—Climate Landscape Response metrics—we accurately predicted the location of 49% to 55% of buffelgrass patches in Saguaro National Park. These metrics and the suggested guidelines for their use can be employed by resource managers to treat buffelgrass during optimal time periods.

Arizona↗

USGS2024

Special topic—Unoccupied aircraft systems

Introduction Unoccupied aircraft systems (UAS) increasingly support volcano monitoring and eruption response activities in the United States and abroad (James and others, 2020). Advances in UAS platforms and miniaturization of sensors over the past decade have expanded the use of this technology for a wide range of applications within volcanology (Jordan, 2019; James and others, 2020). UAS can greatly enhance existing ground-, aerial-, and satellite-based observation and in situ monitoring networks at volcanoes by providing new avenues for data collection in terms of access, resolution, and timing. UAS can collect data in difficult and hazardous environments, reducing risk to occupied aircraft and (or) ground crews; support the generation of dense time series of data through frequent, low-cost, high-resolution surveys; and provide real-time, on-demand measurements at volcanic systems for indicators such as gas, thermal output, and topographic change without the need to wait for contracted aerial flight services or satellite orbit intervals. During the 2018 response to the Kīlauea eruption on the Island of Hawaiʻi, UAS were used extensively and successfully to monitor, track, investigate, and (or) warn of ongoing volcanic activity (fig. L1; Neal and others, 2019). Throughout the eruption, the UAS team was able to provide data products rapidly to emergency managers for situational awareness and to scientists for quantitative hazard assessment (Diefenbach and others, 2018). Over the course of 4 months, more than 1,200 UAS missions were flown and yielded critical data that included (1) live video to emergency operations centers in Hilo and Honolulu for situational awareness; (2) gas emission rates, compositions, and concentrations; (3) repeat nadir videos over sections of the lava channel to support measurements of lava effusion rate; (4) oblique videos for hazards assessment and outreach; and (5) photogrammetry surveys to create very high-resolution topographic models and orthophoto mosaics (Diefenbach and others, 2018). In coming years, the U.S. Geological Survey (USGS) Volcano Hazards Program (VHP) plans to expand its fleet of UAS, associated sensors, and remote pilots to enhance volcano monitoring and response capabilities. Currently (2023), USGS operational capabilities are restricted to small class UAS (sUAS; less than [<] 55 pounds) that are limited in range, payload capacity, and flight duration. Additionally, USGS-piloted platforms are restricted to the U.S. Department of the Interior Office of Aviation Services approved fleet, which includes a limited number of small and medium multi-rotor aircraft and vertical take-off and landing fixed-wing aircraft ( https://www.doi.gov/aviation/uas/fleet ). Each type of platform has advantages and disadvantages. Small rotor-wing quadcopters are fast to deploy, can be carried in a backpack, and are highly maneuverable, but are typically only equipped with a small camera and have a minimal flight range. Medium rotor-wing hexacopters can carry larger payloads (< 20 kilograms [kg]) and varied sensors, but, with the drawback of minimal flight time (<30 minutes), they typically have similar range capabilities to their smaller counterparts and are not as easily deployable. Fixed-wing platforms provide relatively long endurance (<60 minutes) and range and, with the vertical take-off and landing capabilities, can launch and land in relatively small spaces; however, they have less maneuverability and hovering capability than the rotor-wing platforms. Although the 2018 Kīlauea response showed the benefit of the current UAS fleet, all platforms have limited range [<10 kilometers (km)], such that operators must be stationed relatively close to the region of interest. To expand UAS monitoring capabilities, VHP staff have been working closely with industry partners and the National Aeronautics and Space Administration to develop a next-generation UAS for volcano monitoring (Kern and others, 2020). This ruggedized, mid-range (>20 km), multiparametric (gas and photogrammetry) UAS has been developed to meet volcano monitoring needs, particularly at less accessible, more dangerous stratovolcanoes. It is expected in the coming years that additional UAS platforms with new and smaller sensors will expand our capabilities to meet the Nation’s volcano monitoring objectives.

Scientific Investigations Report↗

USGS2025

Toward co-designed Earth System Models: Reflecting end-user priorities in local applications from a modeler's perspective

Earth System Models (ESM) are crucial for quantifying climate impacts across Earth's interconnected systems and supporting science-based adaptation and mitigation. However, not including end-users, especially decision-makers representing communities vulnerable to climate change, can limit model utility, increase epistemic risks, and lead to information misuse in decision-making. While the ESM community increasingly values broad community engagement, end-users may not initially perceive models as useful for local planning. Co-designing models with end-users fosters two-way learning: users better understand models and their outputs, while modelers gain insights into fine-scale local processes like monitoring practices and management priorities. Higher-level co-design can lead to more customized, priority-driven, and useful modeling products. Despite these benefits, modelers often struggle to initiate meaningful partnerships with local communities. Therefore, this paper explores model co-design from the perspective of modelers. This study presents two case studies where modelers and social scientists collaborated with Indigenous communities' decision-makers to reflect their priorities in model design and application. In the Arctic Rivers Project, high-resolution climate and hydrology data sets for Alaska were developed with guidance from an Indigenous Advisory Council, using optimized, coupled land-atmosphere models. In the Mid-Klamath Project, we partnered with the Karuk Tribe's Department of Natural Resources to assess climate change and prescribed burning impacts on terrestrial hydrology in the Klamath River Basin. Drawing from these studies, we introduce a four-level framework: (a) Co-design Configuration; (b) Model Tuning; (c) Incorporate Contextual Knowledge; (d) Co-develop New Model Functions. We aim to help researchers consider and compare co-design across diverse modeling projects systematically and coherently.

Alaska, California, Oregon↗

USGS2025

Leveraging artificial intelligence and machine learning to advance Chesapeake Bay research and management: A review of status, challenges, and opportunities

The Chesapeake Bay and its watershed (hereafter “Chesapeake Bay region”) have been the focus of extensive restoration efforts for several decades. These restoration efforts are guided by the Chesapeake Bay Watershed Agreement (Chesapeake Executive Council 2014) which outlines 10 goals and 31 measurable outcomes. The Chesapeake Bay is globally recognized as a model for coastal restoration due to long-term investments in monitoring, modeling, implementation and research by the Chesapeake Bay Program (CBP) partnership. These monitoring network spans tidal and non-tidal regions and provides data across multiple scales. Artificial intelligence (AI), particularly machine-learning (ML) and deep learning (DL), has emerged as a powerful tool for analyzing large, complex datasets. These techniques have gained widespread adoption across various disciplines, including ecology, hydrology, and environmental science. In the Bay context, AI/ML is increasingly being used to explore drivers of environmental change, analyze system dynamics, and predict conditions in areas with limited monitoring. The CBP partnership, particularly its Scientific and Technical Advisory Committee (STAC), has increasingly recognized the growing role of AI/ML in watershed and estuarine management. Recent Chesapeake Community Research Symposium sessions and initiatives such as the Chesapeake Global Collaboratory highlight increasing regional momentum to apply big data and AI/ML for environmental solutions. Together, these developments underscore the timely need to explore how AI/ML can help advance Chesapeake Bay restoration and management. This STAC workshop, titled “Leveraging Artificial Intelligence and Machine learning to Advance Chesapeake Bay Research and Management: A review of status, challenges, and opportunities,” was held from February 24-25, 2025, in Edgewater, Maryland to bring together over 50 federal, state, and academic scientists and partners to synthesize the current state of AI/ML applications and identify research gaps in Chesapeake Bay research and management. The workshop focused on three main objectives: 1. Summarize recent AI/ML applications and lessons learned in both tidal and nontidal areas of the Chesapeake Bay region. 2. Identify challenges and gaps in applying AI/ML approaches to Chesapeake Bay data. Such challenges and gaps may include data limitations, harmonization issues, ineffective communication of AI/ML insights, and a lack of coordination among research and management institutions. 3. Develop recommendations and identify opportunities for leveraging AI/ML to address issues across the Chesapeake Bay region. Key areas of focus may include generating new information to support watershed management, delivering AI/MLgenerated insights to managers in a clear and actionable way, and fostering greater collaboration among stakeholders within the CBP Partnership. Workshop participants engaged in science presentations and breakout sessions to develop recommendations for advancing the integration of AI/ML techniques into research and management across the Chesapeake Bay region. By synthesizing current applications, identifying challenges, and exploring new opportunities, the workshop has provided valuable insights and recommendations for better leveraging AI/ML approaches to support the success of Bay restoration efforts. Together, these recommendations provide a roadmap for enhancing data-driven, science-based decision making aligned with the goals and outcomes of the Chesapeake Bay Watershed Agreement.

Delaware, Maryland, Virginia↗