USGS Science⌕ Search

SEARCH · USGS Science

Results for “Gas Science and Engineering”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

1,692 records · Page 31Linked to original sources
USGS2026

A roadmap for identifying and interpreting physical processes and national water model prediction bias associated with baseflow index regimes across the contiguous United States

Understanding how groundwater–surface water interactions shape streamflow variability is critical for diagnosing low flow behavior and prediction bias in continental scale hydrologic models. We present a process informed framework that links observed baseflow (BF) dynamics, watershed attributes, and National Water Model (NWM) performance across the contiguous United States. Using daily observed streamflow from 797 reference quality streamgages, we developed monthly baseflow index (BFI) signatures using a streamgage specific, calibrated digital filter. Hierarchical clustering of these signatures identified seven distinct BFI regimes capturing regional and seasonal variability. We evaluated NWM v3.0 retrospective streamflow performance within each regime using multiple hydrograph and flow duration curve-based metrics. Model skill varied systematically across regimes: mixed flow systems were simulated most accurately, while predominantly BF dominated and quickflow dominated regimes exhibited substantially poorer performance. Across nearly all regimes, the NWM underestimated observed BFI magnitude and frequently failed to reproduce seasonal BF patterns, indicating systematic biases in simulated low flow contributions. To relate these regimes to potential process controls, we trained a Random Forest classifier using static watershed attributes and applied Shapley Additive Explanations to identify features most strongly associated with each regime. Results highlight regionally varying influences, including the dominant role of snow fraction and seasonal runoff timing in snow dominated basins and the importance of evapotranspiration and aridity in quickflow dominated systems. Collectively, these findings demonstrate how hydrologic signatures combined with interpretable machine learning can diagnose regime specific model biases and generate process-based hypotheses about limitations in large scale hydrologic prediction systems.

contiguous United States↗
USGS2025

Updating and recalibrating the integrated Santa Rosa Plain Hydrologic Model to assess stream depletion and to simulate future climate and management scenarios in Santa Rosa, Sonoma County, California

The Santa Rosa Plain Hydrologic Model (SRPHM) was developed and published in 2014 through a collaboration between the U.S. Geological Survey (USGS) and Sonoma Water to analyze the hydrologic system in the Santa Rosa Plain watershed, help meet the increasing demand for fresh water, and prepare for future uncertainties in water resources. The original model simulated hydrological conditions and water use from water years 1975 to 2010. Recently (2023), the USGS, in cooperation with Sonoma Water and the California State Water Resources Control Board, updated the SRPHM model to extend its simulation period to the end of the 2018 calendar year, incorporate new estimates of rural and agricultural water use, and use efficient input format for climate variables. The updated model was recalibrated, and evaluation of the new model calibration is included in this report. This report presents the results of comparing the hydraulic heads, streamflow, and groundwater budget simulated by the updated model with those generated by the original model and observed data. The main difference in the simulated budget between the original and updated SRPHM is the estimates of agricultural pumping, rural domestic pumping, and return flow generated from rural water use that was not simulated in the original model. The revised agricultural pumping is simulated using the agricultural package, which constrains pumping to available groundwater. The use of the agricultural package leads to a more realistic estimation of agricultural water use, with revised agricultural pumping being one-third less than that in the original model. The revised rural pumping is about half of the pumping in the original model because of using detailed parcel data to estimate population density in rural areas instead of coarse census tracts. Overall, average total inflows for water years 2006–10 simulated by the updated model were about 2 percent less than the original model, and the average total updated outflows were nearly 5 percent less than the original model. The updated model was then used to generate stream depletion maps, simulate climate change scenarios during 2019–99, and simulate water rights allocation using the Model for Decision Support in Integrated River Basin Management (MODSIM). The results from simulating eight future climate scenarios indicated either an increase in groundwater storage or no significant change in the next 80 years, along with an increase in recharge, an increase in actual evapotranspiration in six out of eight climate projections, and an increase in surface runoff. The increases in the simulated future groundwater storage, recharge, evapotranspiration, and runoff in most climate projections are mainly driven by the projected increase in precipitation in most of the future climate scenarios. The updated model also was used to test a pilot case study demonstrating water-resource allocation among different users with different water rights using the integrated MODSIM-Groundwater and Surface-Water Flow Model (GSFLOW) platform. The updated SRPHM serves as a valuable tool for analyzing historical and future hydrologic conditions in the Santa Rosa Plain watershed and preparing for future uncertainties.

California↗
USGS2024

Stream nitrate dynamics driven primarily by discharge and watershed physical and soil characteristics at intensively monitored sites: Insights from deep learning

We developed a suite of models using deep learning to make hindcast predictions of the 7‐day average backward‐looking nitrate concentration at 46 predominantly agricultural sites across the midwestern and eastern United States. The models used daily observations of discharge and meteorological variables and watershed attributes describing anthropogenic modification to hydrology, nitrogen application, climate, groundwater, land use, watershed physiographic attributes, and soils. Across all sites, discharge and watershed soil and physiographic attributes showed a strong influence on model performance. Analysis of drivers across sites revealed considerable regional differences related to controlling processes such as groundwater contributions. We tested several ways to pool data across sites to develop accurate models and make the most effective use of available data. Single‐site models, in which models are trained and tested at a single location, showed generally strong predictive performance (median Kling‐Gupta Efficiency = 0.66), and accuracy at poorly performing sites could be improved by grouping sites with similar characteristics. Developing a single model for all sites reduced performance at several locations with distinct characteristics, suggesting that there is a threshold of dissimilarity beyond which more data does not improve the model. While many deep learning studies have shown that national or even global models can outperform local models, it is not clear that this is true for water quality constituents. This study demonstrates how data can be combined effectively, using deep learning to develop accurate and interpretable models of instream nitrate at sites where varying processes are responsible for changes in nitrate concentration.

Water Resources Research↗
USGS2024

Afterslip and creep in the rate-dependent framework: Joint inversion of borehole strain and GNSS displacements for the Mw 7.1 Ridgecrest earthquake

The elusive transition toward afterslip following an earthquake is challenging to capture with typical data resolution limits. A dense geodetic network recorded the Mw 7.1 Ridgecrest earthquake, including 16 Global Navigation Satellite System (GNSS) stations and 3 borehole strainmeters (BSM). The sub-nanostrain precision and sub-second sampling rate of BSMs bridges a gap between conventional seismologic and geodetic methods, exemplified by atypical postseismic shear strain reversals observed at nearfield (<2 km) station B921 that remain unexplained. We jointly invert GNSS displacements and BSM strains for coseismic and postseismic slip spanning hours to months over 7 independent periods. Cosiesmically, our model resolves the largest slip magnitudes of up to 6.6 m on the mainshock rupture plane, with similar patterns to other inferred slip distributions. The foreshock fault appears to slip coincidently with mainshock, revealing potential asperities activated during the preceding Mw 6.4 event. Postseismically, the best-fitting models adhere to mechanical rate-and-state expectations of logarithmically decaying slip adjacent to the coseismic rupture terminus, and where deep rheologic conditions favor creep. Most spatial variation occurs in the early postseismic timeframe (<1–2 weeks), with evidence for regional rheologic control and static stress dependence. Triggered creep on the neighboring Garlock Fault unexpectedly persists for >178 days—further highlighting the importance of fault networks in postseismic stress redistribution, critical to assessing future hazard.

Journal of Geophysics Research↗
USGS2025

Tracing metal sources and groundwater flow paths in the Upper Animas River watershed using rare earth elements and stable isotopes

Groundwater flow paths and processes that govern metal mobility and transport are difficult to characterize in mountainous bedrock watersheds. Despite the difficulty in holistic characterization, conceptual understanding of subsurface hydrologic and geochemical processes is key to developing remediation plans for locations affected by acid mine drainage, such as the Upper Animas River watershed in southwestern Colorado, USA. Stable isotopes of water and rare earth elements were utilized to evaluate groundwater flow and metal sources within this complex catchment. Stable isotope samples collected from draining mine adits and springs display systematic spatial variation wherein sample sites at higher elevations have greater seasonal variability than sites at lower elevations. The Upper Cement Creek watershed, where multiple draining mines are present, displays the lowest seasonal variation in stable isotopic signatures, potentially indicating the presence of a large, well-mixed volume of groundwater storage or interbasin groundwater flow. Rare earth elements display statistically significant variation between different alteration styles in the catchment. Overprinting of regional propylitic alteration is evident based on enrichment of middle rare earth elements in acidic springs and mines that are not spatially associated with surficial exposures of acid generating alteration styles. Europium anomaly and middle rare earth enrichment signatures from two flooded mine tunnels on opposite sides of a watershed divide indicate connections to the same subsurface flooded mine workings.

Colorado↗
USGS2026

High-resolution transboundary vegetation community maps of the Sonoran and Mojave Desert ecoregion to support critical landscape conservation planning and habitat management needs

We produced a 30-m resolution binational land cover map of Bird Conservation Region 33 (BCR 33) for the U.S. North American Bird Conservation Initiative. The region covers large portions of the Sonoran and Mojave Deserts. The map can support the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program’s recovery planning efforts and constitutes the first known binational land cover dataset spanning sections of the United States–Mexico border and using a consistent classification system for both countries. The mapped region includes 152 distinct land cover classes, covering a total area of 38,421,453 ha (148,345 mi 2 ), of which 13,148,345 ha (52,706 mi 2 ) are located in Mexico and 24,770,640 ha (95,639 mi 2 ) in the United States. We primarily used Landsat 8 (OLI) imagery, supplemented by limited ground surveys from two field campaigns, drone-based aerial data, and existing vegetation classification frameworks from both countries. The classification applied a data-fusion approach integrating 30-m Landsat 8 imagery, decadal phenology metrics from vegetation indices, and a random forest model trained mainly with datasets from a comprehensive national mapping project from the U.S. Geological Survey (USGS) GAP Analysis Project (GAP) and federal wildland fire agencies’ Landscape Fire and Resource Management Planning Tools (LANDFIRE) (GAP/LANDFIRE) [United States side] and the National Institute of Statistics and Geography (INEGI) [Mexico side] as well as land cover maps and opportunistic open-access and field observations. Mapping of the full BCR 33 region was carried out in two phases: 1) Phase I, the prototype map, covered a smaller portion of the transboundary area and identified 31 land cover classes, and 2) Phase II, the full BCR 33 map (refer to Figure 1), which resulted in 152 land cover classes. Using a Random Forest classifier, we achieved an overall prediction accuracy of 92% for the Phase I map and 87% for the Phase II full region map. This slight decrease can be attributed to working on a larger, more complex area with a greater number of land cover classes. No formal validation was conducted, aside from using a subset of the collected field observations and training data to assess model performance during and after training. The training sites were further verified using Google Earth (Google, 2026) imagery. Two undergraduate students who worked for over a year visually inspected imagery and open access public images to confirm each training site during model training using in-house developed, online, visual tools. A portion of this field training data was reserved for model validation, and the corresponding results are to be presented in later sections. The project developed an end-to-end, medium- and fine-resolution remote sensing–based data fusion mapping approach. This effort produced a map (Nagler et al., 2025) and the online tools to support a dynamic, live, online map for visualizing the transboundary vegetation communities in BCR 33. The toolset is currently hosted by the University of Arizona (UofA) Vegetation Index and Phenology (VIP) Lab to support FWS partners (https://vip.arizona.edu/viplab_data_explorer?LCM_BCR33). The online map is designed to allow rapid updates using new training, validation, or correction data, making it dynamic and maintainable. The approach we took established a framework for rapid updating and correction of land cover maps, as the model can be quickly retrained with new field observations, updated training data, or other sources. This enables dynamic mapping and change detection of the region’s vegetation. This framework is an advance in data fusion and crowdsourced mapping of complex, vulnerable regions, providing support to regional stakeholders and the wider user community. This transboundary map can inform the protection, conservation, and restoration of vegetation, habitat, and ecosystems, particularly for threatened and endangered species across the two nations using consistent and harmonized binational mapping systems. Beyond supporting land management decisions and stakeholders in the transboundary desert ecoregions, this BCR 33 mapping effort establishes a foundation for future rapid, low-cost, cross-border land cover mapping that can benefit and advance ecosystem management.

Arizona, Baja California, California, Nevada, Sina↗
USGS2026

Age and colony variation in Adélie penguin metapopulation vital rates: Insights from a 25-year mark–recapture study

Understanding how vital rates vary with age, life-history stage, and among populations is fundamental for predicting the demographic consequences of environmental change, especially in longer-lived species with complex life histories. These species often exhibit delayed maturity and iteroparity, making nuanced demographic insights critical for assessing their long-term viability. This study investigated age- and colony-related variation in Adélie penguin vital rates including survival, recruitment, and breeding propensity. We used mark–recapture data collected over 25 years (1996–2020) from three Adélie penguin breeding colonies that differed in population sizes and trends but comprised a metapopulation located at capes Royds, Bird, and Crozier on Ross Island, Antarctica. We used multi-state models to estimate survival and detection rates relative to reproductive state and breeding colony and estimated transition probabilities reflecting movements between reproductive states and colonies. Apparent survival varied by reproductive state, colony, and age and averaged 0.80 (SD = 0.02) at Bird, and 0.72 (SD = 0.03) and 0.73 (SD = 0.03) at Crozier and Royds, respectively, for pre-breeders age 2–7 years with strong declines in pre-breeder survival after age 8. We observed less age-related variation in survival of breeders and non-breeders, but we observed differences between colonies with lower survival for breeders (0.72 to 0.80) compared to non-breeders (0.75 to 0.82). The average probability of surviving the first 2 years after fledging ranged from 0.43 (SD = 0.14) at Royds and Crozier (0.43, SD = 0.10) to 0.55 (SD = 0.15) at Bird. Movement between colonies was highest for pre-breeders (0.00%–12.00% depending on age and colony) and lowest for breeders (<0.20%). We observed the lowest age-related recruitment rates at Royds, with recruitment at Crozier almost twice as high, and intermediate at Bird. Breeding propensity was highest at Crozier and lowest at Bird. Colony-specific variation in vital rates likely contributed differently to population trajectories, suggesting that care must be taken to extrapolate vital rate estimates across colonies even within a metapopulation. These findings also highlight the importance of considering age, life-history stage, and geographic variation when assessing population vital rates in species with complex life histories.

Frontiers in Ecology and Environment↗
USGS1995

Water-quality assessment of the Ozark Plateaus study unit, Arkansas, Kansas, Missouri, and Oklahoma: Analysis of information on nutrients, suspended sediment, and suspended solids, 1970-92

Water-quality data collected during water years 1970-90 (October 1 to September 30) for 83 surface-water sites and during 1970-92 for 395 ground-water sites in the 48,000 square mile Ozark Plateaus study unit of the National Water Quality Assessment Program were analyzed using selected descriptive and statistical methods. The water- quality data include nutrient (nitrogen and phosphorus), suspended sediment, and suspended- solids data, and ancillary information such as fertilizer use, animal waste, sewage treatment plant, and land use. Statistically significant differences exist in surface-water quality that can be attributed to physiography, land use, and other effects. The sites that were considered to be substantially affected by sewage treatment plants had the largest concentrations of nutrients. Nutrient concentrations generally were larger at sites associated with agricultural basins than at sites associated with forested basins. Statistically significant differences existed in the quality of ground water that can be attributed to hydrogeologic and land-use effects. Nutrient concentrations generally were largest where the water source is indicated to be shallow in origin and where parts of the hydrogeologic units are in agricultural land-use areas. Water quality has changed at several surface-water sites since 1970. Nutrient concentrations appear to have increased at some sites and decreased at other sites. Causes of these apparent trends are not known, but many of the sites with apparent trends are in agricultural areas. Surface-water loads of nutrients and suspended sediment were affected by several factors including streamflow, climate, drainage area, reservoir operation, and inputs from point and nonpoint sources. Annual loads were largest in large basins, with large inputs of nutrients or sediment during periods of high streamflows at locations where reservoir operation effects are not substantial.

Arkansas, Kansas, Missouri, Oklahoma↗
USGS2025

Decomposing the Tea Bag Index and finding slower organic matter loss rates at higher elevations and deeper soil horizons in a minerogenic salt marsh

Environmental gradients can affect organic matter decay within and across wetlands and contribute to spatial heterogeneity in soil carbon stocks. We tested the sensitivity of decay rates to tidal flooding and soil depth in a minerogenic salt marsh using the Tea Bag Index (TBI). Tea bags were buried at 10 and 50 cm depths across an elevation gradient in a subtropical Spartina alterniflora marsh in Georgia (USA). Plant and animal communities and soil properties were characterized once, while replicate tea bags and porewaters were collected several times over 1 year. TBI decay rates were faster than prior litterbag studies in the same marsh, largely due to rapid green tea loss. Rooibos tea decay rates were more comparable to natural marsh litter, potentially suggesting that is more useful as a standardized organic matter proxy than green tea. Decay was slowest at higher marsh elevations and not consistently related to other biotic (e.g., plants, crab burrows) or abiotic factors (e.g., porewater chemistry), indicating that local hydrology strongly affected organic matter loss rates. TBI rates were 32 %–118 % faster in the 10 cm horizon than at 50 cm. Rates were fastest in the first 3 months and slowed 54 %–60 % at both depths between 3 and 6 months. Rates slowed further between 6 and 12 months, but this was more muted at 10 cm (17 %) compared to 50 cm (50 %). Slower rates at depth and with time were unlikely due to the TBI stabilization factor, which was similar across depths and decreased from 6 to 12 months. Slower decay at 50 cm demonstrates that rates were constrained by environmental conditions in the deeper horizon rather than the composition of this highly standardized litter. Overall, these patterns suggest that hydrological setting, which affects oxidant introduction and reactant removal and is often overlooked in marsh decomposition studies, may be a particularly important control on organic matter loss in the short term (3–12 months).

Georgia↗
USGS2022

South Carolina and Landsat

South Carolina, the eighth State admitted to the union, transcends its size with its deep, rich history; striking beauty; vast natural resources; and extensive cultural diversity. Home to part of the Blue Ridge Mountains of the Central Appalachians, the Upstate is graced with more than 100 waterfalls, while the Lowcountry borders the Atlantic Ocean with 187 miles of coastline and 35 barrier islands. Forests cover two-thirds of the State, and forestry and agriculture together, as agribusiness, make up South Carolina’s leading industry. Two historic crops—cotton and tobacco—still rank in the top 10 commodities, though corn and soybeans now rank higher. Poultry, cattle, peanuts, and flowers also make the list. South Carolina’s population totals more than five million. Other residents include a variety of wildlife, bird, reptile, and fish species, including Ursus americanus (black bears), Alligator mississippiensis (American alligators), and Tursiops truncatus (bottlenose dolphins). More than 100 tree species also reside in South Carolina, which pays homage to one with its “The Palmetto State” nickname. South Carolina’s subtropical climate, long coastline, and lower elevations make it highly susceptible to tornado and hurricane activity and coastal flooding. Projected sea-level rise is a growing concern. A view from space can help monitor and manage natural resources on the land and in rivers, marshes, and the coast. Landsat reveals not just what an area looks like now, but also insights from decades ago.

South Carolina↗
USGS2025

Enhanced hydrologic monitoring and characterization of groundwater drainage features

Groundwater drains to the land surface, generating the baseflow of streams, lakes, and wetlands. The hydrologic resilience of baseflow during prolonged dry periods and after disturbance can be assessed with evolving remote sensing analysis paired with localized monitoring of groundwater drainage features and creative model calibration strategies.

Nature Water↗
USGS2025

Climate change and future water availability in the United States

The steady rise in global temperature as a result of human activity is causing changes in Earth’s water cycle. The balance of water stored within and moving between vapor, liquid, and frozen states in the water cycle is shifting, with consequences for water availability that include increases in drought, fire weather, flooding, and heavy precipitation, as well as cryosphere decline and sea-level rise. In this chapter of the U.S. Geological Survey Integrated Water Availability Assessment—2010–20, we provide an overview of climate-change observations and projections from Earth-system model simulations that relate to future water availability, from global and national climate assessments and from the published literature. Effects of climate change on primary water-cycle components are discussed in context of how global-scale hydroclimate drivers influence regional processes within the United States. Understanding the major climate drivers impacting the water cycle is crucial to predicting future changes in water availability and developing adaptation strategies to ensure human and ecosystem water supplies. First, we provide background information on the water cycle, the climate-model ensemble simulations developed to produce projections based on warming scenarios, and attribution and certainty levels. Tipping points, self-reinforcing feedbacks, cascading effects, and compound extremes are introduced. The framework of climatic impact drivers (CIDs) outlined in the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) is used to show primary drivers of physical change to the water cycle and to understand and predict changes in future water availability. Specific climate-change related observations and projections are discussed for water cycle components of precipitation, evapotranspiration, soil moisture, streamflow, lakes and wetlands, ice and snow, and groundwater, as well as their implications for future water availability for humans and ecosystems. The chapter concludes with a synthesis discussion of three examples of complex regional-scale hydroclimate processes that influence water availability for populations in the United States, including (1) mountain and coastal precipitation, (2) aridification and drought, and (3) the influence of forest-cover change on terrestrial water-vapor recycling.

Professional Paper↗
USGS2024

Apatite and monazite geochemistry record magmatic and metasomatic processes in rare earth element mineralization at Mountain Pass, California

The largest rare earth element (REE) deposit in the United States is a carbonatite intrusion at Mountain Pass in the Mojave Desert, California. Despite a clear spatiotemporal association of alkaline silicate and carbonatite intrusions at Mountain Pass, a genetic model of their mutual formation has not been resolved. The Mountain Pass carbonatite has long been upheld as an example of a primary magmatic body, but recent work has suggested it could be fluid-derived. This study investigates the geochemistry of apatite and monazite grains from the alkaline silicate and carbonatite stocks and dikes of the Mountain Pass district, to elucidate the magmatic history of the intrusive suite and identify the role of fluids in rare earth element mineralization. Three apatite populations are identified in the alkaline silicate rocks. A primary magmatic apatite group supports intrusion of the stocks as separate pulses of magma derived from a spatially extensive metasomatized mantle source region. The second group implicates the role of a regional fluid that mobilized light rare earth elements from apatite grains. A minor group of inherited apatite cores, identified by low Sr and negative Eu anomalies, supports assimilation of crustal material in the formation of the intrusive suite. Analyses of monazite and apatite grains from the carbonatite orebody also reveal a mix of primary magmatic and metasomatic (fluid-related) minerals. Compositional similarities between primary phosphates in the carbonatite and alkaline silicate rocks support a genetic link between the intrusive suites. The presence of fluids regionally and within the carbonatite orebody indicates the Mountain Pass carbonatite should not be classified as a purely magmatic REE deposit.

Arizona, California, Nevada↗
USGS2026

Variability and consistency in wildfire susceptibility: Insights from a national compilation

Background Wildfire risk in the United States is rising and remains a land management priority. The quantitative wildfire risk assessment (QWRA) framework integrates fuels, topography, weather and values at risk to estimate the potential change in value from wildfire. Within this, response functions (RFs) represent how values respond to fire intensity. These are often based on expert judgment, but variation across assessments is unclear. Aims This study uses data from the US Geological Survey (USGS) Wildfire Hazard and Risk Assessment Clearinghouse to characterize consistency and variation across categories and contexts. Methods We applied descriptive statistics to summarize RFs, using tables, box-and-whisker plots and heat maps stratified by highly valued resource or asset (HVRA) category and spatial scale. Key results RFs and value definitions vary, especially for ecosystem-related resources. Some functions, such as for buildings in the wildland–urban interface (WUI), translate well across contexts, while others require more input. Conclusions Some functions are broadly transferable, while others need customization. This analysis provides references and starting points for improvement to RFs in QWRAs. Interpretations Expanding the clearinghouse and dataset and building more transparency in expert elicitation can build trust among communities, agencies and end-users, and can support efficient use of limited resources to mitigate wildfire risk.

International Journal of Wildland Fire↗
USGS2026

Spectral characteristics and mapping of lithium-rich playas in the Basin and Range Province, western United States

This report presents a comprehensive set of prospectivity maps for lithium in brines located within the playas of the Basin and Range Province of western United States. The prospectivity assessment is based on criteria established in the descriptive deposit model outlined in a previous U.S. Geological Survey report, with a focus on regional remote sensing methodologies. The playas within the study area have been systematically ranked according to several key factors, including size, the presence and abundance of source rocks, vegetation as an indicator of water availability, documented prospects, and data products derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Notably, the deposits in the playa of Clayton Valley make it stand out as the sole lithium brine-producing playa in the United States. The ASTER sensor provides optimal regional coverage, characterized by a 60-kilometer swath width and unique multispectral capabilities that are not available with other remote sensing instruments. This report and the accompanying maps are intended to serve as resources for further multidisciplinary assessments of lithium or other elements or commodities where prospectivity can be inferred from the presence of proxies such as alteration minerals.

Arizona, Baja California, California, Chihuahua, I↗
USGS2023

The Saudi Geological Survey-U.S. Geological Survey northern Harrat Rahat project—Styles, rates, causes, and hazards of volcanism near Al Madīnah al Munawwarah, Kingdom of Saudi Arabia

Active volcanic systems pose serious hazards to people and property including inundation and incineration by lava, blanketing by tephra (volcanic ash), exposure to noxious volcanic gases, and damage from shallow earthquakes triggered by ascending molten material (magma). To improve understanding of volcanism and associated seismicity on the western Arabia Plate, the Saudi Geological Survey and the U.S. Geological Survey conducted a multi-year investigation of the northern Harrat Rahat volcanic field adjacent to the city of Al Madīnah al Munawwarah, Kingdom of Saudi Arabia. Project components included creation of a high-resolution digital topographic base; interpretation of eruptive history supported by detailed geologic mapping, paleomagnetism, and abundant high-precision geochronology of volcanic deposits; assessments of eruptive styles and volcanic hazards by physical volcanology; investigation of the origins of magmas in the mantle and of their differentiation in the crust revealed by chemical and isotopic petrology; gravity and magnetotelluric surveys to reveal crustal structures and to search for magma reservoirs; and regional and local seismic tomography and analyses of seismic hazards. Project results are presented in this Professional Paper as chapters written for technical scientific audiences. This initial chapter introduces the project and briefly summarizes results in plain language for readers who have more general backgrounds.

Al Madīnah al Munawwarah↗