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1,101 records · Page 20Linked to original sources
USGS2026

A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping

Land cover information is essential for understanding Earth’s surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD’s temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)—a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD. LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consisting of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency—critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics. Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 ± 1.60% overall agreement, compared to 71.1 ± 1.7% agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 ± 1.22% agreement, compared to 84.6 ± 1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.

Remote Sensing of Environment↗
USGS2025

Using public participatory geographic information systems (PPGIS) to explore uses and values for Mojave Trails National Monument, California

Many people ascribe a variety of values to public lands and waters, but some values are more difficult to assess and quantify than others. Public participatory geographic information systems (PPGIS) are tools that have been used to help quantify and map the public’s diverse values for a landscape. This work describes the first known Office of Management and Budget–approved use of PPGIS by a Department of the Interior bureau. The U.S. Geological Survey developed an internet-based application to aid in gathering PPGIS data, called Values Mapping for Planning in Regional Ecosystems (VaMPIRE). Further, this work describes the first pilot of the VaMPIRE application in coordination with the Bureau of Land Management to collect spatial data and other survey data regarding the public’s uses of and values for locations within Mojave Trails National Monument. We emailed the link to the VaMPIRE application to an interested party email list in 2024 with 207 valid emails and received 74 responses; we also received 47 responses from members of an off-roading social media group. Of the list of 16 value options, recreation was the most popular value for the monument, followed by wilderness and inspirational. Over 1,000 points were placed throughout the monument, indicating locations people use or value, with the locations spread throughout the entire monument. Additionally, most survey respondents stated their ability to receive benefits in locations they mapped would not change in response to a hypothetical scenario related to recreational facility development. This report describes exploratory results from the first use of the VaMPIRE tool in Mojave Trails National Monument and includes reflections on how the process went and considerations for future use of VaMPIRE.

California↗
USGS2024

Radiogenic strontium- and uranium-isotope tracers of water-rock interactions and hydrothermal flow in the Upper Geyser Basin, Yellowstone Plateau Volcanic Field, USA

Natural radiogenic isotopes (primarily 87 Sr/ 86 Sr) from hot springs in the Upper Geyser Basin of the Yellowstone Plateau volcanic field and associated rocks were used to evaluate groundwater flow patterns, water-rock reactions, and the extent of mixing between various groundwater sources. Thermal waters have very low uranium concentrations and 234 U/ 238 U activity ratios near 1.0, which limit their utility as tracers in this reducing setting. Thermal waters have higher Sr concentrations (<22 ng/g) and a wide range of 87 Sr/ 86 Sr values that vary both temporally at individual discharge sites and between adjacent springs, indicating that conduits tap different subsurface reservoirs to varying degrees. Sr from local rhyolites have 87 Sr/ 86 Sr compositions that bound the range of values observed in groundwater throughout the basin. Non-boiling springs on the west flank of the basin discharge water with low 87 Sr/ 86 Sr consistent with flow through young volcanic rocks exposed at the surface. Boiling springs in the central basin have higher 87 Sr/ 86 Sr values reflecting interactions with older, more radiogenic volcanic rocks. Variability in upwelling thermal waters requires mixing with a low 87 Sr/ 86 Sr component derived from young lava or glacial sediments, or more likely, from deeper sources of hot groundwater circulating through buried Lava Creek Tuff having intermediate 87 Sr/ 86 Sr. Isotope data constrain basin-wide output of thermal water to 110–140 kg·s −1 . Results underscore the utility of radiogenic Sr isotopes as valuable tracers of hydrothermal flow patterns and improve the understanding of temperature-dependent water-rock reactions in one of the largest continental hydrothermal systems on Earth.

Wyoming↗
USGS2025

Using gridded seismicity to forecast the long-term spatial distribution of earthquakes for the 2025 Puerto Rico and U.S. Virgin Islands National Seismic Hazard Model

Gridded (or background) seismicity models are a critical component of probabilistic seismic hazard assessments, accounting for off‐fault and smaller‐magnitude earthquakes. They are typically developed by declustering and spatially smoothing an earthquake catalog to estimate a long‐term seismicity rate that can be used to forecast future earthquakes. Here, we present new gridded seismicity models for use in the 2025 National Seismic Hazard Model (NSHM) for Puerto Rico and the U.S. Virgin Islands (PRVI). The previous PRVI NSHM was released in 2003, and our new models incorporate updates to both data and methodology. We utilize an updated earthquake catalog based on improved Puerto Rico Seismic Network data with newly characterized completeness epochs. The catalog is divided into crustal, subduction interface, and intraslab seismicity using new methods and Slab2 subduction zone geometries. To forecast the long‐term spatial distribution of earthquakes, we use an updated methodology developed for the 2023 U.S. 50‐state NSHM, considering three declustering methods and two spatial smoothing methods based on 2D Gaussian kernels. To adapt it for the complex seismotectonics of the region, we also adopt probabilistic methods to account for events with unknown depths and uncertainties in tectonic classification, and develop a new method for spatial scaling to counteract the effects of spatial variability in network coverage while maintaining the use of smaller events. Finally, we test the performance of these spatial models in forecasting the location of M w ≥ 5earthquakes in the region. Our updated methodology improves the representation of epistemic uncertainty relative to the 2003 model, and our results demonstrate the effectiveness of the new measures we have introduced to address heterogeneities in network detection and systematically evaluate forecast performance.

Puerto Rico, U.S. Virgin Islands↗
USGS2026

Deep subsurface organic-rich shale supports abundant, diverse, and novel fungi

As Earth’s principal reservoir of organic carbon and microbial biomass, the deep subsurface hosts microorganisms capable of mobilizing this once-sequestered carbon. Contrary to standard assumptions of eukaryotic scarcity, this study documents abundant fungal communities, ranging from 4.2 × 10 3 to 6.8 × 10 3 fungal cells mL −1 , across a methane-producing organic-rich shale 247–556 meters below the surface. Although fungal:bacterial cell ratios ranged from 1:7028 to 1:713, application of biomass conversion factors developed for oceanic systems yielded a median fungal:bacterial biomass ratio of 1:4.7. 16S rRNA gene amplicons revealed bacterial and archaeal communities mirroring those found in well-characterized extremophilic, carbon-degrading environments, while sequencing of 18S rRNA gene and ITS rRNA spacer amplicons collectively identified a eukaryotic hotspot with 689 fungal OTUs across six phyla. The dominant fungal classes, Agaricomycetes and Dothideomycetes, are well-established degraders of recalcitrant carbon compounds at the surface, suggesting they may similarly contribute to organic matter degradation and ecosystem maintenance in the subsurface. Cultivation and isolation efforts yielded 205 fungal strains, including 13 candidate novel taxa, underscoring the deep subsurface as an underexplored eukaryotic habitat. Stable carbon isotopes indicate methane is predominantly generated via microbial conversion of the fossil carbon, while water isotopes suggest in situ geochemical conditions have been relatively stable since the Late Pleistocene, with subglacial recharge as a plausible mechanism for microbial introduction. Collectively, these findings suggest that fungi are underrecognized contributors to organic matter transformation and functional diversity in the deep biosphere, revealing a critical gap in our understanding of deep subsurface ecosystem processes.

Indiana, Michigan, Ohio↗
USGS2025

Increased flood exposure in the Pacific Northwest following earthquake-driven subsidence and sea-level rise

Climate-driven sea-level rise is increasing the frequency of coastal flooding worldwide, exacerbated locally by factors like land subsidence from groundwater and resource extraction. However, a process rarely considered in future sea-level rise scenarios is sudden (over minutes) land subsidence associated with great (>M8) earthquakes, which can exceed 1 m. Along the Washington, Oregon, and northern California coasts, the next great Cascadia subduction zone earthquake could cause up to 2 m of sudden coastal subsidence, dramatically raising sea level, expanding floodplains, and increasing the flood risk to local communities. Here, we quantify the potential expansion of the 1% floodplain (i.e., the area with an annual flood risk of 1%) under low (~0.5 m), medium (~1 m), and high (~2 m) earthquake-driven subsidence scenarios at 24 Cascadia estuaries. If a great earthquake occurred today, floodplains could expand by 90 km 2 (low), 160 km 2 (medium), or 300 km 2 (high subsidence), more than doubling the flooding exposure of residents, structures, and roads under the high subsidence scenario. By 2100, when climate-driven sea-level rise will compound the hazard, a great earthquake could expand floodplains by 170 km 2 (low), 240 km 2 (medium), or 370 km 2 (high subsidence), more than tripling the flooding exposure of residents, structures, and roads under the high subsidence scenario compared to the 2023 floodplain. Our findings can support decision-makers and coastal communities along the Cascadia subduction zone as they prepare for compound hazards from the earthquake cycle and climate-driven sea-level rise and provide critical insights for tectonically active coastlines globally.

California, Oregon, Washington↗
USGS2024

Slip rate for the Rose Canyon fault through San Diego, California, based on analysis of GPS data: Evidence for a potential Rose Canyon–San Miguel-Vallecitos fault connection?

The Rose Canyon fault is the southern extension of the larger Newport–Inglewood–Rose Canyon fault system, which represents a major structural boundary in the Inner Continental Borderland (ICB) offshore of southern California. Ten to fifteen percent of total plate boundary motion in southern California is thought to be accommodated by the faults of the ICB, but the exact distribution of slip is uncertain. With an onshore segment, the Rose Canyon fault offers an opportunity to measure the slip rate using traditional geodetic methods. In this study, we use Global Positioning System (GPS) surface velocities from a combined campaign and continuous GPS network to constrain elastic models of the Rose Canyon fault. We then compare the observed surface velocities with proposed conceptual models of regional fault connections that facilitate the transfer of slip into the Rose Canyon fault to assess how well the observations are explained by the models. The results of elastic half‐space models suggest that the Rose Canyon fault may be slipping toward the higher end of geologic estimates, with the preferred model indicating a slip rate of 2.4 ± 0.5 mm/yr. Although limited in terms of near‐fault benchmarks, we find an improved model fit using an asymmetrical elastic half‐space model and a higher slip rate, suggesting a potential rheological contrast across the Rose Canyon fault, similar to observations from the northern Newport–Inglewood fault segments. Observed GPS surface velocities, background seismicity, and gravity anomalies south of San Diego Bay point toward a more easterly trace for the Rose Canyon fault, suggesting a possible connection with the San Miguel–Vallecitos fault system. Such a connection could increase the potential rupture lengths of future earthquakes and have important consequences for regional seismic hazards.

California↗
USGS2024

Groundwater flow model for the Des Moines River alluvial aquifer near Des Moines, Iowa

Des Moines Water Works (DMWW) is a regional municipal water utility that provides residential and commercial water resources to about 600,000 customers in Des Moines, Iowa, and surrounding municipalities in central Iowa. DMWW has identified a need for increased water supply and is exploring the potential for expanding groundwater production capabilities in the Des Moines River alluvial aquifer, where it operates two radial collector wells (RCWs). The U.S. Geological Survey, in cooperation with DMWW, completed a study of the Des Moines River alluvial aquifer and interactions of the RCWs with the aquifer; no previously published model has included the existing well locations, which is the focus of this model. A conceptual and numerical groundwater flow model have been developed to characterize the Des Moines River alluvial aquifer under existing conditions, to simulate water levels observed in the RCWs, and to provide publicly accessible hydrologic data and research that advance understanding of the regional hydrologic system and can potentially be used in the future to evaluate groundwater production scenarios. Model performance was assessed by comparing observed and simulated groundwater levels that included water level elevations, water level changes, water level inequality observations, surface water streamflow, and change in surface water volume from upstream to downstream. Water table elevation in the aquifer layers is on average slightly overestimated with average absolute value error less than 1.5 meters at both RCWs and less than 2.5 meters for all observation wells in the alluvial aquifer layers. The model also accurately simulated water tables greater than the RCW design minimum (a water level threshold at which RCW pumping is reduced) in all timesteps for which water level observation data existed. Water table elevation error was higher in other model layers that were not the focus of the study, and the model did not accurately match streamflow targets.

Iowa↗
USGS2025

Implications of physics-based M9 ground motions on liquefaction-induced damage in the Cascadia Subduction Zone: Looking forward and backward

Given the likelihood of future M9 Cascadia Subduction Zone (CSZ) earthquakes, various estimates of the resulting, regional ground motions have been made, including a suite of 30 physics-based simulations that reflect key modeling uncertainties. However, because the last CSZ interface rupture occurred in 1700 CE, the shaking expected in such an event is especially uncertain, as are the impacts to the built and living environments. Like other coseismic impacts, soil liquefaction poses a significant threat and must be considered by any scenario study used to inform planning and response, or to focus mitigation resources. Liquefaction is also notable for its potential to “ground truth” ground-motion estimates, given that its presence or absence in the geologic record can provide constraint on the intensities of shaking in past events. It is thus an important phenomenon looking both forward and backward. Accordingly, using recent physics-based simulations, this study (1) predicts liquefaction in M9 CSZ ruptures at 400 locations in Oregon, Washington, and British Columbia (BC) using an array of cone-penetration-test based models and (2) uses paleoliquefaction evidence at ten sites spanning from Southern Oregon to Vancouver, BC to constrain possible ground-motion intensities experienced in the 1700 CE earthquake. The forward predictions indicate that liquefaction in M9 events could be pervasive in the region and affect numerous population hubs, with the potential for damage across hundreds of square kilometers. The backward analyses suggest that 1700 CE ground-motion intensities may have been less than expected from M9 simulations in some northern portions of the CSZ (e.g. Seattle), given the paucity of 1700 CE liquefaction evidence in these areas. Ultimately, further discovery and analysis of CSZ paleoliquefaction, or lack thereof, will confirm or modify this possibility and the conclusions drawn herein.

Oregon, Washington↗
USGS2024

Estimation of reservoir storage capacity and geomorphic change detection analysis from a multibeam bathymetric survey of Randy Poynter Lake, Rockdale County, Georgia

Rockdale County Department of Water Resources has a directive to update estimates of the reservoir storage capacity of Randy Poynter Lake, located in northern Georgia, and to assess recent sedimentation and associated storage capacity loss. In 2022, the U.S. Geological Survey completed a multibeam bathymetric survey of Randy Poynter Lake to update storage capacity estimates and to quantify storage capacity change since the first multibeam bathymetric survey in 2012 in consideration of estimated errors inherent to bathymetric surveys. Data from the 2022 survey were used to generate contours of the reservoir as well as compute storage capacity at regular increments of water-surface elevation. Storage capacity comparisons between 2012 and 2022 at Randy Poynter Lake show minimal changes that are within the estimated uncertainties, with consistent or slightly increased storage capacities observed at most water-surface elevations and reductions observed at the remaining few elevations. Comparison of the multibeam bathymetric data collected in 2012 with data collected in 2022 further allowed for a formal geomorphic change detection analysis to map, quantify, and infer causation of morphological change over time with respect to a level of detectable change. The volume change in Randy Poynter Lake for the decade between 2012 and 2022 was slightly net-depositional and within the estimated uncertainty. The spatial distribution of sediment deposition was primarily concentrated in the northern portion of the lake, where the principal tributary flows into Randy Poynter Lake. The results of the geomorphic change analysis were used to further understand the future implications to storage capacity change. Despite the challenges of confirming systematic biases because of uncertainties exceeding the observed changes, insights from the study help predict long-term reservoir sediment accumulation, indicating a reservoir half-life extending about 650 years from 2022 on the basis of the current sediment yield estimates.

Georgia↗
USGS2026

Regreening, restoring, and reconnecting a southwestern wetland ecosystem – the Zeedyk wetland

Alluvial wetland ecosystems are vital as biodiversity hotspots but are increasingly threatened by anthropogenic stressors and drought. These pressures are especially acute in arid and semi-arid regions, where eco-hydrologic connectivity is fragile and recovery is slow. This study quantifies the efficacy of nature-based solutions, particularly the ‘Zeedyk approach,’ which employs low-tech Natural Infrastructure in Dryland Streams (NIDS)—including rock detention structures—to slow surface water, raise groundwater tables, and restore wetland function at a spring-fed wetland in Cebolla Canyon, New Mexico, U.S.A. Our results depict a Restoration Feedback Loop that captures stages of change from a healthy wetland in 1935, altered by 20th-century agriculture and grazing, to the re-establishment of the historical flow regime by 2024 documented through an 89-year archive of aerial imagery (1935–2024). By the end of our study period, the Spring-Fed Wetland had expanded by roughly 229% of the original 1935 area, to 4.13 ha. Using 40 years of satellite data, we assess changes in vegetation and hydrology with remote sensing indices. Spatial and temporal analyses reveal significant increases in vegetation greenness and wetness, particularly in an Expanded Wetland subregion, which exhibited ∼3.5x higher wetness and ∼1.5x higher greenness trends compared to adjacent areas. Monthly metrics highlight seasonal variability, with increases in greenness linked to monsoonal rainfall and lateral water redistribution, indicating that restoration impacts extend beyond the primary wetland. This study demonstrates the utility of cloud-based platforms like Google Earth Engine and USGS EarthExplorer for long-term monitoring of wetland restoration, while quantifying the efficacy of the ‘Zeedyk approach’ and demonstrating its potential as a scalable method to restore and conserve wetland meadows in other arid and semi-arid landscapes.

New Mexico↗
USGS2026

Climate-adaptive urban planning: Quantitative assessment of drought impacts and practical strategies for climate-resilient urban green spaces

Urban green spaces (UGSs) are vital for enhancing a city’s resilience and livability; however, their functionality is increasingly jeopardized by drought, particularly in water-scarce regions. This study evaluates drought impact on UGSs in Metropolitan Adelaide, Australia, a representative semi-arid urban system, using satellite-derived Normalized Difference Vegetation Index (NDVI) time-series data spanning 2000–2020. Vegetation dynamics were analyzed through Seasonal-Trend decomposition using Loess (STL), standardized anomaly assessment, lagged Pearson correlation, Ordinary Least Squares (OLS) regression, and Mann–Kendall trend analysis. To isolate climatically sensitive signals, 29 urban lawn patches were examined separately from mixed urban canopy, given their shallow root systems and direct dependence on surface moisture. NDVI declined by approximately 0.09 units during the Millennium Drought (2001–2009), with summer greenness deficits reaching 24% below the 20-year benchmark. Temperature was the dominant driver of lawn NDVI variability (r = −0.863, R 2 = 74.5%), substantially exceeding the effect of rainfall (r = 0.156, R 2 = 2.4%). El Niño–Southern Oscillation (ENSO) cycles modulated vegetation responses, with La Niña years supporting recovery and El Niño years amplifying decline. Post-drought recovery remained incomplete, with NDVI deficits of 8–20% persisting through 2020; full recovery was observed only in 2017, coinciding with the highest recorded summer rainfall. No significant directional trend was detected over the full study period (Mann–Kendall τ = 0.005, p = 0.908). These findings demonstrate that heat, rather than water limitation alone, is the primary driver of vegetation stress in urban systems, highlighting the benefits of integrated management strategies that address both warming and moisture deficits to sustain urban green infrastructure under future climate conditions. We introduce the concept of “urban greenery drought,” referring to a form of vegetation stress in managed urban landscapes where greenness is reduced primarily by elevated temperature and atmospheric demand despite water availability.

Adelaide↗
USGS2025

The 3D Elevation Program—Supporting the economy of Massachusetts

Introduction Massachusetts extends from the mountains of the Appalachian system in the west of the State to the sandy beaches and rocky shorelines of the Atlantic coast in the east. Inland topographic data support a wide range of important activities, including geologic mapping, transportation planning, forest and wildlife management, quantifying ecological services, water supply protection, commonwealth-wide infrastructure planning, local site planning, and flood-plain management. Nearshore bathymetry can be used to support coastal portions of the Commonwealth by addressing the combined threats of ocean warming, strong storm surge, and rising sea levels. The maintenance and (or) expansion of Massachusetts ports (for instance, Boston, New Bedford) and Cape Cod sediment management depends upon the accurate mapping of bathymetry and the frequent influx of sediment and redeposition. Critical applications that address the broad range of requirements depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features. The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey (USGS) in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Massachusetts. The status of available and in-progress 3DEP baseline lidar data in Massachusetts is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 ( https://www.usgs.gov/3dep/lidarspec ) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $1.23 million in new benefits annually to Massachusetts. The top 10 Massachusetts business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.

Massachusetts↗
USGS2025

SURF: An automated method for building nonplanar 3D fault models from earthquake hypocenters

Accurately characterizing 3D fault geometry is vital for improving our understanding of earthquake behavior and informing the development of seismic hazard models. Despite their importance, subsurface fault structures tend to be poorly constrained because of limitations in observational data. Improvements to the seismic networks and earthquake detection algorithms have increased the precision and volume of earthquake catalogs, which help illuminate detailed subsurface fault structure and provide the most direct information available about fault geometries at depth. We present a Python package to automate generating 3D fault geometries directly from hypocentral seismicity patterns. This method begins with clustering events based on their spatial density, identifying coherent patterns. Nearby clusters are then merged based on the similarity of their orientations. We fit nonplanar surfaces using support vector regression to balance surface accuracy with minimal deviations from planarity. The fault models are output as quadrilateral meshes at user‐defined resolution. In the process of generating the 3D fault surfaces, we compute the spatial density of seismicity around the surface and the planarity as quantitative metrics of the model outputs. As a proof of concept, we apply this approach to the San Andreas–Calaveras fault junction region and the 2019 Ridgecrest earthquake sequence, both in California, which contain complex subparallel faults well defined at the Earth’s surface and abundant microseismicity. These case studies demonstrate the method’s ability to model complex fault structures, including long continuous fault surfaces, crossing faults, variably dipping segments, and subparallel faults. We test the method on both standard network catalogs and double‐difference relocated catalogs. We find that our seismicity‐based fault model results align with published 3D models that incorporate additional constraints and interpretations ( Plesch et al. , 2020 ; Aagaard and Hirakawa, 2021 ). This workflow provides a low‐user‐input solution for estimating fault geometries at depth from earthquake catalogs.

California↗
USGS2026

Simulation-based scenario ShakeMaps for large magnitude (MW6.5+) crustal earthquakes on the Seattle, Tacoma, and southern Whidbey Island faults, Washington, USA

Scenario ground‐motion maps based on empirical ground‐motion models (GMMs) provide a rapid and generally reliable means of estimating the amplitude and distribution of earthquake shaking. However, because GMMs are designed for broad applicability, they often rely on simplified representations of Earth structure, which can limit their accuracy in regions with complex source, path, and site effects. This can substantially impact the accuracy of predicted shaking in areas like western Washington State, where deep, interconnected basin structure exerts a strong influence on seismic‐wave propagation. In this study, we present a new suite of simulation‐based scenario ShakeMaps that characterize ground shaking from large‐magnitude ( ⁠ M W 6.5–7.5) earthquakes on the Seattle, Tacoma, and southern Whidbey Island faults. These maps are developed using results from recent 3D wave propagation simulations ( Stone et al. , 2022 , 2023 , 2025 ) that incorporate realistic rupture geometries, variable slip distributions, and a regional 3D seismic velocity model with shallow soils. Broadband ground motions are estimated by combining the low‐frequency (<1 Hz) deterministic seismograms from these studies with high‐frequency (1–10 Hz) stochastic seismograms. Simulated ground motions are corrected to account for the enforced minimum shear‐wave velocity and nonlinear site response. The resulting ShakeMaps represent median ground‐shaking estimates derived from multiple rupture scenarios with varying slip distributions and hypocenter locations for each fault. To extend ShakeMap coverage beyond the simulation domain (i.e., into eastern Washington, northern Oregon, and southwestern British Columbia), we scale GMM‐based ground‐motion estimates using amplification patterns observed in the simulations. These new ShakeMaps reveal the substantial influence of deep basin structure on shaking intensity, underscoring the importance of considering crustal structure complexity in regional hazard assessments for the Pacific Northwest.

Washington↗
USGS2025

Assessing nonpoint-source uranium pollution in an irrigated stream-aquifer system

Uranium (U) in rocks and soils of arid and semi-arid environments can be mobilized by irrigation and fertilization, posing environmental and health risks. Elevated U, along with selenium (Se) and nitrate (NO 3 ) co-constituents, necessitates careful monitoring and management. We developed a distributed-parameter numerical model to assess U pollution in an irrigated stream-aquifer system, applying it to a 552 km 2 region in Colorado's Lower Arkansas River Valley (LARV) over 14 years. A MODFLOW model, describing groundwater and stream flow, was coupled with an RT3D-OTIS model to portray reactive U transport. Calibration using the PESTPP-iES iterative ensemble smoother (iES) software indicated good agreement with observed U concentrations. The model revealed substantial and variable U levels across the LARV, highlighting potential hotspots and possible contributing factors, such as geological composition of the bedrock and near-surface shale and aquifer sediments derived from them, irrigation practices, and riparian landscape. U levels exceed the chronic standard (85th percentile = 30 μg/L, set by the US Environmental Protection Agency), which is the permissible regulatory threshold, in groundwater across 44 % of the region and along the river by an average factor of 2.9. Simulated average U concentrations in the non-riparian aquifer and river are 124 μg/L and 60 μg/L, respectively, compared with 112 μg/L and 62 μg/L for measured values. The average 85th percentile U concentration is 222 μg/L in the aquifer and 82 μg/L in the river. Average simulated U mass loading to the river is 0.17 kg/day per km, compared to an estimated 0.23 kg/day per km. Findings provide a baseline for comparing future simulated outcomes of alternative best management practices (BMPs) for U pollution mitigation and offer a methodology applicable to other irrigated regions.

Colorado↗
USGS2026

Recent scientific contributions by the U.S. Geological Survey in the San Francisco Bay and Sacramento–San Joaquin Delta Estuary

Introduction The San Francisco Bay and Sacramento–San Joaquin Delta Estuary (hereafter, Bay-Delta) is the largest estuary on the West Coast of the United States. The Bay-Delta covers more than 1,600 square miles and drains a watershed of more than 75,000 square miles, which is greater than 40 percent of California. The region surrounding the Bay-Delta is home to about 10 million people, and its habitats (fig. 1) support more than 800 plant and animal species. The waterways of the Bay-Delta are the central hub of California’s extensive freshwater delivery system, supplying water to more than 27 million Californians and 4 million acres of farmland in the Central Valley. The U.S. Geological Survey (USGS) is the primary science agency of the U.S. Department of the Interior, providing a broad range of Earth, water, biological, and mapping data and expertise to inform natural resource management across the country. This fact sheet focuses on research conducted by the USGS in the Bay-Delta region, mostly within the past 5 years. The fact sheet is organized across five major themes: water flow and water quality, fish and wildlife, wetland restoration, invasive species, and hazards. In each of these areas, the USGS works closely with Federal, State, and local agencies; academia; and non-governmental organizations to provide objective, evidence-based science. The data and knowledge gained from USGS research and monitoring are publicly available and are routinely used by agencies, including the U.S. Bureau of Reclamation, U.S. Army Corps of Engineers, U.S. Fish and Wildlife Service, California Department of Water Resources, California Department of Fish and Wildlife, California State Water Resources Control Board, Delta Stewardship Council, and many additional organizations and stakeholders to inform management of the Bay-Delta region and its resources.

California↗
USGS2026

Nonstationary demographic state-space models using unreplicated counts for species undergoing environmental stressors

A fundamental task in ecological statistics is to estimate abundance and growth rate distributions from wildlife monitoring data to inform conservation management. Modeling time series of wildlife populations presents a number of challenges from both statistical and ecological perspectives, including discreteness; lack of replication; nonstationarity; and observation, demographic, and other phenomenological processes. Nonstationary dynamics are often exhibited by populations undergoing environmental stressors. Models must account for these characteristics to produce reliable estimates of abundance and trends, yet estimation can be challenging with unreplicated data. We propose nonstationary demographic state-space models using unreplicated counts for populations undergoing environmental stressors. A reduced growth rate model matches the complexity of the unreplicated count data, and a fecundity bound on growth rate distributions allows the separation of processes affecting growth rates like environmental stressors from those affecting abundance external to growth rates like migration. NDSSMs allow for the embedding of nonstationary model components, and we explore the use of changepoints, volatility clustering, and migration processes. We apply the proposed nonstationary models in case studies of herons affected by predator/competitor reestablishment and three bat species affected by a fungal pathogen causing white-nose syndrome. Nonstationary models outperform stationary models and generalized linear mixed effects models according to model scoring and visual inspection of predictions, and provide estimates more consistent with published values. Incorporating migration improves model fit universally, even with approximate one-way immigration, most likely because populations are extirpated, recolonized, and increase multiple-fold over the upper bound set by species fecundity. In addition, estimates of the timing and severity of the environmental stressor differed for models with migration. Including nonstationary and demographic components in a fecundity-bounded growth rate model improves inference and benefits interpretability of hyperparameters. In turn, this adjusts uncertainties in predictions of abundance and growth rates over time, providing the ingredients needed for informed conservation analysis and for directing future monitoring of at-risk species.

Journal of Agricultural, Biological and Environmen↗