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USGS2025

Overview of The SCEC/USGS Community Stress Drop Validation Study using the 2019 Ridgecrest earthquake sequence

We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress‐drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude‐spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress‐drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two‐week period of the 2019 Ridgecrest sequence, recorded within a 1° radius. In this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite‐fault modeling, ground‐motion‐based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between‐method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress‐drop values.

California↗
USGS1999

Characterization of selected biological, chemical, and physical conditions at fixed sites in the Upper Colorado River Basin, Colorado, 1995-98

Biological community samples were collected at 15 sites in the Upper Colorado River Basin (UCOL) in Colorado as part of the National Water-Quality Assessment (NAWQA) Program. Sites sampled in two physiographic provinces, the Southern Rocky Mountains and the Colorado Plateau, represented agriculture, mining, urban and recreation, and mixed land uses and background conditions. Nine measures of water quality, which include information on nutrients, specific conductance (a surrogate for salinity), trace elements in streambed sediment, pesticides in fish tissue, fish communities, and macroinvertebrate richness and composition and stream habitat were used for comparisons among sites within the two physiographic provinces. Sampling sites from three other NAWQA study units—the Rio Grande Valley, the South Platte River Basin, and the Upper Snake River Basin study units—were categorized on the basis of land use and stream size in order to develop a larger data set for comparison to sites in the UCOL. Three categories of land use—forested (includes mining, urban and recreation, and background), agriculture, and mixed—were used for comparison to the UCOL fixed sites. Results indicated that all sites other than the Colorado River below Baker Gulch (a background site) showed some water-quality characteristics to be significantly affected. Results indicated that the concentrations of cadmium and zinc in streambed sediment at mining land-use sites in the Southern Rocky Mountains physiographic province generally were orders of magnitude higher than streambed-sediment concentrations at the background site. Streambed-sediment concentrations at mining land-use sites in the UCOL were greater than the 75 th percentile of concentrations from sites in the three other NAWQA study units. Fish communities and habitat conditions were degraded at mining land-use sites compared to the background site. Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness and the percentage of EPT were lower at mining land-use sites than at the background site and were less than the 50 th percentile of those for sites from the three other NAWQA study units. Nutrient concentrations at urban and recreation sites in the Southern Rocky Mountain physiographic province generally were greater than concentrations at the background site and generally were between the 25 th and 90 th percentile of concentrations for sites from the three other NAWQA study units. Habitat conditions and fish communities at urban and recreation sites were slightly degraded compared to the background site. EPT richness and the percentage of EPT were lower at urban and recreation sites than at the background site and were between the 25 th and 75 th percentile of those for sites from the three other NAWQA study units. The percentage of Chironomidae, which may be indicative of pollutant-tolerant organisms, was higher at urban and recreation sites than at the background site. Mixed land-use sites in the Southern Rocky Mountains physiographic province had similar nutrient concentrations and similar cadmium and zinc streambed-sediment concentrations. Fish-community degradation index values were very different among the three mixed land-use sites in the Southern Rocky Mountains physiographic province. Larger percentages of omnivores and anomalies such as lesions and deformities at two mixed land-use sites resulted in higher degradation values of the fish community. Agriculture land-use sites had higher concentrations of nutrients and selenium than the background site in the Colorado Plateau physiographic province. Concentrations of p,p '-DDE in fish tissue at agriculture sites were higher than the 75 th percentile of concentrations for sites from the three other NAWQA study units. Fish communities had degradation values near the 75 th percentile for agriculture sites. The percentage of EPT was low at agriculture sites when compared to the background site. Two mixed land-use sites in the Colorado Plateau physiographic province had similar concentrations of nutrients, selenium, and p,p '-DDE, and similar EPT richness and composition. These two sites were located downstream from agricultural and urban activities. Some water-quality measures at these two sites indicated degradation compared to a mixed land-use site upstream from most of the agriculture and urban activities in the Colorado Plateau physiographic province.

Colorado↗
USGS2025

Effect of land cover type on 3D deformation recovery from synthetically deformed high-resolution satellite optical imagery

The limits of detection for earthquake surface deformation in the spatial domain have improved with advances in remote sensing imagery data availability, resolution, and analysis. Sub-pixel correlation and digital elevation model (DEM) differencing from sub-meter, earthquake-spanning satellite optical imagery has enhanced surface rupture mapping and deformation measurements. However, knowledge of measurement accuracy and uncertainty is limited. To address this, we construct orthophotos and digital elevation models (DEMs) from repeat high resolution (∼0.5 m) satellite optical imagery along two sections of the Garlock fault, California with clear fault geomorphology and differing land cover. We deform later sets of DEMs and images with synthetic earthquakes containing both diffuse and discrete horizontal and vertical displacements. Sub-pixel image correlation and DEM differencing demonstrate how vegetation degrades recovered displacement accuracy. In barren land cover, horizontal displacements are detectable to an expected ∼1/10th-pixel size. With shrubs, trees, and grass, detectable displacements increase to >1/2-pixel size, and filtering results by correlation score and using elevation values as input rather than image values improves accuracy. Vertical displacement detection thresholds remain lower in vegetation, at >1-pixel size. Higher slope angles degrade displacement recovery, worsened by vegetation. Diminishing seasonal separation improves accuracy over vegetated regions, though not to the level achieved in barren environments. These results will inform research and operational efforts on the utility of high resolution satellite optical imagery for detecting deformation in varied land cover. Furthermore, they reveal where alternative measurements, such as from LiDAR or radar interferometry, are required to mitigate the effects of vegetation and capture fine-scale crustal deformation.

California↗
USGS2025

Relations of groundwater quality to long-term surface disposal of produced water near the Midway-Sunset and Buena Vista Oil Fields, California, USA

Contamination of groundwater by oil-field fluids in proximity to oil and gas development has been an issue of concern to water users and regulators given long histories of development and legacy disposal practices. A robust set of geochemical tracers including petroleum hydrocarbon compounds, thermogenic gases, inorganic ion concentrations, stable isotopes, radioactive isotopes, and noble gases were used to assess if oil-field fluids mixed with groundwater near the Midway-Sunset and Buena Vista Oil Fields in California, USA. Results show evidence of mixing of oil-field fluids with groundwater within the study area from either anthropogenic or natural processes. Produced water plumes associated with modern surface disposal facilities, used since the late 1950s, extend up to 1.5 km and currently remain within the boundaries of the oil fields. Plumes associated with earlier routing of produced water down natural drainages and in large retention structures (Midway Basin and Sunset Basin) near the Buena Vista Lake Bed are present in groundwater east of the oil fields. Based on geochemical tracer evidence, aerial imagery, and aerial electromagnetic surveys, these legacy plumes reach the western portion of the Central Valley aquifer system, an important groundwater resource for agricultural and domestic supply. The legacy plume associated with Sunset Basin may further be detected downgradient in deeper groundwater beneath the southern extent of the Buena Vista Lake Bed based on the presence of thermogenic gases and petroleum hydrocarbon compounds.

California↗
USGS2026

Where will the cat cross the road? Comparing camera and GPS-based models for identifying wildlife corridors

Designing effective wildlife corridors is a critical conservation challenge in fragmented landscapes. GPS-based step selection functions strongly predict dispersal corridors and connectivity, but GPS collaring can be expensive and invasive. Camera-based occupancy models are widely used for connectivity analyses but may involve trade-offs in data resolution. Despite widespread use of both approaches, few studies have directly compared them using concurrent datasets. We developed a stacked single-species, single-season occupancy model and a Circuitscape connectivity surface for mountain lions (Puma concolor) on Washington’s Olympic Peninsula, USA, and compared them with a connectivity surface from an existing integrated step selection function. Both models predicted mountain lion GPS locations well, with binned Spearman rank correlations of 1 for Circuitscape and 0.96 for the step selection function, though step selection better identified habitat use by dispersers. Connectivity predictions were moderately correlated across the landscape ( r = 0.26), but agreement was strongest in human-dominated areas most critical for corridor planning. We conclude that GPS-based approaches are advantageous when data collection is feasible and the focus is on dispersal or fine-scale movement. However, camera-based approaches may be preferable for multi-species monitoring, large spatial and temporal scales, noninvasive sampling, when resources are limited, or when fine-scale or dispersal-specific inference is not required.

Washington↗
USGS2025

Ultramafic float rocks at Jezero crater (Mars): Excavation of lower crustal rocks or mantle peridotites by impact cratering?

Based on observation and data from meteorites and in situ scientific missions, experiments as well as models, the Martian mantle is assumed to share some compositional and mineralogical affinity with the terrestrial mantle. However, there might be subtle differences like the Martian mantle being more ferroan. Yet, we do not have any direct analysis of a Martian mantle rock to confirm this assumption. NASA’s Perseverance rover found olivine-rich boulder-sized float rocks on the upper Jezero fan (Mars). These boulders have an ultramafic composition and their mineralogy is dominantly composed of Fo 73±3 olivine with high-Mg orthopyroxene, Cr-rich Ti-Fe oxides and minor plagioclase and high-Ca pyroxene. Microtextural and petrological analysis reveals that these minerals crystallized at equilibrium. In addition, these boulders are different from all the bedrocks analyzed by Perseverance along its traverse which are crustal igneous rocks and sediments. Comparing our data to Martian meteorites and available Mars bulk silicate models (BSM), we discuss that these boulders could represent primitive melts and/or lower crustal material, and we specifically hypothesize that they could be mantle peridotites. We propose that these putative mantle rocks could have been excavated by the succession of impacts from the shallow mantle or lower crust in the Isidis region where Jezero crater is located. These olivine-rich boulders could thereby constitute the first direct analysis of a Martian mantle rock.

Earth and Planetary Science Letters↗
USGS2025

Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems

Although rainfall-triggered landslides are initiated by subsurface hydro-mechanical processes related to the loading, weakening, and eventual failure of slope materials, most landslide early warning systems (LEWS) have relied solely on rainfall event information. In previous decades, several studies demonstrated the value of integrating proxies for subsurface hydrologic information to improve rainfall-based forecasting of shallow landslides. More recently, broader access to commercial sensors and telemetry for real-time data transmission has invigorated new research into hydrometeorological thresholds for LEWS. Given the increasing number of studies across the globe using hydrologic monitoring, mathematical modeling, or both in combination, it is now possible to make some insights into the advantages versus limitations of this approach. The extensive progress demonstrates the value of in situ hydrologic information for reducing both failed and false alarms, through the ability to characterize infiltration during, as well as the drainage and drying processes between major storm events. There are also some areas for caution surrounding the long-term sustainability of subsurface monitoring in landslide-prone terrain, as well as unresolved questions in hillslope hydrologic modeling, which relies heavily on the assumptions of diffuse flow and vertical infiltration but often ignores preferential flow and lateral drainage. Here, we share a collective perspective based on our previous collaborative work across Europe, North America, Africa, and Asia to discuss these challenges and provide some guidelines for integrating knowledge of hydrology and climate into the next generation of LEWS. We propose that the greatest opportunity for improvement is through a measure-and-model approach to develop an understanding of landslide hydro-climatology that accounts for local controls on subsurface storage dynamics. Additionally, new efforts focused on the subsurface hydrology are complementary to existing rainfall-based methods, so leveraging these with near-term precipitation forecasts is a priority for increasing lead times.

Alaska↗
USGS2025

Four-band image mosaic of the Colorado River Corridor downstream of Glen Canyon Dam in Arizona, derived from the May 2021 airborne image acquisition

In May 2021, the U.S. Geological Survey’s Grand Canyon Monitoring and Research Center acquired airborne multispectral high-resolution data for the Colorado River in the Grand Canyon, Arizona. The image data, which consist of four spectral bands (red, band 1; green, band 2; blue, band 3; and near infrared, band 4) with a ground resolution of 20 centimeters, are available as 16-bit unsigned-integer GeoTIFF files in Sankey and others (2024) (available online at https://doi.org/10.5066/P9BBGN6G ). The image files are projected in the State Plane Coordinate System, using the central Arizona zone (202) with the North American Datum of 1983 National Adjustment of 2011. The assessed spatial accuracy for these data is based on 47 ground-control points that were independent from the ground-control points used by the contractor for aerotriangulation and is reported at the 95-percent confidence level as 0.514 meter (m) and a root mean square error of 0.297 m. The intended uses of this dataset are primarily in support of scientific research and monitoring applications. Examples of these applications include high-resolution spatial and temporal change detection of the river channel, geomorphic landforms, riparian vegetation, and backwater and nearshore habitat, as well as other ecosystem-wide mapping. These imagery data also serve as reference material for field science mission planning, as base data for field data collection including community science activities, and as a highly detailed guide for technical boat operation during science activities such as reconnaissance for nighttime missions and navigating rapids during low flows.

Arizona, Nevada, Utah↗
USGS2026

On-demand global Landsat evapotranspiration product: Development, evaluation, and dissemination

Global actual evapotranspiration (ET) is one of the essential climate variables needed to understand and manage the relationships among food, energy, and water resources. The U.S. Geological Survey Earth Resources Observation and Science (EROS) Center launched a provisional ET product in 2020, offering on-demand, field-scale global coverage derived from Landsat data through the EROS Science Processing Architecture (ESPA) platform. The ESPA interface provides ET data for cloud-free Landsat overpasses starting in 1982 with Landsat 4 through the current Landsat 9. The ET data are delivered as a Provisional Level-3 Science product created using the Operational Simplified Surface Energy Balance (SSEBop) model. Landsat surface temperature and reference ET are the main model drivers along with vegetation index and net radiation for model parameterization. A large volume of Landsat-based ET orders (e.g., over 1,200,000 images from June 2020 through December 2025) around the world indicate increasing awareness and application of the ET data. The ESPA platform enables land and water resource managers and researchers to access a first-order ET product without requiring advanced knowledge of remote sensing technology or evapotranspiration modeling. We present the methodology and workflow of the on-demand Landsat ET product and its performance evaluations over diverse hydro-climatic settings. The product can help estimate field-scale consumptive water use and thus quickly and consistently assess historical water use, allocation, and budget to inform water management under changing environments. Future ET data aggregated to monthly and seasonal time scales are expected to enhance integration with decision-making tools and procedures.

Remote Sensing of Environment↗
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↗
USGS2025

A geospatial analysis of water-quality threats from orphan wells in principal and secondary aquifers of the United States

Throughout the history of oil and gas production in the United States, millions of wells have been drilled for exploration and energy production. Hundreds of thousands of unplugged wells are no longer actively producing and are currently under orphan status, with no responsible party obligated for plugging. Orphan wells can pose threats to water resources by providing pathways for contaminants such as hydrocarbons and brines to migrate into water-supply aquifers. In this study, we investigate the potential threats to groundwater resources posed by orphan wells at the national scale. Water-quality data is extremely sparse in relation to orphan wells nationally and may not be suitable for identifying contamination from oil and gas development. We used geospatial and statistical methods to evaluate which principal and secondary aquifer systems may be most susceptible to contamination from orphan wells. Analysis involved three sets of susceptibility factors including: 1) factors related to the number and density of orphan wells; 2) factors that can threaten well integrity and contribute to transport of contaminants; and 3) factors related to groundwater withdrawal rates and the affected populations/communities in the event of water quality disturbances. From a dataset of 117,672 documented orphan wells, 64,203 fall within a principal aquifer system, while the remainder fall within a secondary aquifer system. By assessing the combination of well integrity and hydrogeologic factors within these aquifer systems, five groupings of principal aquifers were identified, where groups ranged from aquifer systems with high numbers of orphan wells, multiple well integrity threats and high withdrawals, to aquifers with a relatively low number of orphan wells, limited well integrity threats and minimal water use. Three regions of the country emerge containing aquifers with higher susceptibility to contamination from orphan oil and gas wells. These regions include 1) The Appalachian Basin (including the Pennsylvanian Aquifer System), 2) The Gulf Coast Aquifers (including the Coastal Lowlands Aquifer system) and 3) The California Aquifers (including the California Coastal Basin Aquifer system). This work is the first multivariate geospatial investigation of orphan wells and groundwater resources on a national scale, and sheds light on which aquifers are most susceptible to groundwater contamination from orphan wells.

Science of the Total Environment↗
USGS2024

Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall

Communities downstream of burned steep lands face increases in debris-flow hazards due to fire effects on soil and vegetation. Rapid postfire hazard assessments have traditionally focused on quantifying spatial variations in debris-flow likelihood and volume in response to design rainstorms. However, a methodology that provides estimates of debris-flow inundation downstream of burned areas based on forecast rainfall would provide decision-makers with information that directly addresses the potential for downstream impacts. We introduce a framework that integrates a 24 h lead-time ensemble precipitation forecast with debris-flow likelihood, volume, and runout models to produce probabilistic maps of debris-flow inundation. We applied this framework to simulate debris-flow inundation associated with the 9 January 2018 debris-flow event in Montecito, California, USA. When the observed debris-flow volumes were used to drive the probabilistic forecast model, analysis of the simulated inundation probabilities demonstrates that the model is both reliable and sharp. In the fully predictive model, however, in which debris-flow likelihood and volume were computed from the atmospheric model ensemble's predictions of peak 15 min rainfall intensity, I 15 , the model generally under-forecasted the inundation area. The observed peak I 15 lies in the upper tail of the atmospheric model ensemble spread; thus a large fraction of ensemble members forecast lower I 15 than observed. Using these I 15 values as input to the inundation model resulted in lower-than-observed flow volumes which translated into under-forecasting of the inundation area. Even so, approximately 94 % of the observed inundated area was forecast to have an inundation probability greater than 1 %, demonstrating that the observed extent of inundation was generally captured within the range of outcomes predicted by the model. Sensitivity analyses indicate that debris-flow volume and two parameters associated with debris-flow mobility exert significant influence on inundation predictions, but reducing uncertainty in postfire debris-flow volume predictions will have the largest impact on reducing inundation outcome uncertainty. This study represents a first step toward a near-real-time hazard assessment product that includes probabilistic estimates of debris-flow inundation and provides guidance for future improvements to this and similar model frameworks by identifying key sources of uncertainty.

California↗
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↗
USGS2024

Mapping karst groundwater flow paths and delineating recharge areas for springs in the Little Sequatchie and Pryor Cove watersheds, Tennessee

The Little Sequatchie River and Pryor Cove Branch, in southern Tennessee, drain the eastern escarpment of the Cumberland Plateau to the Sequatchie River near the southern end of the Sequatchie Valley. The Little Sequatchie River is the largest tributary to the Sequatchie River by drainage area, covering over 120 square miles. The hydrology of the two drainage areas has been largely altered by karst processes, which has caused the majority of the streams to sink at the contact between the Mississippian Pennington Formation and the underlying Mississippian Bangor Limestone. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service and Tennessee Department of Environment and Conservation, initiated a study in 2021 to map the karst groundwater pathways in both watersheds in order to delineate recharge areas for several springs. One of these springs, Sequatchie Cave, represents a significant habitat for two Species of Greatest Conservation Need, the Glyphopsyche sequatchie (Sequatchie caddisfly) and the federally endangered Marstonia ogmorhaphe (royal marstonia). Springs and springflow-dominated streams in the Little Sequatchie River valley and Pryor Cove also provide water for agricultural practices and serve as a drinking water source for nearby communities. During the study, a total of 25 dye injections were conducted over eight rounds from January 2022 through March 2023. Dye traces from these injections helped to delineate recharge areas for six major springs, ranging from 7.3 to 65.2 square miles in area. The majority of the dye traces remained subsurface (from sinkpoint to recovery site) for long distances, with karst groundwater travelling nearly 8 miles before resurfacing. The dye traces also had rapid traveltimes, often travelling hundreds to thousands of feet per hour. The goal of this project was to provide scientific data related to karst groundwater pathways and spring recharge areas to aid State and Federal agencies in making informed decisions to protect and preserve this unique and vulnerable karst system.

Tennessee↗
USGS2024

A benchmark dataset and workflow for landslide susceptibility zonation

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100 km 2 "> 4,100 km 2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.

Earth-Science Reviews↗
USGS2025

Constraining landslide frequency across the United States to inform county-level risk reduction

Informative landslide hazard estimates are needed to support landslide mitigation strategies to reduce landslide risk across the United States. Whereas existing national-scale landslide susceptibility products assess where landslides are likely to occur, they do not address how often , which is a critical element of landslide hazard and risk assessments. In particular, the U.S. Federal Emergency Management Agency's National Risk Index (NRI) requires landslide frequency estimates to inform expected annual loss estimates. We present county-level landslide frequency (landslides per area per year) estimates for the 50 US states. We applied Bayesian negative binomial regression to estimate both the expected (average) reported landslide frequency and full distribution of annual landslide counts for each county. We compared a suite of models that used combinations of landslide-susceptible area, probability of potentially triggering earthquakes, frequency of potentially triggering precipitation, and ecological region as predictors. We trained our models with landslide inventory data from counties with the most comprehensive records available nationwide and used zero-inflated negative binomial distributions as an incompleteness model to correct for temporal reporting gaps. We selected a preferred frequency model to inform the NRI based on information criteria and physically plausible parameter estimates. The model showed that average annual reported landslide frequencies vary by 5 orders of magnitude across US counties, ranging from 0.002 (0.00015–0.05) landslides 1000 km −2 yr −1 in Kusilvak Census Area, Alaska, to 29 (19–46) landslides 1000 km −2 yr −1 in Lake County, California, reflecting the country's strong variations in landslide susceptibility, earthquake probability, and other factors for which ecological region serves as a proxy. Counties with estimated frequencies in the top 20 % of all counties are predominately along the West Coast of the continental United States, in mountainous regions of the Pacific Northwest and Intermountain West, in locally steep or earthquake-prone regions of the Midwest and Southeast, along the Appalachians, in southern and southeastern Alaska, and on some Hawaiian islands. By examining the number of landslides predicted in 99th percentile years for each county, we identified that 26 % of US counties likely have potential for widespread landsliding with more than 10 landslides 1000 km −2 yr −1 , even when such large events have not been reported in the training data for that county. Overall, our results better represent the range of possible landslide frequencies and spatial variations than previous national-scale estimates reported in the NRI, and our approach can inform other risk-reduction and loss-mitigation efforts across the United States and globally.

Natural Hazards and Earth System Sciences↗
USGS2025

Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes

As the northern high-latitude permafrost zone experiences accelerated warming, permafrost has become vulnerable to widespread thaw. Simultaneously, wildfire activity across northern boreal forest and Arctic/subarctic tundra regions impacts permafrost stability through the combustion of insulating organic matter, vegetation, and post-fire changes in albedo. Efforts to synthesis the impacts of wildfire on permafrost are limited and are typically reliant on antecedent pre-fire conditions. To address this, we created the FireALT dataset by soliciting data contributions that included thaw depth measurements, site conditions, and fire event details with paired measurements at environmentally comparable burned and unburned sites. The solicitation resulted in 52 466 thaw depth measurements from 18 contributors across North America and Russia. Because thaw depths were taken at various times throughout the thawing season, we also estimated end-of-season active layer thickness (ALT) for each measurement using a modified version of the Stefan equation. Here, we describe our methods for collecting and quality-checking the data, estimating ALT, the data structure, strengths and limitations, and future research opportunities. The final dataset includes 48 669 ALT estimates with 32 attributes across 9446 plots and 157 burned–unburned pairs spanning Canada, Russia, and the United States. The data span fire events from 1900 to 2022 with measurements collected from 2001 to 2023. The time since fire ranges from 0 to 114 years. The FireALT dataset addresses a key challenge: the ability to assess impacts of wildfire on ALT when measurements are taken at various times throughout the thaw season depending on the time of field campaigns (typically June through August) by estimating ALT at the end-of-season maximum. This dataset can be used to address understudied research areas, particularly algorithm development, calibration, and validation for evolving process-based models as well as extrapolating across space and time, which could elucidate permafrost–wildfire interactions under accelerated warming across the high-northern-latitude permafrost zone. The FireALT dataset is available through the Arctic Data Center ( https://doi.org/10.18739/A2RN3092P , Talucci et al., 2024).

Arctic↗
USGS2024

U.S.-Mexico Borderland & vegetation community map

People on both sides of the United States-Mexico border need a high-resolution, binational vegetation community map that spans the entire United States-Mexico borderlands. Traditionally, mapping efforts in this region were impeded by complex logistics related to the international border, differing national needs and plans, and resource allocations and priorities. To address this need, scientists from the U.S. Geological Survey (USGS) Southwest Biological Science Center partnered with the Sonoran Joint Venture, the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program, data engineers from the Department of Biosystems Engineering at the University of Arizona, and collaborators from the Wildlands Network, the Borderlands Program to produce the first prototype land cover map within the overlapping Mojave Desert, Sonoran Desert, and the North American Bird Conservation Initiative’s Bird Conservation Region 33 (BCR33) using Landsat satellite data . BCR33 is an area of high biodiversity, providing habitat for bird species of concern and other wildlife. The land cover map supports FWS recovery plan efforts related to conservation planning activities for many species, including Yellow-billed Cuckoo ( Coccyzus americanus ), Cactus Ferruginous Pygmy-Owl ( Glaucidium brasilianum cactorum ), Southwestern Willow Flycatcher ( Empidonax traillii extimus ), Yuma Ridgway’s Rail ( Rallus obsoletus yumanensis ), Bendire’s thrasher ( Toxostoma bendirei ), LeConte’s thrasher ( Toxostoma lecontei ), Masked Bobwhite ( Colinus virginianus ridgwayi ), jaguar ( Panthera onca ), and endangered plants such as Bartram’s stonecrop ( Graptopetalum bartramii ) and the Pima pineapple cactus ( Coryphantha robustispina ssp. robustispina ). In 2024, a Phase-II map for the full BCR33 region was completed, increasing the understanding of the binational nature of natural communities. The published map and associated paper can be found here .

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