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USGS2025

International data gaps at the Center for Engineering Strong Motion Data

The Center for Engineering Strong Motion Data (CESMD) is utilized by seismologists, engineers, and disaster management professionals in the US and has historically achieved and distributed waveforms from across the globe for significant earthquakes. The increased access to the waveforms via Web API (Application Programming Interface) offers a unique opportunity to provide the community complete datasets, sampling a variety of tectonic environments and geologic conditions, increasing the number of available ground motion records for use in ground motion models (GMMs) and improving the accuracy of earthquake engineering evaluations. The objective of this study is to programmatically identify gaps in global event data from the past decade and backfill missing data gaps at CESMD. We first compare the CESMD catalog with the Advanced National Seismic System (ANSS) Comprehensive Earthquake Catalog identifying regions and time periods where strong-motion data is limited or inadequate. To backfill datasets at CESMD for significant events, we pinpoint regions and time intervals that lack information, creating a list of events for which we’d like to obtain data. An important facet of this work is identifying the source of data and metadata across earthquake repositories around the world and integrating these data repositories into our current strong-motion data processing workflow. In parallel with these newly processed datasets, we are developing a script to produce data origination citations to include provenance and attribution information to associate with respective datasets at CESMD. We showcase our methodology for identifying and filling data gaps at CESMD using three case studies (the 2018 Anchorage Alaska earthquake sequence, seismicity associated with the 2018 Hawaiian Kilauea volcano eruption, and several earthquakes in Turkey) and then outline our strategy to apply our data gap backfilling methods on an international scale.

Conference Paper↗
USGS2026

Influence of new residential construction varying in housing density on bird species, human tolerance guilds, and communities

Human population growth and changing settlement patterns fuel the development of urban fringe lands worldwide, with implications for biodiversity. We conducted a 12-year study of birds in the fast-developing urban fringe lands of the central Puget Sound region, Washington, USA, to examine the effect of development configuration on birds. We hypothesized that lower-intensity conservation developments, compared to higher-intensity planned community developments, would benefit the overall bird community, as well as native forest birds (avoiders of human development) and avian generalist species (adapters to human development), but that higher-intensity planned community developments would benefit synanthropic species (exploiters of human development). We fit single-species and multi-species occupancy models to test these hypotheses. Consistent with our hypotheses, we found that a greater proportion of the overall community, avoiders, and adapters occupied lower-intensity conservation developments compared to higher-intensity planned community developments. However, we did not detect an effect of development type on the exploiter guild, and we found that species in the exploiter guild are variable in their response to the configuration of suburban developments. We also hypothesized that human tolerance guilds would be a useful predictor of individual species responses to development type. This hypothesis was somewhat supported: we found that, for avoiders, 87% of species in the guild had the same response to development type as the overall guild; for adapters, 63% had the same response as the overall guild, and for exploiters, only 44% had the same response as the overall guild. Our results indicate that the configuration of suburban developments can have a meaningful impact on bird communities, particularly on those species that are most sensitive to any level of development.

Washington↗
USGS2024

Exploring the dynamic interactions between the Southern San Andreas Fault and a normal fault under the Salton Sea

We investigate the dynamic interactions between the Southern San Andreas Fault (SSAF) and a proximal normal fault (NF) beneath the Salton Sea in southern California. The NF, positioned near the SSAF terminus at Bombay Beach, exhibits 11–15 displacement events across 14 stratigraphic sequences, with a range of 0.2–1.4 m of vertical offset since ∼2–3 ka. Notably, four of these events may align temporally with SSAF earthquakes, raising questions about the possible interplay between the two faults. Utilizing dynamic rupture models, we analyze the coseismic interactions between the SSAF and NF, addressing under what conditions the SSAF induces slip on the NF. Our findings reveal that a suite of SSAF ruptures, particularly those propagating from north to south, can trigger slip on the normal fault and replicate observed vertical offsets. If the SSAF extends beneath the Salton Sea, earthquakes originating south of the NF intersection are less likely to trigger normal fault slip, although we cannot exclude this possibility. Some SSAF ruptures do not trigger discernible slip on the NF, rendering such events undetectable in the stratigraphic record. Our research contributes toward discussions regarding the seismic hazard in southern California, shedding light on the interplay between the SSAF and NF.

California↗
USGS2025

Variable partitioning of lithium in rhyolitic melt during decompression and ascent

The partitioning behavior of Li in magmatic systems is increasingly being investigated due to the economic importance of Li in the transition to sustainable energy resources (e.g., batteries). However, at upper crustal pressures, it remains uncertain whether Li preferentially partitions into the vapor or liquid (brine) phase or remains in the silicate melt. This complicates our ability to determine where Li resides—silicate melt, minerals, or fluid phase—upon eruption, a crucial factor for understanding its postdepositional movement and concentration into a brine or volcano-sedimentary deposit. Here, we present a novel investigation into the behavior of Li within natural evolved melts during continuous magma decompression and ascent using melt embayments (open melt inclusions). Mineral-hosted melt embayments preserve records of the evolving composition of the exterior melt, including degassing pathways and ascent timescales, when paired with appropriate diffusion coefficients. Lithium concentration profiles were measured in quartz-hosted melt embayments from the rapidly quenched eruptive phases of five rhyolitic, caldera-forming eruptions to investigate the behavior of Li during magma decompression and ascent, where vapor partitioning and ascent dynamics were previously established by investigating H 2 O and CO 2 profiles. We find that in four systems, embayments contain lower interior Li concentrations than the coerupted melt inclusions; the fifth system contains the same Li concentrations in embayments and melt inclusions. However, many of these embayments contain gradients, with 84 % preserving Li enrichment near the melt-bubble interface, as compared to their interior concentration. We interpret these characteristics to represent two distinct stages of Li partitioning during magma decompression and ascent, in contrast to existing literature that proposes only one type of partitioning behavior. The first stage is interpreted as melt depletion of Li, likely driven by partitioning into an exsolved supercritical fluid phase, supported by the strong correlation between the extent of Li depletion and Cl concentration in the melt, as well as the decompression rate. This behavior then fundamentally shifts, where Li reenriches in the melt, postulated to be driven by the unmixing of the supercritical fluid phase at shallow pressures. For the one system that did not develop Li gradients through decompression, we attribute this to the lower values of Na and Cl in the melt, potentially inhibiting the partitioning of Li into a fluid phase. Importantly, the behavior of Li during decompression is not consistent within or between volcanic centers, highlighting the need for systematic experimental investigation in variable composition melts at pressures relevant to conduit dynamics. This knowledge would improve our ability to model Li profiles to understand magma decompression, and predict where Li resides (e.g., stored in volcanic glass, gas, or crystals) upon eruption prior to any later extraction.

Economic Geology↗
USGS2026

Estimation of parental abundance using hierarchical Bayesian modeling with data augmentation

Pedigree-based estimation methods leverage the fact that each offspring in a cohort is genotypically “marked” by its parents and represent a recent and promising toolset for estimating population dynamics. This includes pedigree accumulation estimators that model the “accumulation” of inferred unique parents within a given cohort to estimate parental abundance. Unlike close-kin mark-recapture approaches, which rely on intercohort comparisons, pedigree accumulation modeling can be completed solely using intracohort samples. This is particularly advantageous for semelparous species, where intercohort pairs are impossible and adult life stages can be difficult to sample without affecting their likelihood of successfully reproducing. Previous work has evaluated a range of estimators for such datasets, concluding that the non-parametric Chao estimator provides the most accurate and precise estimates for feasible levels of sampling effort. We used simulated data to evaluate an alternative estimator based on hierarchical modeling and data augmentation in a Bayesian framework. Results indicate that estimates from the hierarchical Bayesian estimator had comparable accuracy and better precision than both the previously tested Chao1 estimator and the improved iChao formulation across a range of sample sizes and sex ratios. Furthermore, the Bayesian estimator was far more robust to simulated errors in pedigree reconstruction, especially the presence of false negatives. Hierarchical Bayesian pedigree accumulation models can also provide additional insight into underlying reproductive ecology through their use of an explicit observation process, allowing for the incorporation or estimation of species- and population-specific reproductive dynamics. More broadly, the parametric nature of these models offers opportunities to efficiently pool information among datasets as well as to propagate uncertainty within more complex models.

Ecology and Evolution↗
USGS2024

Changes in soil erosion caused by wildfire: A conceptual biogeographic model

Soil erosion rates after wildfire are strongly controlled by intrinsic properties such as topography, weather, climate, soil, and vegetation. These landscape and hydroclimatic properties are important in determining post-fire erosion rates; however, their influence on post-fire erosion and their interaction with the intensity of a wildfire remains uncertain. A key limitation in resolving this uncertainty is the lack of conceptual models and frameworks for organising data related to the geomorphic sensitivity of landscapes to wildfire. Our aim is to develop a framework for consolidating understanding of post-fire erosion in the context of hydroclimatic conditions which contribute to system states, for example soil and vegetation properties, and wildfire regime. The framework is developed around a simple conceptual model where the change in erosion due to wildfire is a product of change in runoff generation and sediment supply, which is strongly related to landscape net primary productivity (NPP). We hypothesised that geomorphic sensitivity to wildfire should vary as a unimodal humped relationship across a gradient of NPP, peaking at an intermediate level. To develop this framework and to test the hypothesis, we first review intrinsic soil and vegetation properties related to the supply and transport of sediment from burned and unburned hillslopes. Net primary productivity is systematically related to these intrinsic properties because it integrates many processes involved in soil and vegetation development. Empirical data indicate a trend in the change in surface runoff generation with NPP after wildfire, peaking at an NPP of approximately 15 Mg C ha −1 y −1 . A simple model of fuel availability and soil heating are correlated with a similar “humped” trend in sediment supply. These results are consistent with our conceptual model, which indicates that sediment supply and runoff contribute towards a distinct peak in wildfire effects on erosion at an intermediate level of NPP. We propose that landscapes of intermediate NPP typically have the highest quantity of fuel available to burn, which cause large changes to the soil surface properties. Landscapes at intermediate NPP also tend to produce intrinsic soil and vegetation properties that promote erosion after wildfire. The interplay between these short and long-term landscape characteristics is strongest at intermediate levels of NPP. Our proposed biogeographic model of geomorphic sensitivity to wildfire was supported by erosion data from burned hillslope and zero-order catchments studies from a range fire-prone landscapes in Australia and North America. Our proposed conceptual model will help identify areas most vulnerable to post-fire erosion changes.

Geomorphology↗
USGS2026

King Rails in southeastern Arkansas are majority resident

King Rails ( Rallus elegans ), a declining species of secretive marsh bird, have a mixed migratory strategy, with some individuals migrating and some being year-round residents. Determining the distribution of migratory versus resident King Rails is critical to guiding habitat management and assessing risks to populations. We examined the migratory status of adult King Rails at the Choctaw West Wildlife Management Area in southeastern Arkansas, the only known site in Arkansas with multiple breeding pairs. Of the 23 adult King Rails we tagged in 2022 and 2023, two birds migrated away from the local area, one in each year. One migrated 145 km, and the other 450 km. Our findings suggest that the line between the migratory and resident range of Arkansas King Rails could be north of our site.

Arkansas↗
USGS2026

Tringa flavipes (Lesser Yellowlegs) from separate breeding sites subdivides the Prairie Pothole Region in space and time during southbound migration

Some staging regions support multiple groups of the same migratory species, each of which may use the region differently. Characterizing the ways, in which separate groups use such regions can therefore help to identify vulnerabilities during this sensitive period of the annual cycle. The Prairie Pothole Region (PPR) is a massive wetland complex in the northern Great Plains of North America used by ∼11 million shorebirds during migration. The PPR has been heavily modified by agriculture and is experiencing varied effects of global climate change, threatening the health of the shorebirds that rely on it. Here, we used 6 seasons of southbound tracking data of Tringa flavipes (Lesser Yellowlegs)—a long-distance migratory shorebird species with an estimated population decline of 63% over the last 4 decades—from 9 sites across their breeding range to explore differences in migratory behavior within this important staging region. We found that 75% of tracked individuals used the region during southbound migration, and T. flavipes from different breeding sites detoured 110–875 km from their most direct migratory route to access the PPR. Individuals that arrived later stayed longer and made more stops within the region than those that arrived early. Individuals originating from different breeding sites also displayed spatial and temporal segregation within the region: T. flavipes from southwest and central Alaska relied heavily on the northwestern PPR, while those from Canada used the central and southeastern portions of the PPR. Finally, timing of use varied among groups, but the southeastern PPR became increasingly important over the course of the southbound migratory window, as other wetlands likely dried out. Our study highlights the portions of the PPR of critical importance to migrating T. flavipes and the diversity of ways, in which different groups from within the same species can use a single staging region.

Alaska, Manitoba, Northwest Territories, Ontario, ↗
USGS2024

Earthquake scenario development in conjunction with the 2023 USGS National Seismic Hazard Model

We present earthquake scenarios developed to accompany the release of the 2023 update to the US Geological Survey National Seismic Hazard Model (NSHM). Scenarios can serve a range of local and regional needs, from developing proactive-targeted mitigation strategies for minimizing impending risk to aiding emergency management planning. These deterministic scenarios can also be used to communicate seismic hazard and risk to audiences who are not well versed in methods, such as probabilistic seismic hazard analyses. Specifically, we discuss the scenarios developed, challenges, and lessons learned in the development process, and how this work aided the development of the 2023 NSHM itself. In total, 28 scenarios were developed for Hawaii, Utah, Alaska, and Virginia considering the 2023 NSHM science, past scenario efforts, and input from local experts and stakeholders. Finally, we investigate how NSHM modeling decisions can change estimated impacts to Utah and Hawaii in more detail showing, for example, that a shallower dip of the Wasatch fault under Salt Lake City can increase predicted ground-motion intensities and therefore estimated losses and deaths.

Earthquake Spectra↗
USGS2026

Bayesian belief network model to predict human-wildlife conflict in protected areas

Human-wildlife conflict (HWC) poses a pervasive global challenge, affecting livelihoods and threatening biodiversity. To better anticipate and mitigate HWC risk, we developed a large-scale predictive model using a Bayesian Belief Network (BBN). We surveyed 1,011 park rangers across 135 terrestrial protected areas in three Andean countries, documenting recent HWC incidents involving wildlife persecution or killing, livestock depredation, crop damage, or threats to human safety and property. We identified key drivers of HWC risk, including governance, wildlife acceptance, participation, and habitat quality. A sensitivity analysis revealed that enhancing governance and improving wildlife acceptance could reduce HWC risk by > 85%. The BBN model demonstrated scalability, effectively identifying strategies to reduce HWC risk at multiple scales, from individual protected areas to national networks. Our findings highlight the importance of strengthening governance, increasing wildlife acceptance, and enhancing community participation in conservation efforts. BBNs provide a flexible, cost-effective, and data-driven tool to guide protected areas and wildlife managers in monitoring, anticipating, and making informed decisions to mitigate conflict and promote coexistence.

Scientific Reports↗
USGS2023

Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions

Selecting negative training sites is an important challenge to resolve when utilizing machine learning (ML) for predicting hydrothermal resource favorability because ideal models would discriminate between hydrothermal systems (positives) and all types of locations without hydrothermal systems (negatives). The Nevada Machine Learning project (NVML) fit an artificial neural network to identify areas favorable for hydrothermal systems by selecting 62 negative sites where the research team had confidence that no hydrothermal resource exists. Herein, we compare the implications of the expert selection of negatives (i.e., the NVML strategy) with a random sample strategy, where it is assumed that areas outside the favorable structural ellipses defined by NVML are negative. Because hydrothermal systems are sparse, it is highly probable that, in the absence of a favorable geological structure, hydrothermal favorability is low. We compare three training strategies: 1) the positive and negative labeled examples from NVML; 2) the positive examples from NVML with randomly selected negatives in equal frequency as NVML; and 3) the positive examples from NVML with randomly selected negatives reflecting the expected natural distribution of hydrothermal systems relative to the total area. We apply these training strategies to the NVML feature data (input data) using two ML algorithms (XGBoost and logistic regression) to create six favorability maps for hydrothermal resources. When accounting for the expected natural distribution of hydrothermal systems, we find that XGBoost performs better than the NVML neural network and its negatives. Model validation was less reliable using F1 scores, a common performance metric, than comparing probability estimates at known positives, likely because of the extreme natural class imbalance and the lack of negatively labeled sites. This work demonstrates that expert selection of negatives for training in NVML likely imparted modeling bias. Accounting for the sparsity of hydrothermal systems and all the types of locations without hydrothermal systems allows us to create better models for predicting hydrothermal resource favorability.

Geothermal Resources Council Transactions↗
USGS2024

Uncertainty and spatial correlation in station measurements for mb magnitude estimation

The body‐wave magnitude (⁠⁠) is a long‐standing network‐averaged, amplitude‐based magnitude used to estimate the magnitude of seismic sources from teleseismic observations. The U.S. Geological Survey National Earthquake Information Center (NEIC) relies on in its global real‐time earthquake monitoring mission. Although waveform modeling‐based moment magnitudes are the modern standard to characterize earthquake size, is important because (1) in many cases, waveform modeling is not possible (e.g., low signal‐to‐noise events), (2) is applicable over a broad range of magnitudes, ∼M 4–7, and (3) there is a many decades‐long history of estimating magnitudes. We use the NEIC Preliminary Determination of Epicenters earthquake catalog to investigate the uncertainty in NEIC station measurements. We show that measurements are spatially correlated, which can bias event ⁠, and we describe an empirical relation between this spatial correlation and station‐to‐station distance. We further describe an approach to mitigate bias from the spatial correlation. Accounting for the spatial covariance of observations can change the event from −0.15 to 0.07 units (10th to 90th percentile) for smaller events (⁠⁠). These smaller events have the largest standard deviations ranging from 0.05 to 0.15 units (10th to 90th percentile).

The Seismic Record↗
USGS2024

Seismicity and anisotropic imaging reveal an active detachment beneath the northern Alaska Range foothills

North of the Denali Fault, the collision between the Yakutat block with North America is accommodated by a fold-thrust belt giving rise to the northern Alaska Range foothills. At the western end, the Kantishna Hills anticline hosts prominent microseismicity and surface deformation, interpreted as active folding of the Kantishna Hills anticline above a midcrustal detachment. We test for this detachment by using anisotropy-aware receiver functions to image fabric contrasts within the crust in context with seismicity. Seismic stations near the crest of the Kantishna Hills anticline and near its southern flank show a single strong contrast in dipping fabric at depths of 12–13 km near microseismicity clustering depths, consistent with a detachment plane beneath the fold. A minimum b -value at 10–13 km depth is consistent with seismicity on the detachment, compatible with the imaged anisotropic contrast, while off-fault seismicity is shallower and deeper with smaller magnitudes. South-dipping imbricate thrusts in schist characterize the northern Alaska Range foothills structure. This supports our interpretation of the observed anisotropy as reflecting SSW–SSE-dipping foliation above a detachment at ∼10–13 km depth that exploits existing crustal weaknesses along subtle fabric contrasts observed in the seismically quiescent region north of the actively deforming belt.

Alaska↗
USGS2025

New constraints on location and timing of the Great Lakes tectonic zone, central Upper Peninsula, Michigan, USA

The Great Lakes tectonic zone (GLTZ) forms the boundary between the Wawa–Abitibi and Minnesota River Valley subprovinces within the Archean Superior Province. The GLTZ is concealed for all of its 1100 km length, except for a segment in the central Upper Peninsula of Michigan. There, it is exposed as a northwest-striking mylonite zone along a 11 km segment, extending to the onlap of Paleozoic rocks to the east. Farther east, its location has been unknown. Here, we use aeromagnetic and gravity data to develop interpretations of the expression of the GLTZ and to define its extent under cover. Aeromagnetic gradients over the mylonite zone are interpreted to be produced by structurally juxtaposed rocks with varying magnetizations. Gravity data show a regional gradient along the GLTZ, produced by the juxtaposition of a dense greenstone belt on the north against lower-density gneisses and granites on the south. The GLTZ is interpreted to extend ∼55 km under cover to the east. The GLTZ is terminated on the east by the buried eastern arm of the ca. 1100 Ma Midcontinent Rift. An undeformed granitic dike that cuts the mylonitic foliation produces a U–Pb apatite age of 2523 ± 33 Ma, implying no major post-Archean shearing occurred, and is at odds with previous interpretations of major Proterozoic reactivation. A granite intrusion in the Minnesota River Valley subprovince produces a Pb–Pb zircon age of 2606.9 ± 3.6/7.4 Ma. This suggests that magmatism related to the Sacred Heart orogeny, previously known in Minnesota, extended to Michigan.

Michigan↗
USGS2026

Indirect mineral import reliance and provenance

Mineral commodity supply chain analyses rely on international trade data reported by individual countries as quantities of a mineral commodity form imported from (or exported to) a partner. However, export quantities frequently exceed a country’s domestic production, or occur when no production data are reported, suggesting that the trade partner is merely an intermediary in a transshipment. These discrepancies can result in misleading conclusions regarding supply chain vulnerabilities and dependencies. We present a two-stage methodology to reconcile gaps between reported material sources and actual producers. First, we construct trade networks for specific mineral forms, treating production as a type of import to distinguish producing nations from entrepôts. By tracing flows through these networks, we attribute a target country’s imports to original producers via both direct (in a single trade link) and indirect (transferring through intermediaries) pathways. Second, these production-attributed flows are incorporated into multi-stage supply chains to determine the upstream provenance of feedstock for domestic refining and processing. This approach provides a more representative picture of trade reliance. For example, while the United States (U.S.) Geological Survey reports no imports of unwrought antimony metal from Russia in 2022 (U.S. Geological Survey (2025). Mineral Commodity Summaries 2025. 10.3133/mcs2025), our analysis reveals that over 16% of U.S. imports can be traced back to Russian mining through intermediate processing in countries such as China, India, and Vietnam. Additionally, our analysis of the aluminum supply chain shows that while the U.S. is reported as 52% net import reliant on aluminum materials in 2022, it is 100% reliant on foreign bauxite, 7% of which arrived indirectly. This unreported reliance, which is predominantly tied to bauxite mined in Brazil (43%) and Jamaica (28%), highlights our methods ability to capture the supply chain’s dependence on foreign feedstock that may be missing in single-stage trade data.

Mineral Economics↗
USGS2024

Status and performance of the ShakeAlert® earthquake early warning system: 2019-2023

The U.S. Geological Survey (USGS)‐operated ShakeAlert® system is the United States West Coast earthquake early warning system ( Given et al. , 2018 ). In this study we detail ShakeAlert’s performance during some of the largest events seen by the system thus far. Statewide public alerting using ShakeAlert messages was authorized in California in October 2019. Over the next few years, public alerts were expanded into Oregon and then into Washington ( U.S. Geological Survey, 2024 ). ShakeAlert source results are routinely compared to the USGS Comprehensive Catalog (ComCat; Guy et al. , 2015 ; U.S. Geological Survey, Earthquake Hazards Program, 2017 ), which contains the earthquake location and magnitude determined using complete waveform data. M 4.5 and larger is the threshold used for public alerting and was deliberately set below the level where damage is likely to compensate for cases where the system underestimates the magnitude. Between 17 October 2019 and 1 September 2023, the ShakeAlert system created 95 events with maximum magnitude estimates of M ≥4.5, the public alerting threshold. 94 of the 95 events were due to real earthquakes. Seven were categorized “false” per ShakeAlert’s internal definition that there was no matching catalog event within 100 km and 30 s of origin time; however, all but one of these were real earthquakes that were poorly located, primarily because they were at the edges of the seismic network. Three detected events were labeled “missed” because they were very poorly located (>100 km location error). In addition, the system did not produce solutions for four ComCat events M ≥4.5 ( U.S. Geological Survey, Earthquake Hazards Program, 2017 ), which were all at the edge of the alerting and network boundaries. The ShakeAlert system has accurately detected the majority of earthquakes that have occurred within the operational region since completing the public rollout, and alerts from the system have been delivered to millions of cell phone users throughout the West Coast.

California, Oregon, Washington↗
USGS2025

Factors regulating the potential for freshwater mineral soil wetlands to function as natural climate solutions

There are increasing global efforts and initiatives aiming to tackle climate change and mitigate its impacts via natural climate solutions (NCS). Wetlands have been considered effective NCS given their capacity to sequester and retain atmospheric carbon dioxide (CO 2 ) while also providing a myriad of other ecosystem functions that can assist in mitigating the impacts of climate change. However, wetlands have a dual impact on climate, influencing the atmospheric concentrations of both CO 2 and methane (CH 4 ). The cooling effect associated with wetland CO 2 sequestration can be counterbalanced by the warming effect caused by CH 4 emissions from wetlands. The relative ability of wetlands to sequester CO 2 versus emit CH 4 is dependent on a suite of interacting physical, chemical, and biological factors, making it difficult to determine if/which wetlands are considered important NCS. The fact that wetlands are embedded in landscapes with surface and subsurface hydrological connections to other wetlands (i.e., wetlandscapes) that flow over and through geochemically active soils and sediments adds a new layer of complexity and poses further challenges to understanding wetland carbon sequestration and greenhouse gas fluxes at large spatial scales. Our review demonstrates how additional scientific advances are required to understand the driving mechanisms associated with wetland carbon cycling under different environmental conditions. It is vital to understand wetland functionality at both wetland and wetlandscape scales to effectively implement wetlands as NCS to maximize ecological, social, and economic benefits.

Wetlands↗
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↗