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USGS2024

New 10Be-26Al isochron burial dating informs the Pliocene and Pleistocene evolution of the lower Colorado River, southwestern United States

Four new 10 Be- 26 Al isochron burial ages ranging from 4.4 to 2 Ma on ancestral Colorado River deposits in the lower Colorado River corridor (LCRC) help constrain the river’s evolution during the Pliocene and early Pleistocene. They help fill a gap between previous work that focused on older and younger deposits: Older dated deposits include the 5 Ma Bouse Formation, which records the integration of the Colorado River through a series of preexisting basins to the Gulf of California and the ca. 4.5 - 3.5 Ma Bullhead Alluvium, a 200 to 300 m thick aggregational package that immediately followed integration. The much younger, 100 - 70 ka, Chemehuevi Formation is another major aggradation package mapped throughout the LCRC. The new burial ages on the facies of Santa Fe Railway (4.37 ± 0.71 Ma), boulder conglomerate of Bat Cave Wash (2.12 ± 0.26 and 2.05 ± 0.31 Ma), and the Palo Verde alluvium (3.03 ± 0.26 Ma) partially fill in a 3.5 M.y. gap between the deposition of the Bullhead Alluvium and the Chemeheuvi Formation and document the timescales over which the Colorado River was able to remove the Bullhead aggradational package and initiate newer and smaller aggradational pulses.

Arizona, California, Nevada↗

USGS2024

Comparison of water quality in shallow groundwater near agricultural areas in the Delaware Coastal Plain, 2014 and 2019

The State of Delaware has encouraged agricultural conservation practices to improve nutrient uptake by crops and mitigate nutrient transport to groundwater in the surficial aquifer. To study recent changes in groundwater quality, the U.S. Geological Survey and the Delaware Department of Agriculture (DDA) developed a network of shallow wells near agricultural areas throughout the Delaware Coastal Plain. This network was designed to characterize water quality related to agricultural practices and to detect any recent changes in shallow groundwater quality, in particular groundwater nitrate concentrations. The shallow well network was first sampled in 2014 and resampled in 2019. In 2019, field parameters (including dissolved oxygen, pH, specific conductance, and temperature), major ions, nutrients, stable isotopes of water, and isotopes of nitrate were measured in groundwater samples collected between October and December. Wells were organized into three groups based on their geochemical characteristics measured in 2014: the Agricultural, Urban, and Mixed Groups. Results from the 2019 sampling showed little change in water quality from the 2014 sampling. Land-use factors continued to be the driving influence between groups. Groundwater moves slowly and changes in groundwater quality are likely to respond slowly to changes in conservation practices. Continued sampling of both groundwater quality in this network and monitoring land management practices can help detect groundwater quality trends in the future.

Delaware↗

USGS2026

Patterns and drivers of cliff erosion in Big Sur, California, USA using repeat photogrammetry, 2017–2023

Seacliff erosion in steep terrain poses major risks to transportation and critical infrastructure. In Big Sur, California, USA, seacliff erosion threatens the sustainability of the central coast stretch of California State Route 1, a transportation corridor that is critical to the region's economy. Published cliff retreat rates for the region range from 1 to 40 cm yr −1 , highlighting that high-resolution, process-based studies could enhance understanding of the causes of spatial and temporal variability. We quantified cliff erosion and investigated its drivers along ∼13 km of the Big Sur coastline at week–month timescales during the late fall to early spring wet seasons between January 2017 and June 2023 by analyzing 3D point clouds developed from aerial imagery using four-dimensional structure-from-motion (4D SfM) photogrammetry techniques. We calculated cliff face retreat rates of 2.23 ± 3.06 cm yr −1 (mean ±1 σ ), an order of magnitude lower than long-term estimated rates for the region (which included large deep-seated landslides), but in line with short-term rates reported across California. Change detection imagery comparison, cliff profiles through time, and statistical analysis reveal a cyclical cliff evolution process in which erosion by wave action at the cliff base destabilizes the cliff and primes it for subsequent failure during precipitation events. Although more erosion by volume could be attributed to precipitation-induced increases in soil moisture (784 m 3 km −1 yr −1 ) compared with erosion attributed to wave power (282 m 3 km −1 yr −1 ), our observations underscore the coupled nature of these processes in driving cliff evolution, consistent with established theory and observations.

California↗

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↗

USGS2025

Estimating the probability of export restrictions to inform mineral criticality

As demand for advanced technologies rises, mineral commodities will increase in geopolitical importance. To assess risks associated with mineral commodity supply chain disruptions, governmental agencies and others have developed "criticality" assessments, with criticality described using the economic impact and probability of supply chain disruptions. In previous work, subjective supply risk indicators were developed to approximate this probability, typically combining several factors such as supply diversity and political stability of trading partners, where indicator weightings can substantially impact results. This work explicitly quantifies trade barrier probability using an ensemble of several machine learning classifiers, with probability estimates informed by exogenous variables such as prior trade barrier implementation and global export dominance. Major differences in the high-probability countries and commodities are observed across models, but the ensemble method highlights Indonesia, China, Tanzania, and the United States as particularly high risk. This approach enables a direct, quantitative, objective approach to assessing trade barrier probability, enhancing risk identification and prioritization for policymakers.

SSRN↗

USGS2024

Biogeochemical and community ecology responses to the wetting of non-perennial streams

Transitions between dry and wet hydrologic states are the defining characteristic of non-perennial rivers and streams, which constitute the majority of the global river network. Although past work has focused on stream drying characteristics, there has been less focus on how hydrology, ecology and biogeochemistry respond and interact during stream wetting. Wetting mechanisms are highly variable and can range from dramatic floods and debris flows to gradual saturation by upwelling groundwater. This variation in wetting affects ecological and biogeochemical functions, including nutrient processing, sediment transport and the assembly of biotic communities. Here we synthesize evidence describing the hydrological mechanisms underpinning different types of wetting regimes, the associated biogeochemical and organismal responses, and the potential scientific and management implications for downstream ecosystems. This combined multidisciplinary understanding of wetting dynamics in non-perennial streams will be key to predicting and managing for the effects of climate change on non-perennial ecosystems.

Nature Water↗

USGS2026

Localization of spatiotemporally heterogeneous subsurface flows using autoencoder-based deep learning framework for time-lapse self-potential tomography

Self-potential (SP) monitoring has emerged as a valuable method for characterizing subsurface hydrogeological features and processes due to its sensitivity to fluid-induced electrokinetic effects. Despite advancements in SP inversion, challenges remain in imaging groundwater dynamics from SP activities due to complex hydrological settings and transient noise. In this study, a deep learning autoencoder (AE)-based framework is proposed for the spatiotemporal localization of subsurface fluid movement from time-lapse SP tomography. Temporal segments of time-lapse numerical inversions were first derived from long-term SP monitoring conducted from a floodplain site in Oak Ridge, Tennessee, known for active hyporheic exchange. Subsequently, AE models based on vision transformer (ViT), convolutional long short-term memory (ConvLSTM), convolutional neural network, and temporal convolutional network were individually trained and compared on the SP tomography segments for reconstruction performance. Finally, the reconstruction error over time serves as an anomaly score to identify moments of active SP variation, whereas spatial distributions of errors within these moments are analyzed to image and localize regions associated with anomalous subsurface fluid movement. The results demonstrate that ConvLSTM- and ViT-AE are most capable for the localization task with contrasting error distributions and consistent delineation of anomalies. Applying the method to both SP arrays parallel and perpendicular to the stream produced consistent anomaly zones near a fault or karst feature, validating the robustness and generalization of the approach. These results demonstrate the potential of the proposed framework as a scalable and interpretable tool for spatiotemporal analysis of subsurface flow dynamics in complex hydrogeological systems.

Tennessee↗

USGS2024

Onset of aftershocks: Constraints on the Rate-and-State model

Aftershock rates typically decay with time t after the mainshock according to the Omori–Utsu law, R (t)=K(c+t) −p ⁠ , with parameters K , c , and p . The rate‐and‐state (RS) model, which is currently the most popular physics‐based seismicity model, also predicts an Omori–Utsu decay with p = 1 and a c ‐value that depends on the size of the coseismic stress change. Because the mainshock‐induced stresses strongly vary in space, the c ‐value should vary accordingly. Short‐time aftershock incompleteness (STAI) in earthquake catalogs has prevented a detailed test of this prediction so far, but the newly developed a ‐positive method for reconstructing the true earthquake rate now allows its testing. Using previously published slip models, we calculate the coseismic stress changes for the six largest mainshocks in Southern California in recent decades and estimate the maximum shear as a scalar proxy of the coseismic stress tensor. Aftershock rates reconstructed for events in different stress ranges show that the rates follow a power law with p = 1 independent of stress with no clear sign of a c ‐value. The onset of the power‐law decay is abrupt and more delayed in areas with smaller stress changes. The observations do not necessarily contradict the RS model, as STAI limits the resolution for early aftershocks, and the RS model can reproduce the observations for specific Aσ values. However, the observations lead to strong constraints, namely Aσ <10 kPa and a power‐law decay of the background rate with distance to the fault, with exponent 2.7.

Seismological Research Letters↗

USGS2025

Characterizing the scale of regional landslide triggering from storm hydrometeorology

Rainfall strongly affects landslide triggering; however, understanding how storm characteristics relate to the severity of landslides at the regional scale has thus far remained unclear, despite the societal benefits that would result from defining this relationship. As mapped landslide inventories typically cover a small region relative to a storm system, here we develop a dimensionless index for landslide-inducing rainfall, A * , based on extremes of modeled soil water relative to its local climatology. We calibrate A * using four landslide inventories, comprising over 11 000 individual landslides over four unique storm events, and find that a common threshold can be applied to estimate regional shallow-landslide-triggering potential across diverse climatic regimes in California (USA). We then use the spatial distribution of A * , along with topography, to calculate the landslide potential area (LPA) for nine landslide-inducing storm events over the past 20 years, and we test whether atmospheric metrics describing the strength of landfalling storms, such as integrated water vapor transport, correlate with the magnitude of hazardous landslide-inducing rainfall. We find that although the events with the largest LPA do occur during exceptional atmospheric river (AR) storms, the strength of landfalling atmospheric rivers does not scale neatly with landslide potential area, and even exceptionally strong ARs may yield minimal landslide impacts. Other factors, such as antecedent soil moisture driven by storm frequency and mesoscale precipitation features within storms, are instead more likely to dictate the patterns of landslide-generating rainfall throughout the state.

California↗

USGS2024

Rainfall intensification amplifies exposure of American Southwest to conditions that trigger postfire debris flows

Short-duration, high-intensity rainfall can initiate deadly and destructive debris flows after wildfire. Methods to estimate the conditions that can trigger debris flows exist and guidance to determine how often those thresholds will be exceeded under the present climate are available. However, the limited spatiotemporal resolution of climate models has hampered efforts to characterize how rainfall intensification driven by global warming may affect debris-flow hazards. We use novel, dynamically downscaled (3.75-km), convection-permitting simulations of short-duration (15-min) rainfall to evaluate threshold exceedance for late 21st-century climate scenarios in the American Southwest. We observe significant increases in the frequency and magnitude of exceedances for regions dominated by cool- and warm-season rainfall. We also observe an increased frequency of exceedance in regions where postfire debris flows have not been documented, and communities are unaccustomed to the hazard. Our findings can inform planning efforts to increase resiliency to debris flows under a changing climate.

Arizona, California, Nevada, New Mexico, Utah↗

USGS2026

Apatite (U-Th)/He thermochronology from Marsh Creek anticline reconciles Cenozoic and Holocene strain patterns and elucidates the Cenozoic canning displacement zone of Arctic Alaska

The northeastern Brooks Range is a vast fold-thrust belt that records the northernmost expression of Cenozoic deformation in Alaska, likely with complex dynamic linkages to distant tectonic elements. The principal focus of this manuscript is the Marsh Creek anticline (MCA), which is a large and recently active structure within the broader northeastern Brooks Range. The MCA is a complex, polyphase structure mostly buried beneath the Arctic coastal plain. We present apatite (U-Th)/He (AHe) dates from six outcrop samples, all near the structural crest of the anticline, as well as supporting detrital zircon U/Pb (ZUPb) age spectra from five outcrop samples. The AHe sample transect spans ~3 km of structural relief. The four shallowest AHe samples are not reset. However, the two deepest samples exhibit low intrasample variability among replicates and strong date versus grain radius correlations, suggesting post-depositional burial heating to within the AHe partial retention zone. Inverse thermal history modeling of these data resolves a protracted phase of exhumational cooling that began around 33–28 Ma. Detrital ZUPb age spectra from Paleogene strata support an interpretation of diverse inherited thermal histories recorded by the AHe data, and they are also consistent with extant reconstructions of Paleogene sediment dispersal. Placed into regional context, the results constrain a phase of increasing structural relief in the northeastern Brooks Range fold-thrust belt that began by the Eocene. The relief developed in a zone characterized by diminishing structural relief to the west called the Canning displacement zone. Although this zone was active since the Eocene, several structures including the studied part of the MCA were rapidly exhumed in the Oligocene. Lastly, we argue for kinematic compatibility between Cenozoic contraction and active strike-slip in the northeastern Brooks Range, with the temporal change in strain style likely reflecting the increase in structural relief and topography.

Alaska↗

USGS2000

Ground-water quality atlas of Oakland County, Michigan

The U.S. Geological Survey (USGS), in cooperation with Oakland County Health Division (OCHD), collected 140 water samples from 38 wells in Oakland County during 1998 to better understand ground-water quality. OCHD had observed temporal variations in concentrations of various constituents, so two additional sets of samples were collected to evaluate potential short-term variability related to sample collection procedures and long-term seasonal variability. Replicate samples from 28 wells were analyzed in the Michigan Department of Environmental Quality (MDEQ) Drinking Water Laboratory to compare MDEQ’s analytical results to those obtained from the USGS National Water Quality Laboratory. Several additional databases describing population, land use, water supply, soils, geology, and flows of ground water and surface water are presented in the first part of the report to assist in interpreting the water-quality data. Maps created from these databases are provided in the first portion of the report as an extension of the study-area description. The U.S. Environmental Protection Agency (USEPA) has established Maximum Contaminant Levels (MCL) and Secondary Maximum Contaminant Levels (SMCL) for which samples were analyzed in this study. Water from the 38 wells sampled by the USGS did not exceed the SMCL or MCL for sulfate, fluoride, or nitrite. However, water from 26 wells exceeded the SMCL for iron, water from 12 wells exceeded the SMCL for manganese, and water from 12 wells exceeded the SMCL for dissolved solids. Water from two wells exceeded the MCL for nitrate, although nitrate concentrations in water from most wells was below the detection limit. Water from seven wells exceeded the SMCL for chloride, and water from all wells contained detectable concentrations of chloride. Water from five wells exceeded the MCL for arsenic, and most of the wells sampled contained detectable concentrations of arsenic. These five wells were identified from previous MDEQ analyses to have elevated arsenic concentrations, and were sampled to obtain additional chemistry information. Replicate samples were collected from 26 of the 38 wells for analysis at the MDEQ Drinking-Water Laboratory to compare the results with the USGS National Water Quality Laboratory. The results of the replicate analyses indicate close agreement between the laboratories, with mean differences for nitrate, chloride, and arsenic of 0.10 milligrams per liter (mg/L) as nitrogen, 6.8 mg/L, and 0.0008 mg/L, respectively between the USGS and MDEQ analyses. Potential health effects associated with ingesting nitrate, chloride, and arsenic are provided with the water-quality data, along with references for further information.

Michigan↗

USGS2025

False positives in the identification of dynamic earthquake triggering

Dynamic earthquake triggering is commonly identified through the temporal correlation between increased seismicity rates and global earthquakes that are possible triggering events. However, correlation does not imply causation. False positives may occur when unrelated seismicity rate changes coincidently occur at around the time of candidate triggers. We investigate the expected false positive rate in Southern California with global M ≥ 6 earthquakes as candidate triggers. We compute the false positive rate by applying the statistical tests used by DeSalvio and Fan (2023), https://doi.org/10.1029/2023jb026487 to synthetic earthquake catalogs with no real dynamic triggering. We find a false positive rate of ∼3.5%–8.5% when realistic earthquake clustering is present, consistent with the 95% confidence typically used in seismology. However, when this false positive rate is applied to the tens of thousands of spatial-temporal windows in Southern California tested in DeSalvio and Fan (2023), https://doi.org/10.1029/2023jb026487 , thousands of false positives are expected. The expected false positive occurrence is large enough to explain the observed apparent triggering following 70% of large global earthquakes (DeSalvio & Fan, 2023, https://doi.org/10.1029/2023jb026487 ), without requiring any true dynamic triggering. Aside from the known triggering from the nearby El Mayor-Cucapah, Mexico, earthquake, the spatial and temporal characteristics of the reported triggering are indistinguishable from random false positives. This implies that best practice for dynamic triggering studies that depend on temporal correlation is to estimate the false positive rate and investigate whether the observed apparent triggering is distinguishable from the correlations that may occur by chance.

JGR Solid Earth↗

USGS2024

Layered intrusions in the Precambrian: Observations and perspectives

Layered intrusions are plutonic bodies of cumulates that form by the crystallization of mantle-derived melts. These intrusions are characterized by igneous layering distinguishable by shifts in mineralogy, texture, or composition. Layered intrusions have been fundamental to our understanding of igneous petrology; however, it is their status as important repositories of critical metals – such as platinum-group elements, chromium, and vanadium – that has predominantly driven associated research in recent decades. Many layered intrusions were emplaced during the Precambrian, predominantly at the margins of ancient cratons during intervals of supercontinent accretion and destruction. It appears that large, layered intrusions require rigid crust to ensure their preservation, and their geometry and layering is primarily controlled by the nature of melt emplacement. Layered intrusions are best investigated by integrating observations from various length-scales. At the macroscale, intrusion geometries can be discerned, and their presence understood in the context of the regional geology. At the mesoscale, the layering of an intrusion may be characterized, intrusion-host rock contact relationships studied, and the nature of stratiform mineral occurrences described. At the microscale, the mineralogy and texture of cumulate rocks and any mineralization are elucidated, particularly when novel microtextural and mineral chemical datasets are integrated. For example, here we demonstrate how mesoscale observations and microscale datasets can be combined to understand the petrogenesis of the perplexing snowball oiks outcrop located in the Upper Banded Series of the Stillwater Complex. Our data suggest that the orthopyroxene oikocrysts did not form in their present location, but rather formed in a dynamic magma chamber where crystals were transported either by convective currents or within crystal-rich slurries. Critical metals may be transported to the level of a nascent intrusion as dissolved components in the melt. Alternatively, ore minerals are entrained from elsewhere in a plumbing system, potentially facilitated by volatile-rich phases. There are many ore-forming processes propounded by researchers to occur at the level of emplacement; however, each must address the arrival of the ore mineral, its concentration of metals, and its accumulation into orebodies. In this contribution, several of these processes are described as well as our perspectives on the future of layered intrusion research.

Precambrian Research↗

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↗

USGS2012

Development of vegetation based soil quality indices for mineralized terrane in arid and semi-arid regions

Soil quality indices (SQIs) are often management driven and attempt to describe key relationships between above- and below-ground parameters. In terrestrial systems, indices that were initially developed and modified for agroecosystems have been applied to non-agricultural systems in increasing number. We develop an SQI in arid and semi-arid ecosystems of the Western US impacted by different types of geologic mineralization using the relationship between vegetation community parameters and soil abiotic and biotic properties. We analyze these relations in soils associated with three different mineralization types: podiform chromite, Cu/Mo porphyry, and acid-sulfate gold vein systems at four different sites in California and Nevada. Soil samples were collected from undisturbed soils in both mineralized and nearby unmineralized substrates as well as from waste rock and tailings. Aboveground net primary productivity (ANPP), canopy cover and shrub density were measured for the vegetative communities. Minimum data sets were developed based on correlations between the soil and vegetation parameters, refined using principal components analysis, scored using non-linear functions, and combined into an overall SQI. The indices are comprised of one or two microbial parameters and three to six abiotic parameters, the latter consisting of nutrients and metals. Given the preliminary development of this approach, the parameters and combinations to arrive at an SQI for a given site cannot at this time be correlated or compared with that of another site. This SQI approach provides a means of quantifying disturbed ecosystem recovery resulting from mining, and could be applied to other disturbances in a way that readily distills the information for potential use by land managers. However, severely disturbed areas with little to no aboveground biomass, such as unreclaimed tailings, have likely crossed an ecological threshold that precludes the use of this type of monitoring tool.

California↗

USGS2026

Distributed volcanic fields—Patterns, products, processes, and plumbing

Distributed volcanic fields—also referred to as “monogenetic fields” or simply “volcanic fields”—are found on every continent, on the seafloors, and in every tectonic setting on Earth. Yet, our understanding of what constitutes a distributed volcanic field, and our knowledge of the common products, spatiotemporal patterns in activity, and plumbing systems of these fields, are commonly poor and (or) debated. Here we provide an overview of the current understanding of distributed volcanic fields (DVFs) through review and compilation of published data, with an emphasis on understanding the characteristics of fields that are essential to prepare for future hazardous volcanic activity. Distributed fields can erupt restricted to variable magma compositions. Activity in these fields produces nearly all types of volcanoes, including scoria and spatter cones, maars, low shields, lava fields, stratovolcanoes, and calderas, but monogenetic volcanoes are the defining characteristic of DVFs. The number of vents and lifespans of DVFs vary greatly; vent numbers range from 2 to greater than 1,000, and fields can remain active for 150,000 to 10 million years. Some characteristics of DVFs appear field dependent. Although the volumes of single monogenetic eruptions within DVFs globally are variable (from less than 0.1 to a few cubic kilometers), each field may have a characteristic range in eruption volumes. Eruptions within distributed volcanic fields can have a random distribution and timing, but spatial and (or) temporal vent clustering and alignment are common, and eruptions may be time or volume predictable. Average eruption recurrence intervals in DVFs span over an order of magnitude, from less than 3 to greater than 100 thousand years. Exposures of the shallow portions of older systems reveal that many magma batches end their ascent before reaching the surface, with some dikes stopping within only meters of the surface. Petrologic insights into the deeper plumbing systems of DVF volcanoes indicate variable depths of magma crystallization and (or) stalling, and that magma transport and storage systems may be unique to a particular DVF and its underlying crustal structure. Both seismic and petrologic data for mafic DVF eruptions corroborate intrusions of magma into storage systems in the months to years before eruption, as well as variable ascent rates through the crust (days to weeks). Few distributed fields are closely monitored, as they erupt infrequently, but these observations imply that the amount of time available to install monitoring instrumentation after the first felt seismicity may be short, as precursory activity is frequently on the order of days to months for modern DVF eruptions. Satellite-based geodesy may provide useful warning in such cases. This chapter concludes with outstanding questions and areas for future research that will aid in our understanding and preparedness for future DVF eruptions.

Professional Paper↗

USGS2025

Beyond the mangroves: A global synthesis of tidal forested wetland types, drivers and future information opportunities

There is increasing awareness of the global diversity of tidal forested wetlands (TFWs) and their significance in the provision of ecosystem services. These ecosystems, including mangrove forests, tidal freshwater forested wetlands, supratidal forests and transitional forests together span multiple climatic zones, geomorphic settings, and inundation and salinity regimes. We utilise case studies across five continents to demonstrate the state of knowledge among TFWs. Intertidal mangroves are the best-defined of the TFWs thanks to decades of research on their geomorphology, hydrology and ecology across their broad distribution. Non-mangrove forest settings, however, demonstrate more diverse hydrological, biochemical and vegetation conditions. In many cases, non-mangrove forests are situated at upper intertidal or supratidal elevations, where surface waters and groundwater are subject to interactions between tides freshwater inputs. Salinity datasets show variations ranging from tidal freshwater forested wetlands and ‘low-salinity mangroves’ to mesohaline or marine salinities, often with high temporal variability. While the floristic composition of non-mangrove forests vary among biogeographic regions, locally dominant TFW species are commonly distributed beyond the tidal niche into non-tidal wetland and upland forests. This presents challenges for traditional remote sensing approaches to ecosystem mapping, which are mostly lacking for non-mangrove forests. Geomorphic approaches and developments in machine learning offer opportunities to address this.

Preprint↗