SEARCH · USGS Science

Results for “Soil Dynamics and Earthquake Engineering”

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

SourceResearch connectionFrom yearThrough year

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

1,696 records · Page 13Linked to original sources

USGS2026

Assessment of extreme subsurface hydrologic conditions captured during atmospheric river storms in the San Francisco Bay area (California, USA) with applications to shallow landslide early warning

An increase in soil pore water pressure is the typical trigger for the majority of landslides caused by rainfall. Atmospheric river storms, common to the west coast of North America during the winter season, can deliver landslide triggering rainfall resulting in severe impacts to coastal communities. Using a network of hydrologic monitoring stations situated within landslide-prone terrain in the San Francisco Bay area of California (USA), we assess the meteorologic conditions and resulting hydrologic and landslide response resulting from eight consecutive storm events that caused thousands of shallow landslides during the winter of 2022–2023. We find disparate hydrological responses and resultant degrees of observed landsliding ranging from < 1 landslide/km2 to 18 landslides/km2 at the monitoring sites that reflect the interplay and differences between rainfall delivery, subsurface hydrological characteristics, and geotechnical properties at each site. Antecedent soil moisture from both early season rainfall and the first storm in the sequence played a critical role in setting up some hillslopes for failure. Subsequent storms then generated elevated pore water pressures for several hours with associated landsliding. However, we find that the occurrence of widespread landsliding required not only sufficient pore water pressure magnitude in susceptible hillslopes, but also full and prolonged effective soil saturation throughout hillslope profiles. Landslides may still occur at lower values and durations of effective saturation but are likely to be less extensive regionally. We present these findings within the context of research directions and improvements to landslide early warning systems first suggested by researchers 40 years ago.

California↗

USGS2026

Statewide surficial geologic map of Nebraska underscores Quaternary landscape evolution from the High Plains to the Central Lowland

Surficial geologic mapping in Nebraska has been conducted primarily at the 1:24,000 scale since the mid-1990s, although there have also been limited efforts to map generalized Quaternary and surficial geology within the state and the region. We compiled and evaluated disparate maps (1:24,000–1:1,000,000) and datasets—including geologic, soil and soil parent material, and geomorphic maps as well as LiDAR derivatives—to produce a single 1:500,000 scale surficial geologic map of Nebraska that is the first of its kind. This new map provides a coarse-scale surficial geologic map that will be incorporated into a nationwide U.S. Geological Survey Quaternary geologic map. It also reflects the variation and uniqueness of physical landscapes in the state, where the Great Plains and Central Lowland physiographic provinces meet, further developing a richer interdisciplinary understanding of regional geomorphology in the heart of North America.

Nebraska↗

USGS1976

Helium detection as a guide for uranium exploration

Helium, a byproduct of radioactive decay, may prove to be a valuable indicator of the presence and distribution of uranium deposits. Recent technological advances permit the development of instrumentation not previously adapted for this purpose. Commercially available equipment can provide high sensitivity at low cost and allow reasonable mobility for field use. A truck-mounted mass-spectrometer, tuned for helium-4, permits immediate adjustment or modification of sampling patterns in response to accumulating data. The inlet system of the spectrometer has been designed to allow flexibility in gas analyses from various sample types--soil gas, atmosphere, or gases in water. Sensitivity of the instrument is better than 50 parts of helium per 10 9 parts of gas. Replicate samples and standards can be analyzed in only 3 minutes. Helium in soil gas is being used for the initial evaluation of the technique. A hollow probe, as much as 2 m long, is driven into the ground; and a 10-cc syringe is used to purge the probe and extract the gas. This sample is then injected into the inlet system and introduced into the spectrometer at constant pressure. The output signal is displayed on a chart recorder. Investigations can be performed on a qualitative, relative basis, or on a quantitative basis by comparison to calibrated helium standards. Preliminary field testing includes studies of the responses to variations in wind speed, temperature, barometric pressure, moisture, and sampling depth over extended time periods, as well as studies of geologic controls on the helium content in soil gas. Surveys over known uranium occurrences reveal some anomalous helium distributions. This report describes an updated technological approach to an old idea: that of using helium, a byproduct of uranium radioactive decay, as an exploration tool for uranium. Helium is the sixth most abundant gas found in the earth's atmosphere (Table 1). It is an inert gas and very mobile, mixing rapidly. When the alpha particles from the radioactive decay of uranium and thorium pick up two electrons, atoms of the isotope helium-4 are formed. Several helium-4 atoms are produced from each decay series of U-238, U-235, and Th-232 (fig. 1). The half-lives of the parent isotopes are5also shown in figure 1. One gram of uranium will produce ~10 5 atoms of helium-4 per second, and one gram of thorium produces ~2.5 x 10 4 atoms of helium-4 per second (fig. 2). Calculations of the helium produced from the crust and mantle reveal that 1,125 x 10 30 atoms per year are produced, but only 7 x 10 30 atoms per year escape from the earth (Damon and Kulp, 1958). More is being produced than is being lost; in fact, the total atmospheric content of helium could be produced in only 2 million years (MacDonald, 1963)! However, all the crustal and mantle helium-4 does not degas into the atmosphere as it is formed. It is trapped in crystal lattices and in pore spaces within the earth. What this means is that there is excess helium-4 in the earth--excess, that is, compared to the atmospheric concentration in equilibrium with the helium-4 escape rate into space and the helium-4 flux from the crust and mantle (Nicolet, 1957). Near-surface pockets of high helium-4 concentrations are known, and some natural gas fields are so enriched that they are the source for commercial production of helium. Work by Roberts and others (1975) has shown high helium concentration in the soil gas associated with geothermal areas; and work by Clark and Kugler (1973), Dyck (1975), and Goldak (1974), for example, has noted high helium concentrations in soils and waters in the vicinity of uranium deposits. These latter studies certainly indicate the potential of helium detection for use as an exploration tool for uranium.

Open-File Report↗

USGS2026

Uncertainty quantification of geologic energy storage in depleted gas reservoirs using material balance equations embedded in a hierarchical errors-in-variables model

The storage potential for gas in geologic settings, such as depleted hydrocarbon reservoirs and solution-mined salt caverns, is becoming salient to future energy infrastructure planning. Technologies such as carbon capture, utilization, and storage, carbon dioxide-enhanced oil recovery, and natural gas and hydrogen storage help meet growing energy demands, reduce carbon emissions to meet climate goals, and provide energy security amid geopolitical uncertainties. Therefore, estimates of underground gas storage capacity could be useful for efficiently navigating the energy transitions. Material balance is a fundamental method in reservoir engineering for estimating original gas in place and potential storage capacity at the scale necessary for national assessments of subsurface pore space resources. However, the deterministic method cannot accommodate multiple data sources or quantify uncertainty in predictions. In this study, a method that embeds material balance equations within a hierarchical errors-in-variables model is proposed which allows the estimation of the distributions of reservoir properties needed for assessments. Uncertainties associated with these reservoir properties have traditionally been expert-elicited, whereas the uncertainty estimates from the proposed models are data-driven. Capacity and uncertainty estimates can be used in a probabilistic resource assessment, supplementing information traditionally used by assessors or even replacing this expert elicitation step when data are unavailable. Various regression models are compared in a case study of the Michigan Basin, a large contributor to the United States’ current natural gas storage capacity. In particular, errors-in-variables models help ameliorate regression dilution and can quantify uncertainty in predictions of pressure in addition to storage capacity. Overfitting is addressed by quantifying generalization error and model averaging in simple and stratified cross-validation against reported working gas capacity, representing the varying quality and quantity of available data. Incorporating a statistical framework into existing numerical methods in reservoir engineering can improve the quality of estimation, and in particular, this method brings rigor to uncertainty quantification as part of a larger effort by the U.S. Geological Survey to assess domestic energy gas storage resources in depleted hydrocarbon reservoirs.

Mathematical Geosciences↗

USGS2025

The δ13C signature of dissolved organic and inorganic carbon reveals complex carbon transformations within a salt marsh

Coastal wetlands have high rates of atmospheric CO 2 uptake, which is subsequently respired back to the atmosphere, stored as organic matter within flooded, anoxic soils, or exported to the coastal ocean. Transformation of fixed carbon occurs through a variety of subsurface aerobic and anaerobic microbial processes, and results in a large inventory of dissolved carbon. Carbon source and the roles of aerobic respiration, sulfate reduction, and methane cycling were evaluated within salt marsh peat and the underlying sandy subterranean estuary. There is a large increase in dissolved inorganic carbon (DIC, 7,350 ± 3,900 μmol L −1 ), dissolved organic carbon (DOC, 1,040 ± 1,480 μmol L −1 ) and CH 4 (14.5 ± 33.3 μmol L −1 ) within the marsh porewaters compared to creek waters. Alkalinity production (5,730 ± 2,170 μeq L −1 ) and sulfate removal (1,810 ± 1,970 μmol L −1 ) indicate anaerobic respiration, however, relative contributions from the various decomposition pathways cannot be identified due to overlapping geochemical signatures. The δ 13 C of the DOC (−29.0 ± 3.7‰) and DIC (−11.2 ± 1.1‰) produced within the marsh differed from the bulk soil organic matter δ 13 C (−14.5 ± 0.2‰). We explore a variety of mechanisms that could result in co-occurring depleted δ 13 C-DOC and enriched δ 13 C-DIC compared to the bulk soil organic carbon pool and salt marsh vegetation, including selective mineralization, production of δ 13 C-depleted bacterial biomass, and methane-derived DOC. While important questions remain about carbon cycling pathways, we found evidence of a cryptic methane cycle. Alteration of the δ 13 C of carbon species complicates source attribution in solid and dissolved phases and careful consideration should be used when carbon is partitioned between in situ salt marsh production and external marine and terrestrial sources.

Massachusetts↗

USGS2024

Monitoring and assessment of urban stormwater best management practices at selected Chicago public schools in Chicago, Illinois, from September 1, 2016, to July 1, 2017

The Space to Grow program helps transform aging and neglected schoolyards of Chicago Public Schools into outdoor community spaces with the goal of promoting health and learning while addressing neighborhood flooding issues. Virgil I. Grissom Elementary School and Donald L. Morrill Math and Science School were selected in 2014 for schoolyard upgrades and the installation of various green infrastructure (GI) improvements. The U.S. Geological Survey installed sensors to measure precipitation, groundwater levels, and stormwater runoff volumes from September 1, 2016, to July 1, 2017. At Virgil I. Grissom Elementary School, about 933,000 gallons of water fell on the schoolyard during the monitoring period. No discharge was recorded coming from the GI sewer lines, but backflow indicated water was flowing from the sewer line draining the impervious running track into the combined manhole structure and backwards into the GI retention basins (as designed). This design allowed for a 100-percent capture rate. Native soil at Virgil I. Grissom Elementary School also was conducive to rapid infiltration. Soil borings at Virgil I. Grissom Elementary School indicated about 10.5 feet (ft) of fine sand overlying silty clay to a depth of at least 16 ft. At Donald L. Morrill Math and Science School, about 1,120,000 gallons of water fell on the schoolyard during the monitoring period. About 72.5 precent of this water was discharged into the sewer system, and the other 27.5 percent was captured by the GI. Unlike Virgil I. Grissom Elementary School, the soil profile at Donald L. Morrill Math and Science School consisted of about 5 ft of clay loam overlying stiff blue clay to a depth of at least 12 ft. The sewer line coming from the GI under the football field was at the bottom of the reservoir. This design seemed to allow water to flow out of the line before being absorbed by the retention basin.

Illinois↗

USGS2026

Bathymetric maps, surface areas, and storage capacities of Council Grove Lake and Marion Reservoir, Kansas, and Pine Creek Lake, Oklahoma, 2024

The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, completed high-resolution multibeam bathymetric surveys to compute new elevation-area-capacity tables for Council Grove Lake and Marion Reservoir, Kansas, and Pine Creek Lake, Oklahoma. Elevation-area-capacity tables identify the relation between the water-surface elevation, surface area, and storage capacity of the lake. The surface areas and storage capacities of each lake were computed from bathymetric surfaces combining multibeam echo sounder data collected in 2024 and light detection and ranging point-cloud data collected in 2016 and 2018.

Kansas, Oklahoma↗

USGS2025

Dietary bioavailability of uranium to a model freshwater invertebrate

Uranium (U) mining increases environmental exposures. Understanding how U is taken up by organisms can aid in evaluating the potential for bioaccumulation and toxicity. Although the importance of aqueous geochemical speciation is well recognized for U bioavailability after dissolved exposures, far less is known about the processes controlling U bioavailability after dietary exposures. This study characterizes the biogeochemical drivers of dietary U uptake in the freshwater snail Lymnaea stagnalis in laboratory experiments. Solids tested included benthic diatoms pre-exposed to dissolved U(VI), soils from contaminated U mine sites, and colloidal hydrous ferric oxide (HFO) synthesized in the presence of dissolved U(VI) or with U complexed by natural organic matter (NOM). Results showed that U was bioavailable from all solids. Uranium assimilation efficiency (AE), a proxy for dietary U bioavailability, varied among solids. AE was lowest for the U-contaminated soils (25 ± 17%) and highest for the U-laden diatoms (71 ± 13%). AE varied slightly among HFO preparations, suggesting modest influences of NOM and iron on U bioavailability. Increases in dietary U exposures reduced feeding rates, and the extent of feeding inhibition appeared inversely related to U bioavailability. The high U assimilation and range of bioavailability have implications for toxicity risks inferred without considering dietary uptake.

Environmental Science and Technology↗

USGS2000

Estimating the probability of elevated nitrate (NO2+NO3-N) concentrations in ground water in the Columbia Basin Ground Water Management Area, Washington

Logistic regression was used to relate anthropogenic (man-made) and natural factors to the occurrence of elevated concentrations of nitrite plus nitrate as nitrogen in ground water in the Columbia Basin Ground Water Management Area, eastern Washington. Variables that were analyzed included well depth, depth of well casing, ground-water recharge rates, presence of canals, fertilizer application amounts, soils, surficial geology, and land-use types. The variables that best explain the occurrence of nitrate concentrations above 3 milligrams per liter in wells were the amount of fertilizer applied annually within a 2-kilometer radius of a well and the depth of the well casing; the variables that best explain the occurrence of nitrate above 10 milligrams per liter included the amount of fertilizer applied annually within a 3-kilometer radius of a well, the depth of the well casing, and the mean soil hydrologic group, which is a measure of soil infiltration rate. Based on the relations between these variables and elevated nitrate concentrations, models were developed using logistic regression that predict the probability that ground water will exceed a nitrate concentration of either 3 milligrams per liter or 10 milligrams per liter. Maps were produced that illustrate the predicted probability that ground-water nitrate concentrations will exceed 3 milligrams per liter or 10 milligrams per liter for wells cased to 78 feet below land surface (median casing depth) and the predicted depth to which wells would need to be cased in order to have an 80-percent probability of drawing water with a nitrate concentration below either 3 milligrams per liter or 10 milligrams per liter. Maps showing the predicted probability for the occurrence of elevated nitrate concentrations indicate that the irrigated agricultural regions are most at risk. The predicted depths to which wells need to be cased in order to have an 80-percent chance of obtaining low nitrate ground water exceed 600 feet in the irrigated agricultural regions, whereas wells in dryland agricultural areas generally need a casing in excess of 400 feet. The predicted depth to which wells need to be cased to have at least an 80-percent chance to draw water with a nitrate concentration less than 10 milligrams per liter generally did not exceed 800 feet, with a 200-foot casing depth typical of the majority of the area.

Washington↗

USGS2025

Understanding market sensitivity: Estimation of supply and demand elasticities for non-fuel minerals

In today’s rapidly changing economic landscape, understanding market responsiveness to price changes and the factors influencing commodity prices has become increasingly relevant. Price elasticities serve as indicators of how variations in market conditions affect supply and demand, providing insights into the sensitivity of commodity markets to price fluctuations. This paper presents a comprehensive analysis of price elasticities of supply and demand for 74 non-fuel mineral commodities including precious metals, base metals, minor metals, and industrial minerals that are utilized across various industries. We employ various econometric techniques, including fixed effects models for panel data and two-stage dynamic ordinary least squares (2S-DOLS) alongside autoregressive distributed lag (ARDL) models for time series analysis, to derive robust estimates of price elasticities. Our findings reveal variability in elasticities among different commodities and indicate that all studied mineral commodities exhibit price inelastic supply and demand in the short run, which we define as one year for the purposes of our analysis, given that the data is all annual. This research provides original estimates of price responsiveness for a wide range of commodities that have not been previously addressed in the literature, thereby enhancing the understanding of market dynamics in the mineral sector. Given that price elasticities can be influenced by factors such as market structure, technological advancements, mining costs, and industry-specific demand drivers, we use variables that serve as proxies for these factors.

Mineral Economics↗

USGS2024

Land-use interactions, Oil-Field infrastructure, and natural processes control hydrocarbon and arsenic concentrations in groundwater, Poso Creek Oil Field, California, USA

Like many hydrocarbon production areas in the U.S., the Poso Creek Oil Field in California includes and is adjacent to other land uses (agricultural and other developed lands) that affect the hydrology and geochemistry of the aquifer overlying and adjacent to oil development. We hypothesize that the distributions of hydrocarbons and arsenic in groundwater in such areas will be controlled by complex interactions between mixed land uses, oil-field infrastructure, and natural processes. In 2020–2021, samples of groundwater and surface water were collected and analyzed for a large suite of inorganic and organic chemicals and isotope and gas tracers to test this hypothesis. Those data are supplemented with ancillary data on historical geochemistry, hydrology, geology, and oil-field infrastructure. Hydrocarbons in groundwater (e.g., methane through pentane gases and benzene) are associated with natural processes (e.g., fault offsets or transition in sediment depositional environment) and oil-field infrastructure (e.g., fluid-migration pathways associated with uncemented annulus in oil wells or unlined pits). Arsenic concentrations >10 μg per liter (μg/L; maximum concentration 12.9 μg/L) are associated with natural processes in old, high-pH groundwater, and more recent recharge of water from natural and/or engineered recharge processes. Along the southwest margin of the oil field, pumping for drinking-water and irrigation supplies in combination with engineered groundwater recharge produce a depression in groundwater elevations where groundwater with elevated sulfate concentrations from agricultural areas and groundwater with hydrocarbons from the oil field mix to produce a zone of sulfate reduction that removes hydrocarbons and arsenic from groundwater but produces elevated sulfide (S 2- ) concentrations (maximum concentration 29 mg per liter, mg/L). In this study, multiple approaches were required to resolve the overlapping effects of land uses, oil-field infrastructure, and natural processes on the distributions of hydrocarbons and arsenic in groundwater. The combined use of geographic, historical, physical, chemical, isotopic, and other information to constrain processes could be a useful approach for studies in other hydrocarbon-production areas. This is particularly important where land uses affect aquifer hydrology to an extent that causes mixing of groundwaters with different chemical compositions.

California↗

USGS2024

The 3D Elevation Program—Supporting Ohio's economy

Introduction High-quality elevation data are proving to be a resource of great economic value in dealing with many important issues in Ohio. Current and accurate high-resolution elevation data support flood risk management, water quantity and quality assessment, precision farming, conservation planning, impervious-surface modeling, forest and other natural resources management, abandoned mine and geologic hazard assessment, karst mapping, and siting of wellhead pads for horizontal drilling. These data also support coastal zone management, traffic safety and preliminary engineering site-selection studies for transportation infrastructure, solar potential and other renewable energy planning, aviation safety, and identification of features of interest or concern such as archaeological sites and orphan oil and gas wells. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

Ohio↗

USGS2026

Monitoring changes in Landsat thermal features in urban and non-urban interfaces from 1986 to 2023 in two international urban centers: Implications for climate and global issues

Rapid urbanization is reshaping thermal environments worldwide, with the strongest impacts occurring at the interface between urban and non-urban areas. Impervious surfaces, as key indicators of urban expansion, are critical for monitoring urban growth and assessing surface urban heat island (SUHI) effects. Land use and land cover change (LULCC) provides an essential link between urban dynamics and their environmental and societal consequences. Here, we integrated the U.S. Geological Survey (USGS) Climate Global Issues (CGI) Land Cover Product with Landsat thermal time-series to investigate SUHI evolution in two contrasting metropolitan regions: Wuhan, China, and Brasília, Brazil. Using data spanning 1986–2023, we analyzed the relationships between land cover, Landsat-based land surface temperature (LST), and SUHI intensity, and identified persistent thermal hotspots. Results demonstrate that the land cover data utilized increases the accuracy of impervious surface mapping along urban–rural gradients. Average SUHI intensities were 3.4 °C in Wuhan and 3.3 °C in Brasília, with statistically significant warming trends of 0.04 °C/year and 0.01 °C/year, respectively. Maximum temperature proved to be a robust indicator of SUHI intensification, capturing long-term upward trends. Our findings highlight the important role of urban land cover dynamics in shaping temporal SUHI variability and hotspot emergence. This prototype framework demonstrates the scientific and policy value of combining long-term land cover monitoring information with satellite thermal monitoring to quantify and track SUHI at city scale, supporting sustainable urban planning and climate adaptation strategies.

Remote Sensing↗

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

A conceptual framework to assess post-wildfire water quality: State of the science and knowledge gaps

Wildfire substantially alters aquatic ecosystems by inducing moderate to catastrophic physical and chemical changes. However, the relations of environmental and watershed variables that drive those effects are complex. We present a Driver-Factor-Stressor-Effect (DFSE) conceptual framework to assess the current state of the science related to post-wildfire water-quality. We reviewed 64 peer-reviewed papers using the DFSE framework to identify drivers, factors, stressors, and effects associated with each study. A total of five drivers were identified and ranked according to their frequency of occurrence in the literature: atmospheric processes > fire characteristics > ecologic processes and characteristics > land surface characteristics > soil characteristics. Commonly reported stressors include increased nutrients, runoff, and sediment transport. Furthermore, although several different factors have been used at least once to explain water-quality effects, relatively few factors outside of precipitation and fire characteristics are frequently studied. We identified several gaps indicating the need for long-term monitoring, multi-factor studies, consideration of organic contaminants, consideration of groundwater, and inclusion of soil characteristics. This assessment expands on other reviews and meta-analyses by exploring causal linkages between influential variables and overall effects in post-wildfire watersheds. Information gathered from our assessment and the framework itself can be used to inform future monitoring plans and as a guide for modeling efforts focused on better understanding specific processes or to mitigate potential risks of post-wildfire water quality.

Water Resources Research↗

USGS2026

Microclimate mediates the strength and direction of avian biotic interactions

Theory predicts that that the strength and direction of species interactions can shift from being competitive in benign environments toward being facilitative in stressful environments. However, the environmental context dependency of species interactions has rarely been tested in animal communities. We capitalized on a 15-year, landscape-scale dataset, collected annually in a relatively stable old-growth forest environment to test the long-held hypothesis that the strength and direction of species interactions might be mediated by climatic conditions. It is generally accepted that competitive and facilitative interactions drive the distributions of many species. Using multi-species dynamic occupancy models applied to long-term data, we tested whether annual settlement by bird species could affect either the persistence or settlement by other phylogenetically related species, and whether these interactions are mediated by microclimate. We found that species interactions were influenced by microclimate for some, but not all avian species pairs. Related species pairs more often showed settlement dynamics that were indicative of attraction rather than repulsion. In some cases, competitive interactions at warmer microclimates flipped to become facilitative in colder ones. However, the reverse was also true: facilitative interactions amplified for some species under warm conditions. Furthermore, species pairs that were closely related were more likely to exhibit competitive relationships along at least part of the microclimatic gradient. Our results highlight the importance of using long-term data to incorporate competitive and facilitative interactions into species distribution models and support the notion that the strength and direction of species interactions can be dependent on microclimatic environmental conditions.

Oregon↗

USGS2024

RegionGrow3D: A deterministic analysis for characterizing discrete three-dimensional landslide source areas on a regional scale

Regional-scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics-based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three-dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad-scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km 2 ) using 3D, limit-equilibrium (LE)-based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back-calculated from inventoried landslides using 3D LE-based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA.

Journal of Geophysical Research: Earth Surface↗

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↗