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Humpback whales produce complex, structured songs that vary among individuals, populations, and through time. This study presents a novel method of studying changes in humpback whale song elements through time while accounting for detection probability.
Hydrologic variation is a primary driver of stream ecosystems. Changing hydrology can lead to assemblage shifts and alterations in suitable habitat for freshwater species. As climate change is predicted to alter flow patterns in addition to increasing water temperatures, insight into relationships between species occupancy, hydrology, and temperature is critical for understanding current and future distributions. We examined how hydrologic variability, temperature, and other environmental variables interact to influence Micropterus dolomieu (Smallmouth Bass) occurrence. We used Spatial Stream Network models, allowing for the incorporation of spatial autocorrelation along streams' unique dendritic network, to examine Smallmouth Bass occupancy across a range of hydrologic variation in the Ozark-Ouachita Interior Highlands, USA. Hydrologic variation was the main driver of Smallmouth Bass occurrence, with occurrence more likely in groundwater streams with low hydrologic variation and high flow permanence. For groundwater streams, occurrence was positively associated with summer stream temperature and negatively associated with annual stream temperature. As variation increased, more variables showed significant relationships with occurrence. Distance metrics were important for all models, however as hydrologic disturbance increased, flow connected distance played a lesser role and stream distance played a greater role. Hydrologic variability was the overarching determinant of Smallmouth Bass occurrence and strongly influenced the predictive importance of environmental variables and geospatial relationships. Greater hydrologic variability resulted in stronger statistical relationships between occurrence and environmental variables and an increased importance of system connectivity. As climate change alters hydrologic processes and streams become more variable, understanding and accounting for these shifting relationships is essential.
Understanding the role of hydrologic variation in structuring aquatic communities is crucial for successful conservation and sustainable management of native freshwater biodiversity. Partitioning beta diversity into the additive components of spatial turnover and nestedness can provide insight into the forces driving variability in fish assemblages across stream flow regimes. We examined stream fish beta diversity across hydrologic and anthropogenic disturbance gradients using long-term (1916–2016) site occurrence records ( n = 17,375) encompassing 252 species. We assessed total beta diversity (Sørensen dissimilarity), spatial turnover, and nestedness of fish assemblages in contrasting stream flow regimes across a gradient of decreasing flow stability: groundwater stable ( n = 77), groundwater ( n = 67), groundwater flashy ( n = 175), perennial runoff ( n = 141), runoff flashy ( n = 255), and intermittent ( n = 63) streams. Differences in total beta diversity among the stream flow regimes were driven predominantly (>86 %) by spatial turnover (i.e. species replacement) as opposed to nestedness (i.e. species loss or gain). Total fish beta diversity and spatial turnover were highest in streams with intermediate flow stability (groundwater flashy), while more flow-stable streams (groundwater stable and groundwater) had lower turnover and higher nestedness. Species turnover was also strongly associated with seasonal variation in hydrology across all flow regimes, but these relationships were most evident for assemblages in intermittent streams. Distance-based statistical comparisons showed significant correlations between beta diversity and anthropogenic disturbance variables, including dam density, dam storage volume and water withdrawals in catchments of groundwater stable streams, while hydrologic variables were more strongly correlated with beta diversity in streams with runoff-dominated and flashy flow regimes. The high spatial turnover of species implies that fish conservation actions would benefit from watershed-focused approaches targeting multiple streams with wide spatial distribution, as opposed to simply focusing on preserving sites with the greatest number of species.
I develop independent logic trees for aleatory variability for crustal and subduction-zone (interface and intraslab) earthquakes for seismic hazards analyses in Puerto Rico and the U.S. Virgin Islands (PRVI) from existing suites of ground-motion models (GMMs) and from ground-motion datasets, including a regional PRVI dataset. The aleatory variability models are parameterized using a partially nonergodic partitioning of standard deviation that consists of independently developed between-event ( ), site-to-site ( ), and event-corrected single-station ( ) standard deviation components. The effects of nonlinear site response on aleatory variability are incorporated through additional terms that modify the standard deviation components. Because one goal of this work is to develop independent logic trees for aleatory variability that synthesize the aleatory variability models from GMMs, I make use of the functional forms of the input GMMs. The PRVI dataset contains a limited number of stations with high-quality site metadata and does not contain records from earthquakes with magnitudes greater than 6.1, so I choose not to develop the aleatory variability models from the regional dataset alone. Instead, the standard deviation components from regional ground-motion data are evaluated against the components derived from GMMs and from available global datasets, and regionalized standard deviation components are incorporated where there is evidence that regional effects exhibit substantial differences. The resulting logic trees for aleatory variability consist of models of and that are consistent with semiempirical GMMs for active crustal and subduction-zone regimes, and two alternative models of , including one model that exhibits site-to-site variability informed by PRVI data, with values that exceed global models. The aleatory variability models may be considered in future hazards assessments in PRVI to simplify the hazard calculations, to incorporate regional ground-motion variability effects, and to enable direct logic-tree weighs of aleatory variability.
Many people ascribe a variety of values to public lands and waters, but some values are more difficult to assess and quantify than others. Public participatory geographic information systems (PPGIS) are tools that have been used to help quantify and map the public’s diverse values for a landscape. This work describes the first known Office of Management and Budget–approved use of PPGIS by a Department of the Interior bureau. The U.S. Geological Survey developed an internet-based application to aid in gathering PPGIS data, called Values Mapping for Planning in Regional Ecosystems (VaMPIRE). Further, this work describes the first pilot of the VaMPIRE application in coordination with the Bureau of Land Management to collect spatial data and other survey data regarding the public’s uses of and values for locations within Mojave Trails National Monument. We emailed the link to the VaMPIRE application to an interested party email list in 2024 with 207 valid emails and received 74 responses; we also received 47 responses from members of an off-roading social media group. Of the list of 16 value options, recreation was the most popular value for the monument, followed by wilderness and inspirational. Over 1,000 points were placed throughout the monument, indicating locations people use or value, with the locations spread throughout the entire monument. Additionally, most survey respondents stated their ability to receive benefits in locations they mapped would not change in response to a hypothetical scenario related to recreational facility development. This report describes exploratory results from the first use of the VaMPIRE tool in Mojave Trails National Monument and includes reflections on how the process went and considerations for future use of VaMPIRE.
This paper hypothesizes that the Meteor Crater impact in Arizona, USA, 56,000 years ago triggered landslides in Grand Canyon that dammed the Colorado River and formed Nankoweap paleolake. This is compatible with shock and earthquake physics for the impact that infer a M5.4 seismic event, attenuated to an effective magnitude of M3.5 at Grand Canyon. Results that support the hypothesis include radiocarbon dating of driftwood and luminescence dating of associated slack-water lake sediments that are preserved in caves up to 60 m above the modern Colorado River. Radiocarbon ages from two locations, including Stanton’s Cave, date the driftwood as 55.25 ± 2.44 ka (n = 4). Sediments associated with the driftwood gave a luminescence age of 56.00 ± 6.39 ka (n = 2). These six Grand Canyon dates, and three published ages for the Meteor Crater impact, show statistically indistinguishable results that support the hypothesis for a geologically instantaneous series of events with a mean age of 55.60 ± 1.30 ka. This work highlights the value of radiocarbon dating near the limits of the technique, integration of multiple dating methods, and seismic and landslide hazards associated with meteorite impacts in regions of extreme topography like Grand Canyon.
Use of per- and polyfluoroalkyl substances (PFAS) in the petroleum industry could be a cause for concern due to the large volumes of produced water (PW) generated during oil and gas extraction, the reuse of these wastes in water-stressed regions, and adverse health outcomes related to PFAS exposures. However, PW PFAS characterization is nearly absent in the literature, and hydraulic fracturing (HF) chemical disclosures often omit the identities of additives as proprietary. Here we evaluate PFAS in PW samples from three petroleum wells in the Denver Basin during their first year of production. Total concentrations of targeted PFAS (Σ 40 PFAS) were < 35 ng/L in PW samples, with short-chain PFAS like perfluorobutanoic acid persisting throughout the sampled duration. Analysis of freshwater inputs for hydraulic fracturing (Σ 40 PFAS ~ 113 ng/L) and mixed fracture fluid (Σ 40 PFAS ~ 69 ng/L) indicated much of the targeted PFAS content was derived from the input water, and not from HF additives, however samples subjected to oxidation indicated the presence of PFAS precursors that would not be detected by targeted analysis. This study highlights that while PFAS content is low in the studied PWs, the potential for redistribution of PFAS in the environment may be a consideration for reuse applications.
The estimated ultimate recovery (EUR) is an important parameter for forecasting oil and gas production and informing decisions regarding field development strategies. In this study, we combined site-specific geologic, completion, and operational parameters with the predictive capabilities of machine learning (ML) models to predict EURs of the wells for the Eagle Ford Marl Continuous Oil Assessment Unit. We developed an extensive dataset of wells that have produced from the lower and upper Eagle Ford Shale intervals and reduced the model complexity using principal component analysis. We tested the ML models and estimated the sensitivities of ML-predicted EURs to changes in the values of different input variables. The results of applying the optimized ML model to the Eagle Ford suggest that the approach developed in this study could be promising. The ML estimates of the EURs fit the DCA-based values with an R 2 ~ 0.9 and a mean absolute error of ~36 × 10 3 bbl. In the lower Eagle Ford Shale, the EUR estimates were found to be most sensitive to changes in porosity, net thickness of the interval, clay volume, and the API gravity of the oil; and that in the upper Eagle Ford Shale they were most sensitive to changes in the total organic carbon and water saturation, which suggests that it could be important to consider these parameters in assessing these intervals or close analogs.
The spatial heterogeneity of soil nutrients is crucial for the water bird and whole floodplain wetland ecosystem in large lakes, and it is influenced by the dramatic water level changes and sedimentation progress in West Dongting Lake (WDL). Soil samples were collected at various soil depths along the Yuan River and Li River that feed into WDL. The concentrations of soil total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), and soil grain size were tested. The stoichiometric ratios of C, N, P, and the mean value of soil grain size ( Mz ) were calculated. The differences of soil TOC, TN, TP and the stoichiometric ratio at different sites and soil depths were compared. Linear regression was used to explore the relationships of Mz and nutrient concentrations, and relationships between TOC, TN, and TP. Redundancy analysis was used to explore the relationship between soil nutrients, heavy metal concentrations, and plant community diversity. The results showed that the distributions of soil TOC, TN, and TP concentrations differed across regions in west Dongting Lake along the Yuan and Li Rivers. Total organic carbon concentration differed at different sedimentation depths. Soil grain size showed negative effect with soil TOC, TN, and TP concentrations in this region. Plant community diversity correlated positively with soil TOC and negatively with Hg. West Dongting Lake was N limited despite the high wet deposition of N. It could potentially be attributed to the insufficient presence of aerobic environments for microbes during intermittent flooding of the floodplain, coupled with feeble mineralization. This study can provide valuable insights for the conservation of water bird habitats and wetland ecosystems.
Recent advances in machine learning (ML) identifying areas favorable to hydrothermal systems indicate that the resolution of feature data remains a subject of necessary improvement before ML can reliably produce better models. Herein, we consider the value of adding new features or replacing other, low-value features with new input features in existing ML pipelines. Our previous work identified stress and seismicity as having less value than the other feature types (i.e., heat flow, distance to faults, and distance to magmatic activity) for the 2008 USGS hydrothermal energy assessment; hence, a fundamental question regards if the addition of new but partially correlated features will improve resulting models for hydrothermal favorability. Therefore, we add new maps for shear strain rate and dilation strain rate to fit logistic regression and XGBoost models, resulting in new 7-feature models that are compared to the old 5-feature models. Because these new features share a degree of correlation with the original relatively uninformative stress and seismicity features, we also consider replacement of the two lower-value features with the two new features, creating new 5-feature models. Adding the new features improves the predictive skill of the new 7-feature model over that of the old 5-feature model; albeit, that improvement is not statistically significant because the new features are correlated with the old features and, consequently, the new features do not present considerable new information. However, the new 5-feature XGBoost model has a statistically significant increase in predictive skill for known positives over the old 5-feature model at p = 0.06. This improved performance is due to the lower-dimensional feature space of the former than that of the latter. In higher-dimensional feature space, relationships between features and the presence or absence of hydrothermal systems are harder to discern (i.e., the 7-feature model likely suffers from the “curse of dimensionality”).
Streamflows for selected flow durations from 1 to 99 percent and the August median streamflows were estimated for 11 long-term streamflow-gaging stations in and near the study area. Estimates of streamflow and associated standard errors were determined for selected flow durations from 50 to 99 percent and the August median streamflows for 21 low-flow partial-record stations and for selected flow durations from 1 to 99 percent and the August median streamflows for two partial-record stations and seven short-term discontinued streamflow-gaging stations. Median streamflows per square mile for the 10-, 50-, and 90-percent flow durations and the August median streamflows were 3.90, 1.01, 0.185, and 0.248 cubic feet per second per square mile. Streamflows per square mile at selected flow-duration discharges between 1 and 99 percent at the 41 stations were related to basin characteristics to explain differences in streamflow characteristics. Basin characteristics included basin elevations, extent of stratified-drift deposits, land use, aspect, and underlying bedrock geology types. Most streamflow differences were positively correlated to basin elevation differences, most likely because precipitation increases with elevation, and to stratified-drift deposits, which allow more precipitation to recharge the ground water and to discharge later than do till and bedrock deposits. Mean base flow was computed from continuous records of daily mean discharge at 11 long-term streamflow-gaging stations in and near the study area. Mean annual base flow ranged from 13.4 to 24.5 inches per year. Minimum annual base flow ranged from 45 to 72 percent of mean annual rates at the 11 long-term stations, and the ratio of base flow to streamflow (base-flow index) ranged from 0.55 to 0.80. Base-flow durations between 1 and 99 percent were calculated from streamflow records at the 11 long-term streamflow-gaging stations. Base flow accounted for 45.5 to 85.0 percent of total annual streamflow at the 1- and 99-percent flow durations. Ground-water-recharge rates were computed from continuous records of daily mean discharge at 11 long-term streamflow-gaging stations in and near the study area. Mean annual ground-water-recharge rates ranged from 17.5 to 22.4 inches per year at 10 of the 11 long-term stations. Mean annual ground-water-recharge rates ranged from 2 to 7 inches per year higher than base flow. Minimum annual ground-water-recharge rates ranged from 48 to 72 percent of mean annual ground-water-recharge rates. Mean annual potential ground-water recharge was estimated from monthly climatological data collected at six climatological stations in and near the study area. Mean potential ground-water recharge ranged from about 17.9 to 28.9 inches per year, with a median value of 22.6 inches per year. This median value compares well to that calculated by use of streamflow records at the 11 streamflow-gaging stations (20.0 inches per year). Streamflows per square mile for the 10-, 50-, and 90-percent flow durations at stations in and near the study area were similar to those computed for other unregulated long-term continuous streamflow-gaging stations in central and eastern Massachusetts. Base-flow and ground-water-recharge rates in the study area compared closely to results from other studies in southeastern Massachusetts and Rhode Island, which were based on the same computational methods.
Regulatory agencies in humid temperate environments rely on timely evaluations of streamflow depletion and drawdown to protect aquatic ecosystems and existing water users. Numerical models offer detailed insights, but their complexity and time demands often preclude their practical use in rapid decision-making. We present pycap-dss, an open-source Python package that implements a suite of analytical solutions for estimating streamflow depletion and drawdown. The tool supports superposition of multiple wells and time-varying pumping, enabling cumulative impact assessments in situations with multiple wells and streams. The software is modular and extensible, allowing users to interchange solutions or add new analytical methods. A YAML-based configuration supports batch processing of multiple wells, and an optional AnalysisProject class facilitates integration with regulatory workflows. Rigorous unit and regression testing ensures computational reliability, and continuous integration supports ongoing development. We demonstrate deterministic examples of drawdown where multiple solutions are readily compared and streamflow depletion with multiple wells in the Central Sands region of Wisconsin. We also show the value of Monte Carlo analyses of streamflow depletion in the same Central Sands example, leveraging computational efficiency to evaluate the uncertainty of individual and cumulative streamflow depletion calculations from over 200 high-capacity wells.
The Upper Cretaceous Tuscaloosa marine shale (TMS) potential production area encompasses 20.4 million acres across central Louisiana (LA), southern Mississippi (MS), southwestern Alabama (AL), and a small southwestern section of the Florida panhandle (Hackley et al., 2018). It remains a minor and largely undeveloped unconventional shale oil play with production from the TMS confined along the east-west LA-MS State boundary (Fig. 1). The mean undiscovered, technically recoverable resources in the TMS are estimated at 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas (Hackley et al., 2018). Geochemical analyses of source rock solvent extracts and oil samples indicate that, in the play area, the TMS is the primary source of shale oil produced from the TMS as well as of accumulated oils in the underlying conventional reservoirs of the lower Tuscaloosa (Hackley et al., 2020). The API gravity of TMS oils ranges from approximately 34 to 46 degrees (Hackley et al., 2020; Croke et al., 2020).
Although variable well log resolution and its control on saturation estimation has been studied, it has not been directly applied to a specific location to explore the nature of gas hydrate within a sand reservoir. We applied in-situ measurements of resistivities, neutron porosity, and gamma ray at two sites in the Qiongdongnan Basin, South China Sea (QDN-W05–2021 and QDN-W08–2021) to investigate the reservoir parameters of a hydrate-bearing sand reservoir. Our results show that gas hydrate is distributed in 5 zones with a total thickness of 10.7 m and an average saturation of 69% at the QDN-W05–2021 site, while they are distributed in 2 zones with a total thickness of 4.3 m and an average saturation of 49% at the QDN-W08–2021 site. We found that variances in saturations estimated from lateral-extra deep button (RX), phase shift (P40H-P40L), and attenuation (A40H-A40L) resistivities within the laterally mapped continuous sand body were affected by the nature of gas hydrate occurrences. Results indicate gas hydrate forms and accumulates at the center of the sand layer and tends to be less or not present toward the top and base. Integrated with seismic data, the in-situ measurements provide insights in the evolution of a mushroom-shaped, hydrate-gas reservoir system. In the system, free gas is likely horizontally transported from the top-center of the gas chimney to the surrounding areas in the early stage dominated by a warm-gas environment, whereas hydrate forms in the opposite pathway starting from the surrounding areas in the following stage with temperature reducing. Our study suggests that high-resolution in-situ measurements not only are a tool to identify the physical properties, but also can be used to help explain the physical process of hydrate growth and accumulation.
In this study, we apply an empirical Green’s function (eGf) method within a ground‐motion modeling framework to mitigate trade‐offs between source, path, and site effects. Many physical processes contribute to spatial variations in observed ground motions, including earthquake radiation pattern, directivity, variable path attenuation, and site effects. Current nonergodic ground‐motion models use spatially varying coefficients for path and site effects, but they do not address trade‐offs with complex earthquake source effects. To quantify the influence of directivity on ground‐motion amplitudes, we use records from multiple smaller earthquakes with epicenters near that of a larger event. We use these small magnitude events as eGfs and estimate repeatable path and site effects at individual stations, assuming that the average adjustments are not controlled by directivity. We adjust residuals from the larger earthquake using the eGf terms, isolating effects related to the rupture. This method clearly enhances the observed broadband directivity observed in the 2022 M 5.1 and 2007 M 5.4 Alum Rock earthquake ground motions, reinforcing the conclusion that their ruptures were unilateral. For the 2004 M 6.0 Parkfield earthquake, we find a bilateral rupture model better fits the data because variations in rupture velocity, slip rate, and slip distribution seem to have a stronger effect on the ground motions than rupture direction alone. Applying eGf adjustments reduces the standard deviation of the rupture models over the three earthquakes by 32% on average and by up to 57% for the 2022 Alum Rock earthquake, confirming we have effectively removed repeatable effects related to the wave propagation path and site response. We propose a novel measure of the frequency‐dependent directivity amplification strength as the reduction in ground‐motion residual variability gained by fitting a directivity model; for the three earthquakes considered, this parameter varies between 25% and 75%, indicating that directivity can strongly influence ground motions and should be considered in ground‐motion modeling.
From July to October 2022, a non-eruptive volcanic earthquake swarm occurred within ~15 km of Taʻū Island, located in eastern American Samoa. Felt reports from local residents were the only available information about the swarm when it started, as American Samoa lacked a seismic monitoring network. We developed a consistent single-station catalog for the entire swarm, using seismic data from the nearest station IU.AFI, ~250 km away. We applied the EQTransformer deep-learning model (Mousavi et al., 2020), automatically picking Pn and Sn arrivals on IU.AFI continuous data. We retained only events with Sn-Pn times of 22.5–25 seconds, consistent with the expected locations based on felt reports, then detected smaller swarm events with subsequent template-matching. This single-station catalog characterized the swarm’s onset and escalation to peak activity before a multi-agency field response team installed a local seismic network in mid-August 2022. This permanent seismic network captured the swarm’s decline. EQTransformer identified short S-P times on the first two locally deployed seismometers, both Raspberry Shake sensors, to constrain the swarm’s distance from Taʻū Island. Modern seismological processing methods, combined with basic observations such as felt reports, can quickly contribute useful information during an earthquake response in a poorly monitored region.
Wetland sedimentation and vegetation patterns at 11 highway crossings in West Tennessee were studied from 1987 to 1989. The purpose of the study was to investigate potential adverse effects of highway crossings on wetlands. Sedimentation rates, determined from root-burial depths, were highly variable. Average rates offine-grained deposition ranged from 0.005 to 0.033 foot per year for stations in locally ponded areas and from -0.002 to 0.039 foot per year for stations in drained areas. Sedimentation rates upstream from highway crossings were not significantly different from downstream rates at 8 of the 11 study sites. Three study sites had significantly greater sedimentation rates downstream. Sand splays were observed downstream from bridges at most study sites. Vegetation patterns and tree growth appear most strongly related to hydropetiod, defined as the average length of time an area is covered by water each year. The influence of sedimentation on tree growth is difficult to separate from the influence of hydroperiod because areas with high sedimentation rates typically have long hydroperiods. Estimated hydroperiod increased no more than 1 percent because of backwater from the highway crossings at the 11 study sites, while the estimated average depth of flood-plain inundation increased by an average of 6 percent.
During prolonged periods of above-average precipitation, rising groundwater levels have the potential to cause damage to and interfere with underground infrastructure and building foundations. To understand the relations between precipitation and groundwater in the vicinity of Lake Nokomis, the U.S. Geological Survey, in collaboration with the University of Minnesota, quantified five components of the groundwater budget: groundwater recharge, change in surficial aquifer storage, surficial aquifer groundwater discharge to Lake Nokomis, groundwater evapotranspiration, and groundwater discharge to underlying bedrock aquifers. Field data, geologic records, and empirical calculation methods were used to quantify groundwater budget components for April 2023 through April 2024. Lake water budget data indicate that Lake Nokomis is a flowthrough system during periods with no outflow through the weir, with groundwater inputs equal to outputs. Roughly 40 percent of precipitation that fell in the study area was added to the surficial aquifer as recharge. Uncertainty in the vertical hydraulic conductivity resulted in wide-ranging estimates (spanning three orders of magnitude) of water discharging from the surficial aquifer to the underlying bedrock aquifer. Drought conditions persisted for the duration of this study and were not representative of the conditions that motivated this study. This study is a start towards understanding relations between precipitation, Lake Nokomis levels, and groundwater levels that could affect local underground infrastructure.