The application of Long Short-Term Memory (LSTM) models for streamflow predictions has been an area of rapid development, supported by advancements in computing technology, increasing availability of spatiotemporal data, and availability of historical data that allows for training data-driven LSTM models. Several studies have focused on improving the performance of LSTM models; however, few studies have assessed the applicability of these LSTM models across different hydroclimate regions. This study investigated the single-basin trained local (one model for each basin), multi-basin trained regional (one model for one region), and grand (one model for several regions) models for predicting daily streamflow in water-limited Great Basin (18 basins) and energy-limited New England (27 basins) regions in the United States using the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) data set. The results show a general pattern of higher accuracy in daily streamflow predictions from the regional model when compared to local or grand models for most basins in the New England region. For the Great Basin region, local models provided smaller errors for most basins and substantially lower for those basins with relatively larger errors from the regional and grand models. The evaluation of one-layer and three-layer LSTM network architectures trained with 1-day lag information indicates that the addition of model complexity by increasing the number of layers may not necessarily increase the model skill for improving streamflow predictions. Findings from our study highlight the strengths and limitations of LSTM models across contrasting hydroclimate regions in the United States, which could be useful for local and regional scale decisions using standalone or potential integration of data-driven LSTM models with physics-based hydrological models.
In a conservation context, identifying key habitats suitable for reproduction, foraging, or survival is a useful tool, yet challenging for species with large geographic distributions and/or living in remote regions.
The objective of this study is to identify selected habitats at multiple levels and scales of the threatened eastern North American population of golden eagles (Aquila chrysaetos). We studied habitat selection at three levels: landscape (second order of selection), foraging (third order of selection), and nesting (fourth order of selection).
Using tracking data from 30 adults and 366 nest coordinates spanning over a 1.5 million km2 area in remote boreal and Arctic regions, we modelled the three levels of habitat selection with resource selection functions using seven environmental features (aerial, topographical, and land cover). We then calculated the relative probability of selection in the study area to identify regions with higher probabilities of selection.
Eagles selected more for terrain ruggedness index and relative elevation than land cover (i.e., forest cover, distance to water; mean difference in relative selection strength: 1.2 [0.71; 1.69], 95% CI) at all three levels. We also found that the relative probability of selection at all three levels was ~ 25% higher in the Arctic than in the boreal regions. Eagles breeding in the Arctic travelled shorter foraging distances with greater access to habitat with a high probability of selection than boreal eagles.
Here we found which aerial and topographical features were important for several of the eagles’ life cycle needs. We also identified important areas to monitor and preserve this threatened population. The next step is to quantify the quality of habitat by linking our multi-level, multi-scale approach to population demography and performance such as reproductive success.
The location of estuarine organisms varies based on geophysical cycles and environmental conditions, which can strongly bias understanding of organism abundance and distribution. In the San Francisco Estuary, California, extensive monitoring surveys have provided insight into the life history and ecology of certain commercially important or legislatively protected fish species. However, there remains substantial uncertainty in factors influencing the vertical and lateral distributions of many other nekton species in the San Francisco Estuary, including longfin smelt Spirinchus thaleichthys, for whom such distributional information may highly influence interpretation of existing data. We carried out paired sampling using surface and demersal gears to address three questions: (1) Does diel phase influence the vertical position of nekton (e.g., surface versus demersal)? (2) Do environmental conditions, specifically turbidity, influence the vertical and lateral positions of nekton (e.g., center channel versus peripheral shoal)? (3) Does tidal variability influence vertical and lateral distributions of nekton? We documented variability in sampled nekton densities across diel phase (day/night), vertical position (surface/bottom), and lateral position (channel/shoal). Tidal phase and turbidity concentration influenced vertical and lateral distributions for some species at certain locations. Although infrequently encountered, we documented associations of longfin smelt with the lower water column and shoal habitats, with some evidence for upward vertical shifts in low light conditions brought about by nightfall or elevated turbidity. Observed habitat associations provide insight into how interacting geophysical and environmental factors may influence the distribution of nekton and thus the vulnerability of individual species to detection by sampling gears.
The risk of lampricide applications (such as 4-nitro-3-[trifluoromethyl]phenol [TFM]) to nontarget fauna continues to be a concern within the Great Lakes Fishery Commission Sea Lamprey Control Program, especially among imperiled aquatic species—such as native freshwater mussels. The Grand River (Ohio, USA) is routinely treated for larval sea lampreys (Petromyzon marinus), and this river contains populations of the federally threatened mussel Obovaria subrotunda. Given this spatial overlap, information on the sensitivity of O. subrotunda to TFM is needed. Our objectives were to assess the toxicity of TFM to (1) adult Obovaria olivaria (a surrogate for O. subrotunda), (2) glochidial larvae of O. olivaria and O. subrotunda, (3) juveniles of O. olivaria and O. subrotunda, and (4) adult Percina maculata (host for O. subrotunda glochidia). In acute toxicity tests, TFM was not toxic to glochidia and adult mussels at exposure concentrations that exceed typical treatment rates. Although significant dose–response relationships were observed in hosts and juveniles, survival was ≥95% (Percina maculata), ≥93% (O. olivaria), and ≥74% (O. subrotunda) at typical treatment rates. However, the steep slope of these dose–response relationships indicates that an approximately 20% difference in the treatment level can result in nearly an order of magnitude difference in survival. Collectively, these data indicate that routine sea lamprey control operations are unlikely to acutely affect these species or their host. However, given that many mussel species are long-lived (30–100 years), the risks posed by lampricide treatments in the Great Lakes would be further informed by research on the potential long-term effects of lampricides on imperiled species. Environ Toxicol Chem 2024;00:1–8. Published 2024. This article is a U.S. Government work and is in the public domain in the USA.
Biological soil crusts (biocrusts) can thrive under environmental conditions that are stressful for vascular plants such as high temperatures and/or extremely low moisture availability. In these settings, and in the absence of disturbance, cover of biocrusts commonly exceeds cover of vascular plants. Arid landscapes are also typically slow to recover from disturbance and prone to altered vegetation and invasion by exotic species. In the sagebrush ecosystems, cover of annual, exotic, invasive grasses are lower where cover of biocrusts and vascular plants are greater, suggesting that biocrusts play a role in helping arid sites avoid conversion to dominance by invasive grasses. The conceptual framework for assessing ecological resistance and resilience (R&R) is used across the region to estimate the risk of invasion by annual grasses and the likelihood of recovery of native plants following disturbance. However, this framework does not currently account for biocrusts. We used data collected by the Bureau of Land Management Assessment, Inventory, and Monitoring program to relate biocrusts, specifically the presence of lichens and mosses, to the R&R framework. Lichens frequently occur on warm, dry sites, classified as lower R&R. Mosses frequently occur on sites classified as moderate or moderately low R&R. Without management practices that favor biocrusts in low-moderate R&R, these areas may be more vulnerable to transitioning from being dominated by shrubs to annual grasses. Under climate change scenarios, the area occupied by lower R&R sites is likely to increase, suggesting that the role of biocrusts in maintaining site resistance to invasion may also increase.
Nutrient (nitrogen [N] and phosphorus [P] chemistry) downgradient from onsite wastewater treatment system (OWTS) was evaluated with a groundwater study in the area surrounding Elizabeth Lake, the largest of three sag lakes within the Santa Clara River watershed of Los Angeles County, California.
Elizabeth Lake is listed on the “303 (d) Impaired Waters List” for excess nutrients and is downgradient from more than 600 OWTS. The primary objective of this study was to develop a conceptual hydrogeological model to determine if discharge from OWTS is transported into shallow groundwater within the Elizabeth Lake subwatershed and contributes nutrients to Elizabeth Lake in excess of the total maximum daily load limit. An analysis of historical data and data collected for this study provided estimates of aquifer properties, such as hydraulic gradients and other parameters necessary to estimate boundary conditions. Electrical resistivity tomography (ERT) surveys were done to determine the best monitoring well locations and to estimate depth to groundwater. During 4 separate sampling events, 11 wells, 2 imported water tanks, 1 spring (sampled on March 17, 2019), and Elizabeth Lake were sampled, which occurred during February–September 2020.
ERT transects and borehole geophysical measurements indicated that there were low to high resistivity materials in the subsurface and potential perched fresh water. Most of the aquifer material was characterized as sandy silt, occasionally with mixed clays and medium gravels, and was estimated to have a hydraulic conductivity from 3.28x10−3 to 16.4 feet per day, a porosity from 0.34 to 0.42, and a hydraulic gradient from 0.01 to 0.03. Although bedrock was not obvious in ERT transects, all well depths were terminated at depths of an impassible confining layer observed to be a highly consolidated blue-gray clay. Depths to granitic bedrock, based on road outcrops and lithologic driller logs, varied throughout the study area. Depth to the bedrock was estimated to be shallow on the north side of Elizabeth Lake at approximately 30 feet below land surface (ft bls). Depth to bedrock is at 50 ft bls toward the east of the Elizabeth Lake subwatershed, which is at topographic ground surface to the north and south of the residential development. Groundwater levels ranged from approximately 0 to 12 ft bls during this study. Historical water levels ranged from 8 to 16 ft bls in the lower elevation of the study area and increased to depths of as much as 80 ft bls at higher elevations on the north and south boundaries of the Elizabeth Lake subwatershed.
Water-quality samples were analyzed for major ions, nutrients, dissolved organic carbon, stable isotopes, and age-dating tracers. A principal component analysis was completed to determine organic matter sources. The proportion of recharge from imported waters, used for domestic consumption, was calculated using stable water isotopes, deuterium (δD) and oxygen (δ18O). Recharge from imported waters accounted for approximately 15–71 percent of the total recharge to groundwater within the study area. Total nitrogen concentrations ranged from 0.17 to 30.9 milligrams per liter (mg/L) as N, and phosphorus, measured in the soluble form as orthophosphate, ranged from 0.03 to 0.35 mg/L as P. Nitrate concentrations in groundwater samples ranged from less than the detection limit (0.01 mg/L as N) to approximately 24 mg/L as N. Nitrate was not detected in 3 of the 12 sites sampled during the study (2 wells and Elizabeth Lake). Dissolved organic carbon concentrations ranged from 0.4 to 27 mg/L in groundwater and from 9.9 to 100 mg/L in Elizabeth Lake. Ammonium and orthophosphate concentrations generally were low in groundwater. However, elevated concentrations of ammonium in Elizabeth Lake were assumed to be due to avian waste products or biological nitrogen fixation. Groundwater ages were mostly modern (recharged since 1952), with a median recharge temperature of 13 degrees Celsius.
Redox conditions in groundwater indicated the likely occurrence of nitrate attenuation by denitrification downgradient from the wells to the south of Elizabeth Lake before groundwater discharges to the lake. Undetectable nitrate in Elizabeth Lake at the time of sampling was likely due to algal uptake. Most wells contained stable isotopes of nitrogen and oxygen in nitrate (δ15N-NO3 and δ18O-NO3) molecules with values consistent with denitrification. However, one monitoring well on the north of Elizabeth Lake (ELLA-8) had no evidence of denitrification, based on elevated concentrations of nitrate and a sufficient amount of dissolved oxygen such that the water was oxic and not favorable for the denitrification reaction. Consequently, this nitrate could be delivered to Elizabeth Lake through groundwater discharge if nitrate is not removed from the system by denitrifying bacteria downgradient from the well before the groundwater discharges into Elizabeth Lake. The principal component analysis demonstrated that dissolved organic matter optical properties track different sources of dissolved organic matter from decayed plants, animals, and animal-derived wastes. Two wells contained strong indicators of OWTS water presence, although geochemical evidence indicated other wells may also be affected by OWTS discharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245012","collaboration":"Water Resources Mission Area—National Water Quality ProgramDirector,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
As a globally widespread apex predator, humans have unprecedented lethal and non-lethal effects on prey populations and ecosystems. Yet compared to non-human predators, little is known about the movement ecology of human hunters, including how hunting behavior interacts with the environment.
We characterized the hunting modes, habitat selection, and harvest success of 483 rifle hunters in California using high-resolution GPS data. We used Hidden Markov Models to characterize fine-scale movement behavior, and k-means clustering to group hunters by hunting mode, on the basis of their time spent in each behavioral state. Finally, we used Resource Selection Functions to quantify patterns of habitat selection for successful and unsuccessful hunters of each hunting mode.
Hunters exhibited three distinct and successful hunting modes (“coursing”, “stalking”, and “sit-and-wait”), with coursings as the most successful strategy. Across hunting modes, there was variation in patterns of selection for roads, topography, and habitat cover, with differences in habitat use of successful and unsuccessful hunters across modes.
Our study indicates that hunters can successfully employ a diversity of harvest strategies, and that hunting success is mediated by the interacting effects of hunting mode and landscape features. Such results highlight the breadth of human hunting modes, even within a single hunting technique, and lend insight into the varied ways that humans exert predation pressure on wildlife.
Uncontrolled stormwater runoff volume is a legacy stressor on sewer-system capacity that is further compromised by the effects of aging infrastructure. Green stormwater infrastructure (GSI) has been used in a variety of designs and configurations (for example, bioretention) with the goal of increasing evapotranspiration and infiltration in the local water cycle. In practice, GSIs have variable effectiveness in reducing runoff volume.
An urban residential site near Detroit, Michigan, called RecoveryPark was monitored for 8 years before and after GSI construction to evaluate how effectively the GSI reduced volumes of stormwater flowing to Detroit’s Water Resource Recovery Facility through combined sewer systems. In addition to the GSI, the study site included an urban farm where salad crops were grown in hoop houses. The monitoring approach was to characterize the urban water cycle through high-frequency measurements of inflows and outflows. Datasets included meteorological data, soils and sediment characteristics, groundwater levels, flows within the combined sewer system, and soils and water chemistry with specific focus on the disposition of road salt.
Although land cover within the RecoveryPark sewershed was high-density residential in the 1950s, the sewershed included only one residence within the 8.74-acre sewershed during this study. Measurements of annual precipitation at the site exceeded long-term annual averages by more than 10 inches during 3 of the 8 years of study. Potential evapotranspiration was often greater than the measured precipitation that averaged 28–34 inches per year. As compared to underlying clay-rich sediments, soils data indicated relatively permeable sediments near land surface with estimated hydraulic conductivity of 0.75 inches per hour; however, these values decreased with increasing depth. Groundwater-level data revealed increases in groundwater storage as indicated by increases in seasonal groundwater levels and development of a groundwater mound adjacent to the GSI. These increases in groundwater levels were directly adjacent to swales designed to infiltrate stormwater and only became evident after installing the GSI.
Flows within the combined sewer system included rainwater, septic effluent, groundwater infiltration, leakage from water-supply lines, and release of water stored in abandoned foundations. Dry-weather flows (no rain fell within the prior 3 days) averaged 7–10 gallons per minute, which were much greater than flows estimated by septic outflow alone. A set of estimated water budgets were compiled, and results showed large discrepancies in unaccounted flows. To further examine these discrepancies, dye-tracing within the combined sewer system helped examine the sources of water by relating flow volumes to drainage area. For one of the monitoring sites within the combined sewer system along the southeast side of the study area, flows estimated by dye concentrations were more than 10 percent greater than those measured by standard methods. Through peak-flow-regression analysis, a minimum of 2.4 million gallons of water per year were infiltrated or lost to evapotranspiration because of GSI construction. After site modifications were made by excavating gravel drains to improve drainage characteristics, estimated stormwater volumes within the combined sewer system returned to near preconstruction levels. The GSI was effectively bypassed to address slow infiltration rates and standing water; the bypass all but eliminated the potential benefits of volume reduction.
Late in the project, a water-quality study was added to examine the transport of road salt and associated chloride within the GSI and the combined sewer system. Continuous specific conductance was used as a surrogate for chloride concentrations to estimate that 2,790 pounds of dissolved chloride passed through the sewershed during the winter months of late 2020 through early 2021. These data were collected after GSI modification, therefore most, if not all, of the chloride was transported directly to Detroit’s Water Resource Recovery Facility via the combined sewer system. Mixing diagrams using chloride and bromide concentrations of road salt, potable water, rainwater, groundwater, and water from the combined sewer system confirmed that water within the combined sewer system is a mix of these sources. The poor condition of the combined sewer system pipes and resulting unaccounted inflows added to the challenge of accurately monitoring and identifying sources and sinks of water within the RecoveryPark sewershed.
Our research results suggest that—along with clear and quantifiable objectives—the catchment and site conditions should be well-characterized before determining the GSI design. In addition, the work presented in this report provides implications and lessons learned for effectiveness and future studies of GSI in urban settings. These efforts can be improved through increased communication between stakeholders, use of high-quality soils in GSI that have suitable hydraulic characteristics, redundant data-collection networks for critical data streams, and focusing meteorological-data collection within the GSI to obtain relevant evapotranspiration data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245018","collaboration":"Prepared in cooperation with United States Environmental Protection Agency","usgsCitation":"Haefner, R.J., Hoard, C.J., and Shuster, W., 2024, Hydrologic study of green infrastructure in poorly drained urbanized soils at RecoveryPark, Detroit, Michigan, 2014–21: U.S. Geological Survey Scientific Investigations Report 2024–5018, 29 p., https://doi.org/10.3133/sir20245018.","productDescription":"Report: viii, 29 p.; Dataset; 2 Data Releases","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154558","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499447,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116361.htm","linkFileType":{"id":5,"text":"html"}},{"id":427762,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96GBEXW","text":"USGS data release","linkHelpText":"Select pipe-flow monitoring data from RecoveryPark in Detroit, MI (2015–2016)"},{"id":427761,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FP21N9","text":"USGS data release","linkHelpText":"Select pipe-flow monitoring data from RecoveryPark in Detroit, MI (2015–2021)"},{"id":427759,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5018/images/"},{"id":427758,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5018/sir20245018.XML"},{"id":427757,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5018/sir20245018.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5018"},{"id":427756,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5018/coverthb.jpg"},{"id":427763,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":427760,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245018/full"}],"country":"United States","state":"Michigan","city":"Detroit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.0495294507557,\n 42.37379642239543\n ],\n [\n -83.0495294507557,\n 42.36384762590089\n ],\n [\n -83.0332708011147,\n 42.36384762590089\n ],\n [\n -83.0332708011147,\n 42.37379642239543\n ],\n [\n -83.0495294507557,\n 42.37379642239543\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1992 Folwell Avenue
St. Paul, MN 55108
This report characterizes changes in peak streamflow in Missouri and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-streamflow frequency analysis in Missouri. Records of annual peak and daily streamflow at streamgages and gridded monthly climatic data (observed and modeled) were examined across four trend periods (100 years, water years 1921–2020; 75 years, 1946–2020; 50 years, 1971–2020; and 30 years, 1991–2020) for trends, change points (abrupt changes in the streamflow time series), and other statistical properties indicative of changing conditions. Peak streamflow magnitudes generally exhibit upward trends across the State for the 100-, 75-, and 50-year trend periods and only in southern Missouri for the 30-year trend period. The medians of the trend magnitudes (normalized by median peak streamflow) range from a 10-percent increase during the 30-year trend period to a 40-percent increase during the 100-year trend period. Changes in the 90-percent quantile of peak streamflow, which correspond to the 10-percent exceedance probability often used for the design of drainage structures, are not as substantial or widespread, showing consistent increases mainly in the southern part of the State in the 50- and 30-year trend periods. Streamgages with trends in peak streamflow often also have change points, or abrupt changes, in streamflow magnitude. Change points in peak streamflows generally follow that of the peak streamflow trends, with upward change points throughout most of the State at the 100- and 75-year trend periods and in southern Missouri at the 30-year trend period. Temporally, clusters upward of change points are observed in the 1970s through 1980s for the 100-, 75-, and 50-year trend periods and around 2006 and 2007 for the 50- and 30-year trend periods.
A peaks-over-threshold analysis, which evaluates changes in the frequency of peak streamflows over a certain threshold, indicates that high flows have increased in frequency at 50 to 64 percent of streamgages in the 100- and 75-year trend periods. Most streamgages in the 50- and 30-year trend periods exhibit no change. Although the frequency of high flows has increased at some streamgages and trend periods in Missouri, these increases are not as widespread as the increases in the magnitude of peak streamflow.
Upward trends in observed temperature and observed annual precipitation dominate in all trend periods, with no downward trends in precipitation and only two somewhat likely downward trends in temperature for the 100-year trend period. Increases in annual precipitation mostly are limited to southern Missouri for the 30-year trend period. The proportion of precipitation falling as snow has largely decreased in the study basins across the State, which is expected in response to increasing temperature. Upward trends in modeled annual runoff, which in this study incorporates only the effects of climatic variation, are observed in the same geographic areas where there are increases in observed annual precipitation. When peak streamflow and climatic trends are considered together, widespread upward trends in peak streamflows for the 100-, 75-, and 50-year trend periods and for the 30-year trend period mainly in southern Missouri (encompassing both trends and abrupt change) appear to be driven largely by increases in precipitation based on spatial patterns and statistical relations.
The prevalence of nonstationarity in peak streamflow in Missouri has important implications for peak-flow frequency analysis. Winter and spring precipitation and the occurrence of extreme precipitation events are expected to increase across the State. If precipitation continues to increase as expected, peak-flow frequency estimates based on older records may no longer represent the hydrologic regime of today, and methods for nonstationary peak-flow frequency analysis may be needed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064F","collaboration":"Prepared in cooperation with the Illinois Department of Transportation, Iowa Department of Transportation, Michigan Department of Transportation, Minnesota Department of Transportation, Missouri Department of Transportation, Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wisconsin Department of Transportation","usgsCitation":"Marti, M.K., and Heimann, D.C., 2024, Peak streamflow trends in Missouri and their relation to changes in climate, water years 1921–2020, chap. F of Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 50 p., https://doi.org/10.3133/sir20235064F.","productDescription":"Report: viii, 50 p.; Dataset; Data Release","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-148298","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":427713,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235064F/full"},{"id":499377,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116360.htm","linkFileType":{"id":5,"text":"html"}},{"id":427715,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R71WWZ","text":"USGS data 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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Groundwater-resource quality is assumed to be less responsive to drought compared to that of surface water due to relatively long transit times of recharge to drinking-supply wells. Here, we evidence dynamic perturbations in aquifer pressure dynamics during drought and subsequent recovery periods cause dramatic shifts in groundwater quality on a basin scale. We used a novel application of time-series clustering on annual nitrate anomalies at >450 public-supply wells (PSWs) across California's San Joaquin Valley during 2000–22 to group sub-populations of wells with similar water-quality responses to drought. Additionally, we statistically evaluated the direction and magnitude of multi-constituent water-quality changes across the San Joaquin Valley using a broader dataset of >3000 PSWs with data during two select hydrologic stress periods representing an extreme drought (2012–16) and subsequent recovery (2016–19). Results of time-series clustering and stress-period change analyses corroborate a predominant regional response to pumping stress characterized by increased concentrations of anthropogenic constituents (nitrate, total dissolved solids) and decreased concentrations of geogenic constituents (arsenic, fluoride), which largely reversed during recovery. Cluster analysis also identified a secondary, less commonly occurring group of PSWs where nitrate decreased during drought, but explanatory factor analysis was not able to discern hydrogeologic drivers for these two divergent response patterns. Long-term tracer data support the hypothesis that the predominant regional signal of nitrate increase during drought is caused by enhanced capture of modern-aged groundwater by PSWs during periods of pumping stress, which can drive rapid changes in water quality on seasonal and multiannual timescales. Pumping-induced migration of modern, oxic groundwater to depth during drought may affect geochemical conditions in deeper portions of regional aquifers controlling the mobility of geogenic contaminants over the long term.
Fluvial strata of the Upper Triassic Chinle Formation and Dockum Group, exposed across the Western Interior of North America, have long been interpreted to record a transcontinental river system that connected the ancestral Ouachita orogen of Texas and Oklahoma, USA, to the Auld Lang Syne basin of northwestern Nevada, USA, its inferred marine terminus. Fluvial strata are well-characterized by existing detrital zircon data, but the provenance of the Auld Lang Syne basin is poorly constrained. We present new detrital zircon U-Pb and Hf isotopic data that characterize the provenance of Norian siliciclastic strata that dominate the Auld Lang Syne basin. Mixture modeling of Auld Lang Syne basin data identifies the Alleghany−Ouachita−Marathon belt of eastern Laurentia as a dominant source of sediment, but the presence of Triassic detrital zircon grains in Auld Lang Syne basin strata indicates that at least one peri-Laurentian arc segment had to have also contributed sediment. A comparison of new Hf isotopic data with those characterizing various peri-Laurentian volcanic arcs demonstrates that although multiple arc segments may have simultaneously contributed zircons to the Auld Lang Syne basin, the west Pangean arc of northern Mexico stands out as a unique source of highly evolved Permian to Triassic detrital zircon grains in samples from the Auld Lang Syne basin. Altogether, our data and analyses demonstrate source-to-sink connectivity between the Late Triassic (Norian) Cordilleran margin and remnant late Paleozoic highlands of southern to eastern Laurentia, which ultimately framed a Mississippi River−scale, transcontinental watershed that traversed the topographically subdued Laurentian continental interior.
Population monitoring is an essential component of biodiversity conservation and management, but low detection probabilities for rare and/or cryptic species makes estimating abundance and occupancy challenging. Passive acoustic monitoring combined with machine learning algorithms represents a potential path forward to effectively and efficiently monitor the occurrence of rare vocalizing species across entire forest landscapes. Our objectives were to develop and implement a convolutional neural network (PNW-Cnet) to identify vocalizations of a rare and threatened forest nesting bird species – the marbled murrelet (Brachyramphus marmoratus) – in the Pacific Northwest, U.S.A., 2018–2021. We used PNW-Cnet predictions from broadscale passive acoustic monitoring data to examine spatiotemporal patterns in the distribution of murrelets. PNW-Cnet showed sufficiently high prediction accuracy (overall precision > 0.9) to enable broadscale population monitoring. Spatiotemporal analysis showed that annual peak murrelet call abundance occurs in ordinal weeks 28–32 (late July–Mid August) but this varied by study area. The greatest number of detections typically occurred in the Olympic Peninsula and Oregon Coast Range where late-successional forest dominates and nearer to ocean habitats. We demonstrate that passive acoustic monitoring can be used to understand intensity of use across broad scales for a rare and cryptic species in addition to the typical detection/non-detection data that are often collected. Passive acoustic monitoring combined with PNW-Cnet offers considerable promise for species distribution modeling and long-term population monitoring for rare species.
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These shorelines are compiled within a geographic information system and analyzed in the USGS Digital Shoreline Analysis System (version 5.1) software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling scientists and resource managers to identify areas that are historically most susceptible to erosion or accretion.
The effort in this report represents an expansion of the USGS national-scale shoreline database to include Puerto Rico and the islands of the territory, Vieques and Culebra. The USGS, in cooperation with the Coastal Research and Planning Institute of Puerto Rico (part of the Graduate School of Planning at the University of Puerto Rico, Río Piedras Campus) has derived and compiled a database of historical shoreline positions for Puerto Rico from the early 1900s through 2018, with the goal of providing beneficial insight for coastal managers and communities vulnerable to coastal change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1191","collaboration":"Prepared in cooperation with the University of Puerto Rico","usgsCitation":"Henderson, R.E., Heslin, J.L., Himmelstoss, E.A., and Barreto-Orta, M., 2024, National shoreline change—Summary statistics for vector shorelines from the early 1900s to the 2010s for Puerto Rico: U.S. Geological Survey Data Report 1191, 41 p., https://doi.org/10.3133/dr1191.","productDescription":"Report: vii, 41 p.; Data Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154669","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499105,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116362.htm","linkFileType":{"id":5,"text":"html"}},{"id":427852,"rank":7,"type":{"id":18,"text":"Project 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U.S. Geological Survey
384 Woods Hole Road
Woods Hole, MA 02543
Effective habitat management for rare and endangered species requires a thorough understanding of their specific habitat requirements. Although machine learning models have been increasingly used in the analyses of habitat use by wildlife, the primary focus of these models has been on generating spatial predictions. In this study, we used machine learning models in combination with simulated management actions to guide planning and inform managers. We used data from 61 whooping cranes (Grus americana) tagged with GPS telemetry collars between 2009 and 2018 near Aransas National Wildlife Refuge in coastal Texas. We included variables based on topography, land use classification, vegetation height, plant phenology, drought, storm surge events, and both wild and prescribed fires. We then built models at multiple scales: population level, home range level, and roosting and daytime within home range level. We simulated responses to the two primary management actions used to enhance whooping crane habitat on Aransas National Wildlife Refuge: prescribed fire and removal of woody vegetation. At the population and home range scales, land use classification variables had the highest importance values, whereas the combined elevation and bathymetry layer was the most important predictor at both roosting and daytime within home range scales. Our findings revealed that the effects of fire, although generally modest, varied spatially. Areas dominated by estuarine wetlands exhibited higher predicted use within the first months after a fire, whereas those dominated by palustrine wetlands were more likely to be avoided in the immediate postfire years. Our simulation of vegetation removal identified the areas on Aransas National Wildlife Refuge where whooping cranes were predicted to benefit the most if vegetation were removed. These techniques can be used by other researchers wanting to examine and predict the effects of potential management actions on target species habitat.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4820","usgsCitation":"Lehnen, S.E., Sesnie, S., Butler, M.J., Pearse, A.T., and Metzger, K.L., 2024, Management implications of habitat selection by whooping cranes (Grus americana) on the Texas coast: Ecosphere, v. 15, no. 4, e4820, 19 p., https://doi.org/10.1002/ecs2.4820.","productDescription":"e4820, 19 p.","ipdsId":"IP-154780","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":439863,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4820","text":"Publisher Index Page"},{"id":427816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.40722242363222,\n 28.90776261197753\n ],\n [\n -97.13839987001899,\n 29.222267406234465\n ],\n [\n -98.73867639073208,\n 28.864354149984962\n ],\n [\n -98.56563608351834,\n 27.049501678715416\n ],\n [\n -97.20145548476677,\n 26.91559995478883\n ],\n [\n -95.40722242363222,\n 28.90776261197753\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-04-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Lehnen, Sarah E.","contributorId":145588,"corporation":false,"usgs":false,"family":"Lehnen","given":"Sarah","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":898862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sesnie, Steven E.","contributorId":315379,"corporation":false,"usgs":false,"family":"Sesnie","given":"Steven E.","affiliations":[{"id":68297,"text":"U.S. Fish and Wildlife Service, Division of Biological Sciences, Albuquerque, NM 87102, USA","active":true,"usgs":false}],"preferred":false,"id":898863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Butler, Matthew J.","contributorId":296149,"corporation":false,"usgs":false,"family":"Butler","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":898864,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":898865,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Metzger, Kristine L.","contributorId":147144,"corporation":false,"usgs":false,"family":"Metzger","given":"Kristine","email":"","middleInitial":"L.","affiliations":[{"id":16794,"text":"USFWS, Div of Biol Serv, Albuquerque, NM","active":true,"usgs":false}],"preferred":false,"id":898866,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70254757,"text":"70254757 - 2024 - Estimating age and growth of Largemouth Bass in southwestern reservoirs using otoliths and scales","interactions":[],"lastModifiedDate":"2024-06-07T15:14:24.832721","indexId":"70254757","displayToPublicDate":"2024-04-11T10:07:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating age and growth of Largemouth Bass in southwestern reservoirs using otoliths and scales","docAbstract":"Age and growth data are frequently used to monitor and manage important North American sport fishes such as Largemouth Bass Micropterus salmoides. Continental and regional growth standards have been developed for this species to assess fish growth over time and across space. However, Largemouth Bass age and growth data are infrequently collected in Arizona and the reliability of age estimates derived from typical structures (e.g., scales, otoliths) in the Southwest is uncertain. Our objectives were to 1) compare precision and bias of age estimates from scales with those from otoliths and 2) estimate Largemouth Bass growth in several southwestern warmwater reservoirs by using otoliths. We collected Largemouth Bass from three Arizona reservoirs—Alamo, Peña Blanca, and Roosevelt—by boat electrofishing in spring 2021. We removed scales and sagittal otoliths from fish and then prepared and independently aged them three times. We compared differences in precision and bias between scales and otoliths using reader agreement percentages, confidence ratings, average coefficients of variation, and age-bias plots. We used age estimates from Largemouth Bass otoliths to calculate mean lengths-at-age at capture and relative growth indices based on published growth standards in each reservoir. Largemouth Bass scale age estimates were less precise, overestimated ages of younger fish, and underestimated age of older fish compared with those of otoliths. Growth was lower in Peña Blanca Lake than in the other two reservoirs according to mean length-at-age estimates, and relative growth indices suggested that Largemouth Bass growth in all three reservoirs was above average at younger ages, but less so at older ages. The results from this study add to a growing body of literature supporting the use of otoliths for estimating age and growth of Largemouth Bass.
","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-23-006","usgsCitation":"Ingram, S., Grant, J., Beard, Z.S., Berg, N., Ringelman, A.M., and Bonar, S.A., 2024, Estimating age and growth of Largemouth Bass in southwestern reservoirs using otoliths and scales: Journal of Fish and Wildlife Management, v. 14, no. 2, p. 315-323, https://doi.org/10.3996/JFWM-23-006.","productDescription":"9 p.","startPage":"315","endPage":"323","ipdsId":"IP-152855","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":439874,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-006","text":"Publisher Index Page"},{"id":429650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Alamo Lake, Rena Blanca Lake, Roosevelt Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.08210462737988,\n 31.40979882180362\n ],\n [\n -111.0921670470852,\n 31.40979882180362\n ],\n [\n -111.0921670470852,\n 31.397486172336002\n ],\n [\n -111.08210462737988,\n 31.397486172336002\n ],\n [\n -111.08210462737988,\n 31.40979882180362\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.55375068272383,\n 34.281204643167825\n ],\n [\n -113.61396524331289,\n 34.281204643167825\n ],\n [\n -113.61396524331289,\n 34.22354373694439\n ],\n [\n -113.55375068272383,\n 34.22354373694439\n ],\n [\n -113.55375068272383,\n 34.281204643167825\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.2613555367119,\n 33.79316218206927\n ],\n [\n -111.2613555367119,\n 33.6099720664448\n ],\n [\n -110.9547021734547,\n 33.6099720664448\n ],\n [\n -110.9547021734547,\n 33.79316218206927\n ],\n [\n -111.2613555367119,\n 33.79316218206927\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Ingram, Steven J.","contributorId":288205,"corporation":false,"usgs":false,"family":"Ingram","given":"Steven J.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":902425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Joshua D.","contributorId":288304,"corporation":false,"usgs":false,"family":"Grant","given":"Joshua D.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":902426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beard, Zachary S.","contributorId":198840,"corporation":false,"usgs":false,"family":"Beard","given":"Zachary","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":902427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berg, Nathan","contributorId":337443,"corporation":false,"usgs":false,"family":"Berg","given":"Nathan","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":902428,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ringelman, Anna M.","contributorId":337445,"corporation":false,"usgs":false,"family":"Ringelman","given":"Anna","email":"","middleInitial":"M.","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":902429,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902430,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254439,"text":"70254439 - 2024 - Empirical ground-motion basin response in the California Great Valley, Reno, Nevada, and Portland, Oregon","interactions":[],"lastModifiedDate":"2024-05-24T11:55:30.252514","indexId":"70254439","displayToPublicDate":"2024-04-11T06:53:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Empirical ground-motion basin response in the California Great Valley, Reno, Nevada, and Portland, Oregon","docAbstract":"Volcanic islands are often subject to flank instability, resulting from a combination of magmatic intrusions along rift zones and gravitational spreading causing extensional faulting at the surface. Here, we study the Koaʻe fault system (KFS), located south of the summit caldera of Kīlauea volcano in Hawaiʻi, one of the most active volcanoes on Earth, prone to active faulting, episodic dike intrusions, and flank instability. Two rift zones and the KFS are major structures controlling volcanic flank instability and magma propagation. Although several magmatic intrusions occurred over the KFS, the link between these faults, two nearby rift zones and the flank instability, is still poorly studied. To better characterize the KFS and its structural linkage with the surrounding fault and rift zones, we performed a detailed structural analysis of the extensional fault system, coupled with a helicopter photogrammetric survey, covering part of the south flank of Kīlauea. We generated a high-resolution DEM (~ 8 cm) and orthomosaic (~ 4 cm) to map the fracture field in detail. We also collected ~ 1000 ground structural measurements of extensional fractures during our three field missions (2019, 2022, and 2023). We observed many small, interconnected grabens, monoclines, rollover structures, and en-echelon fractures that were in part previously undocumented. We estimate the cumulative displacement rate across the KFS during the last 600 ~ 700 years and found a decrease toward the west of the horizontal component from 2 to 6 cm per year, consistent with GNSS data. Integrating morphology observations, fault mapping, and kinematic measurements, we propose a new kinematic model of the upper part of the Kīlauea’s south flank, suggesting a clockwise rotation and a translation of a triangular wedge. This wedge is bordered by the extensional structures (ERZ, SWRZ, and the KFS), largely influenced by gravitational spreading. These findings illustrate a structural linkage between the two rift zones and the KFS, the latter being episodically affected by dike intrusions.
The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing number of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9 and Sentinel-2, and planned future missions, like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA)’s Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from reference systems to client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16081333","usgsCitation":"Thankappan, M., Christopherson, J., Cantrell, S.J., Ryan, R., Pagnutti, M., Bright, C., Naughton, D., Ruslander, K.L., Wang, L., Hudson, D., Shaw, J., Ramaseri Chandra, S.N., and Anderson, C., 2024, Concept of a satellite cross-calibration radiometer for in-orbit calibration of commercial optical satellites: Remote Sensing, v. 16, no. 8, 1333, 20 p., https://doi.org/10.3390/rs16081333.","productDescription":"1333, 20 p.","ipdsId":"IP-161574","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":439890,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16081333","text":"Publisher Index Page"},{"id":429400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-04-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Thankappan, Medhavy","contributorId":337054,"corporation":false,"usgs":false,"family":"Thankappan","given":"Medhavy","email":"","affiliations":[{"id":80959,"text":"Geosciences Australia (GA)","active":true,"usgs":false}],"preferred":false,"id":901854,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christopherson, Jon 0000-0002-2472-0059 jonchris@usgs.gov","orcid":"https://orcid.org/0000-0002-2472-0059","contributorId":2552,"corporation":false,"usgs":true,"family":"Christopherson","given":"Jon","email":"jonchris@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":901855,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cantrell, Simon John 0000-0001-6909-1973","orcid":"https://orcid.org/0000-0001-6909-1973","contributorId":337055,"corporation":false,"usgs":true,"family":"Cantrell","given":"Simon","email":"","middleInitial":"John","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":901856,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ryan, Robert","contributorId":337056,"corporation":false,"usgs":false,"family":"Ryan","given":"Robert","affiliations":[{"id":80960,"text":"Innovative Imaging and Research Inc. (I2R)","active":true,"usgs":false}],"preferred":false,"id":901857,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pagnutti, Mary","contributorId":337057,"corporation":false,"usgs":false,"family":"Pagnutti","given":"Mary","email":"","affiliations":[{"id":80960,"text":"Innovative Imaging and Research Inc. (I2R)","active":true,"usgs":false}],"preferred":false,"id":901858,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bright, Courtney","contributorId":337058,"corporation":false,"usgs":false,"family":"Bright","given":"Courtney","email":"","affiliations":[{"id":80961,"text":"Commonwealth Scientific and Industrial Research Organisation (CSIRO)","active":true,"usgs":false}],"preferred":false,"id":901860,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Naughton, Denis","contributorId":337059,"corporation":false,"usgs":false,"family":"Naughton","given":"Denis","email":"","affiliations":[{"id":80959,"text":"Geosciences Australia (GA)","active":true,"usgs":false}],"preferred":false,"id":901861,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ruslander, Kathryn Lynn 0000-0003-3036-1731","orcid":"https://orcid.org/0000-0003-3036-1731","contributorId":337060,"corporation":false,"usgs":true,"family":"Ruslander","given":"Kathryn","email":"","middleInitial":"Lynn","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":901862,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wang, Lan-Wei","contributorId":337061,"corporation":false,"usgs":false,"family":"Wang","given":"Lan-Wei","affiliations":[{"id":80959,"text":"Geosciences Australia (GA)","active":true,"usgs":false}],"preferred":false,"id":901863,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hudson, David","contributorId":337062,"corporation":false,"usgs":false,"family":"Hudson","given":"David","affiliations":[{"id":80959,"text":"Geosciences Australia (GA)","active":true,"usgs":false}],"preferred":false,"id":901864,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shaw, Jerad 0000-0002-8319-2778 jshaw@usgs.gov","orcid":"https://orcid.org/0000-0002-8319-2778","contributorId":3564,"corporation":false,"usgs":true,"family":"Shaw","given":"Jerad","email":"jshaw@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901865,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ramaseri Chandra, Shankar N. 0000-0002-4434-4468","orcid":"https://orcid.org/0000-0002-4434-4468","contributorId":216043,"corporation":false,"usgs":true,"family":"Ramaseri Chandra","given":"Shankar","email":"","middleInitial":"N.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":901859,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":901866,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70254134,"text":"70254134 - 2024 - Deep resistivity geophysics of the San Juan–Silverton caldera complex, San Juan County, Colorado (USA)","interactions":[],"lastModifiedDate":"2024-06-03T15:06:06.93235","indexId":"70254134","displayToPublicDate":"2024-04-10T07:04:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Deep resistivity geophysics of the San Juan–Silverton caldera complex, San Juan County, Colorado (USA)","docAbstract":"Magnetotelluric (MT) and audiomagnetotelluric (AMT) data are used to better understand the subsurface geology and mineral resources in the San Juan–Silverton caldera complex located near Silverton, Colorado, western United States, as part of the extensive southern Rocky Mountains volcanic field that covers much of southwestern Colorado and northern New Mexico. Seven MT and AMT profiles of varying lengths image resistivity structure to depths of ~5 km. The AMT inversion models characterize geophysical responses of near-surface lithologies, structures, and mineralized systems and also help corroborate airborne electromagnetic data at shallow levels. The MT inversion models extend our depth of investigation from near the surface to great depths (~5 km) and help to form hypotheses about roots of the hydrothermal plumbing that fed shallower mineralized systems. Subsurface high resistivities occur beneath intermediate-composition lava flows and Proterozoic units. Subsurface moderate- to low-resistivity values may reflect hydrothermal plumbing that served as flow paths for mineralizing fluids and metallic ore formation. The model interpretations presented in this study could be utilized in remediation planning or mineral resource applications. The methods used could be applied to other watersheds with similar volcanic environments containing acid-generating historical mines or hydrothermally altered and mineralized source rocks.
Genetic variation in Arctic species is often influenced by vicariance during the Pleistocene, as ice sheets fragmented the landscape and displaced populations to low- and high-latitude refugia. The formation of secondary contact or suture zones during periods of ice sheet retraction has important consequences on genetic diversity by facilitating genetic connectivity between formerly isolated populations. Brant geese (Branta bernicla) are a maritime migratory waterfowl (Anseriformes) species that almost exclusively uses coastal habitats. Within North America, brant geese are characterized by two phenotypically distinct subspecies that utilize disjunct breeding and wintering areas in the northern Pacific and Atlantic. In the Western High Arctic of Canada, brant geese consist of individuals with an intermediate phenotype that are rarely observed nesting outside this region. We examined the genetic structure of brant geese populations from each subspecies and areas consisting of intermediate phenotypes using mitochondrial DNA (mtDNA) control region sequence data and microsatellite loci. We found a strong east–west partition in both marker types consistent with refugial populations. Within subspecies, structure was also observed at mtDNA while microsatellite data suggested the presence of only two distinct genetic clusters. The Western High Arctic (WHA) appears to be a secondary contact zone for both Atlantic and Pacific lineages as mtDNA and nuclear genotypes were assigned to both subspecies, and admixed individuals were observed in this region. The mtDNA sequence data outside WHA suggests no or very restricted intermixing between Atlantic and Pacific wintering populations which is consistent with published banding and telemetry data. Our study indicates that, although brant geese in the WHA are not a genetically distinct lineage, this region may act as a reservoir of genetic diversity and may be an area of high conservation value given the potential of low reproductive output in this species.
The frequency and extent of wildfires have increased in recent decades with immediate and cascading effects on water availability in many regions of the world. Precipitation is used as primary input to hydrologic models and is a critical driver of post-wildfire hydrologic hazards including debris flows, flash floods, water-quality effects, and reservoir sedimentation. These models are valuable tools for understanding the hydrologic response to wildfire but require accurate precipitation data at suitable spatial and temporal resolutions. Wildfires often occur in data-sparse, headwater catchments in complex terrain, and post-wildfire hydrologic effects are particularly sensitive to high-intensity, short-duration precipitation events, which are highly variable and difficult to measure or estimate. Therefore, the assessment and prediction of wildfire-induced changes to watershed hydrology, including the associated effects on ecosystems and communities, are complicated by uncertainty in precipitation data. When direct measurements of precipitation are not available, datasets of indirect measurements or estimates are often used. Choosing the most appropriate precipitation dataset can be difficult as different datasets have unique trade-offs in terms of spatial and temporal accuracy, resolution, and completeness. Here, we outline the challenges and opportunities associated with different precipitation datasets as they apply to post-wildfire hydrologic models and modeling objectives. We highlight the need for expanded precipitation gage deployment in wildfire-prone areas and discuss potential opportunities for future research and the integration of precipitation data from disparate sources into a common hydrologic modeling framework.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1728","usgsCitation":"Partridge, T.F., Johnson, Z., Sleeter, R., Qi, S.L., Walvoord, M.A., Murphy, S.F., Peterman-Phipps, C.L., and Ebel, B., 2024, Opportunities and challenges for precipitation forcing data in post-wildfire hydrologic modeling applications: WIREs Water, v. 11, no. 5, e1728, 27 p., https://doi.org/10.1002/wat2.1728.","productDescription":"e1728, 27 p.","ipdsId":"IP-155206","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":427613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439910,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1728","text":"Publisher Index Page"}],"volume":"11","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Partridge, Trevor Fuess 0000-0003-1589-4783","orcid":"https://orcid.org/0000-0003-1589-4783","contributorId":302668,"corporation":false,"usgs":true,"family":"Partridge","given":"Trevor","email":"","middleInitial":"Fuess","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":898395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sleeter, Rachel 0000-0003-3477-0436 rsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-0436","contributorId":666,"corporation":false,"usgs":true,"family":"Sleeter","given":"Rachel","email":"rsleeter@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":898396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":898399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peterman-Phipps, Cara L. 0000-0003-1822-2552","orcid":"https://orcid.org/0000-0003-1822-2552","contributorId":259166,"corporation":false,"usgs":true,"family":"Peterman-Phipps","given":"Cara","email":"","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":898400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898401,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}